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Chiu, Fang-Ying, and Yun Yen. "Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach." Cancers 14, no. 6 (March 14, 2022): 1475. http://dx.doi.org/10.3390/cancers14061475.
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Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system. It encroaches on brain tissue with heterogeneous regions of a necrotic core, solid part, peritumoral tissue, and edema. This study provided qualitative image interpretation in GBM subregions and radiomics features in quantitative usage of image analysis, as well as ratios of these tumor components. The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients with cerebral gliomas. Based on efficiently classified and retrieved cerebral multi-parametric MRI, all data were analyzed to derive volume-based data of the entire tumor from local cohorts and The Cancer Imaging Archive (TCIA) cohorts with GBM. Edema was mainly enriched for homeostasis whereas necrosis was associated with texture features. The proportional volume size of the edema was about 1.5 times larger than the size of the solid part tumor. The volume size of the solid part was approximately 0.7 times in the necrosis area. Therefore, the multi-parametric MRI-based radiomics model reveals efficiently classified tumor subregions of GBM and suggests that prognostic radiomic features from routine MRI examination may also be significantly associated with key biological processes as a practical imaging biomarker.
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Hayasaka, Satoru, An-Tao Du, Audrey Duarte, John Kornak, Geon-Ho Jahng, Michael W. Weiner, and Norbert Schuff. "A non-parametric approach for co-analysis of multi-modal brain imaging data: Application to Alzheimer's disease." NeuroImage 30, no. 3 (April 2006): 768–79. http://dx.doi.org/10.1016/j.neuroimage.2005.10.052.
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Mutihac, Radu. "Thresholding Wavelet-Based Statistical Parametric Maps of Functional Brain Imaging Data." NeuroImage 47 (July 2009): S124. http://dx.doi.org/10.1016/s1053-8119(09)71189-9.
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Autry, Adam, Sana Vaziri, Marisa LaFontaine, Jeremy Gordon, Hsin-Yu Chen, Javier Villanueva-Meyer, Susan Chang, et al. "NIMG-43. ADVANCED MULTI-PARAMETRIC HYPERPOLARIZED 13C/1H IMAGING OF GBM." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi138. http://dx.doi.org/10.1093/neuonc/noab196.542.
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Abstract INTRODUCTION The goal of this study was to characterize progressive and pseudoprogressive GBM using multi-parametric hyperpolarized (HP)-13C / 1H MRI. METHODS Dynamic HP-13C MRI was acquired from 13 patients with progressive GBM [patients (scans): 2(3) IDH-mutant; 11(13) IDH-wildtype] and 2 IDH-wildtype patients (3 scans) demonstrating pseudo-progression following intravenous injection of HP [1-13C]pyruvate. Frequency-selective echo-planar imaging (3s temporal resolution, 3.38 cm3 spatial resolution) captured [1-13C]pyruvate metabolism to [1-13C]lactate and 13C-bicarbonate in the brain. Dynamic 13C data were kinetically modeled to obtain the pyruvate-to-lactate conversion rate constant k PL and temporally summed to calculate 13C-metabolite percentiles and ratios (linearly interpolated 2x in-plane). 1H imaging included T2, post-Gd T1, perfusion (nCBV, %recovery), diffusion (ADC), and lactate-edited spectroscopy (CNI, choline-to-NAA index; 1H-lactate). The normal-appearing white matter (NAWM), non-enhancing lesion (NEL), and contrast-enhancing lesion (CEL) were segmented from 1H images. 13C-resolution masks were iteratively applied on a voxel-wise basis to evaluate 1H imaging parameters within each ROI and multi-parametric data were collectively evaluated using a mixed effects model in R. RESULTS Progressive IDH-mutant GBM compared to wildtype counterparts displayed increased perfusion %recovery (p < 0.001) and k PL (p < 0.01), together with reduced 1H-lactate (p < 0.001) and pyruvate percentile (p < 0.01), in the T2 lesion. Among IDH-wildtype progressive GBM, the CEL was distinguished from NEL/NAWM by increased nCBV (p < 0.05/0.001), 1H-lactate (p < 0.05/0.001); and decreased bicarbonate / lactate (p < 0.05/0.001). The CEL and NEL were collectively distinguished from NAWM by elevated CNI (p < 0.001/0.001), ADC (p < 0.05/0.001), pyruvate percentile (p < 0.001/0.001), lactate percentile (p < 0.001/0.001), and relative lactate / pyruvate (p < 0.001/0.05). Psuedo-progressive IDH-wildtype GBM displayed lower k PL (T2 Lesion; p < 0.01) and nCBV (CEL; p < 0.01) compared to progressive GBM. CONCLUSION HP-13C parameters can potentially augment proton imaging and demonstrated Warburg-associated metabolic alterations.
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Wang, Meiling, Wei Shao, Xiaoke Hao, Shuo Huang, and Daoqiang Zhang. "Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis." Bioinformatics 38, no. 8 (February 10, 2022): 2323–32. http://dx.doi.org/10.1093/bioinformatics/btac074.
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Abstract Motivation As a rising research topic, brain imaging genetics aims to investigate the potential genetic architecture of both brain structure and function. It should be noted that in the brain, not all variations are deservedly caused by genetic effect, and it is generally unknown which imaging phenotypes are promising for genetic analysis. Results In this work, genetic variants (i.e. the single nucleotide polymorphism, SNP) can be correlated with brain networks (i.e. quantitative trait, QT), so that the connectome (including the brain regions and connectivity features) of functional brain networks from the functional magnetic resonance imaging data is identified. Specifically, a connection matrix is firstly constructed, whose upper triangle elements are selected to be connectivity features. Then, the PageRank algorithm is exploited for estimating the importance of different brain regions as the brain region features. Finally, a deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) method is developed for the identification of genetic associations with functional connectivity phenotypic markers. This approach is a regularized, deep extension, scalable multi-SNP-multi-QT method, which is well-suited for applying imaging genetic association analysis to the Alzheimer’s Disease Neuroimaging Initiative datasets. It is further optimized by adopting a parametric approach, augmented Lagrange and stochastic gradient descent. Extensive experiments are provided to validate that the DS-SCCA approach realizes strong associations and discovers functional connectivity and brain region phenotypic biomarkers to guide disease interpretation. Availability and implementation The Matlab code is available at https://github.com/meimeiling/DS-SCCA/tree/main. Supplementary information Supplementary data are available at Bioinformatics online.
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Mohammed Danfulani and Shamsuddeen Ahmad Aliyu. "The role of multi-parametric magnetic resonance imaging (MRI) in early prediction of malignant transformation of low-grade Gliomas (A Systematic review)." GSC Advanced Research and Reviews 5, no. 3 (December 30, 2020): 014–29. http://dx.doi.org/10.30574/gscarr.2020.5.3.0122.
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Introduction: Low-grade gliomas is the most common primary brain tumour, although the presentation may take up to two decades, there is high tendency of early malignant transformation which raise a growing concern. Multi-parametric MRI studies have the potential for predicting the early malignant transformation. Methods: A comprehensive electronic search of various databases was conducted together with forward tracking of the reference list to retrieve relevant qualitative primary studies. Moreover, hand search for journal that was not available electronically was also conducted. Through assessment of the relevant studies was ensured and the included studies were carefully selected. The relevant data was extracted by data extraction form recommended by Cochrane collaborations. Results: The search yielded 1158 which was narrowed down to eight (8) studies that satisfied the inclusion criteria. These studies are assessing the role of different MRI parameters in predicting the early malignant transformation of Low-grade gliomas. The risk of bias and the applicability concern of the included studies are low. Conclusion: Based on the findings of this review; Multi-parametric MRI studies have the potential of predicting the early malignant transformation of low-grade gliomas. There is need for high quality large scale, prospective studies on the role of multi-parametric MRI studies in early prediction of malignant transformation of LGGs and meta-analysis of these studies is highly recommended.
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Tsolaki, Evangelia, Patricia Svolos, Evanthia Kousi, Eftychia Kapsalaki, Ioannis Fezoulidis, Konstantinos Fountas, Kyriaki Theodorou, Constantine Kappas, and Ioannis Tsougos. "Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data." International Journal of Computer Assisted Radiology and Surgery 10, no. 7 (July 15, 2014): 1149–66. http://dx.doi.org/10.1007/s11548-014-1088-7.
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Shi, Min, Dong Dong Yang, Yuan Zhou, Huan Zhao, and Yu Fang. "Analysis of Migraine Induced Monitoring Imaging Data by Multilayer Mixed Cluster Detection." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1278–83. http://dx.doi.org/10.1166/jmihi.2019.2729.
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The complex network of resting brain function was constructed by graph theory to study the difference of network topology between migraine patients and normal people. The complex network of brain function of the two groups was constructed respectively, and the average clustering coefficient, characteristic path length, small cosmopolitan, homology, median centrality and other measurement parameters of the two groups of complex networks were calculated and compared. The multi-layer hybrid ensemble clustering detection is introduced for data analysis, and the edge connectivity of consensus is optimized by modular analysis combined with hill climbing algorithm to improve the performance of the multi-layer hybrid ensemble clustering detection process driven by modularity. Conclusion: The abnormal areas of resting brain function network in migraine patients are related to pain management, visual processing and sensory relay, the findings of this study are helpful to better explain the clinical symptoms of migraine.
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Chen, Ping-Feng, R. Grant Steen, Anthony Yezzi, and Hamid Krim. "Joint Brain Parametric -Map Segmentation and RF Inhomogeneity Calibration." International Journal of Biomedical Imaging 2009 (2009): 1–14. http://dx.doi.org/10.1155/2009/269525.
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We propose a constrained version of Mumford and Shah's (1989) segmentation model with an information-theoretic point of view in order to devise a systematic procedure to segment brain magnetic resonance imaging (MRI) data for parametric -Map and -weighted images, in both 2-D and 3D settings. Incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the 3-tissue segmentation possible. Moreover, we proposed a novel method to jointly segment the -Map and calibrate RF Inhomogeneity (JSRIC). This method assumes theaveragevalue of white matter is the same across transverse slices in the central brain region, and JSRIC is able to rectify the flip angles to generate calibrated -Maps. In order to generate an accurate -Map, the determination of optimal flip-angles and the registration of flip-angle images are examined. Our JSRIC method is validated on two human subjects in the 2D -Map modality and our segmentation method is validated by two public databases, BrainWeb and IBSR, of -weighted modality in the 3D setting.
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Zhang, Jiang, Huafu Chen, Fang Fang, Hualin Liu, and Wei Liao. "A FREQUENCY SIGNAL METHOD FOR fMRI DATA ANALYSIS." Biomedical Engineering: Applications, Basis and Communications 22, no. 05 (October 2010): 377–83. http://dx.doi.org/10.4015/s1016237210002134.
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Currently, all the data processing strategies for functional magnetic resonance imaging (fMRI) utilize temporal informationpaying little attention to or totally ignoring frequency information. In this paper, a new method is proposed to detect the functional activation regions in the brain by using the frequency information of fMRI time series. The main idea is that the frequency entropy information (FEI) difference of fMRI data between task and control states is specified as brain activation index. The validity of the proposed FEI approach is confirmed by analyzing the result of the simulated synthesized data. Additionally, the comparison of receiver operating characteristic (ROC) curves acquired respectively from the proposed scheme, the statistical parametric mapping (SPM), and the Support Vector Machine (SVM) methods of fMRI data analysis indicate an obvious superiority of the former. In vivo fMRI studies of subjects with event-related experiment reveal that FEI method can enable the effective detection of brain functional activation.
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Guo, Ying. "A weighted cluster kernel PCA prediction model for multi-subject brain imaging data." Statistics and Its Interface 3, no. 1 (2010): 103–11. http://dx.doi.org/10.4310/sii.2010.v3.n1.a9.
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Qiu, Yeping Lina, Amaury Sabran, Hong Zheng, and Olivier Gevaert. "QOL-55. INTEGRATED MULTI-SCALE MODEL FOR PEDIATRIC BRAIN TUMOR SURVIVAL PREDICTION." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii440—iii441. http://dx.doi.org/10.1093/neuonc/noaa222.708.
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Abstract Brain tumors are the most common solid tumors affecting children, and its prognosis has been a great challenge for physicians and researchers. With the advances in high-throughput sequencing technology and digital pathology, more quantitative data is now becoming available and more information may potentially be discovered in whole slide images (WSIs) and molecular tumor characteristics to determine survival and treatment. Imaging and genomic data, though very different in nature, both may contain different aspects of disease characteristics that are important for survival prediction. Hence our work aims to build a framework to integrate two data modules, whole-slide histopathology image data, and RNA sequencing data, for a unified model to improve pediatric brain tumor survival outcome prediction. The imaging data and genomic data are both of high dimensions and on different scales. We use two independent modules, each of which consists of a deep neural network, to extract lower dimensional features from imaging and genomic data respectively. We concatenate the extracted features and use a third neural network to train a Cox regression model using the merged feature as input. Each module is first pre-trained with TCGA adult brain tumor data, and subsequently fine-tuned with pediatric brain tumor data. The entire pipeline is tested on the holdout pediatric brain tumor dataset. Preliminary results suggest that the integrated framework achieves improved prediction performance than using each single data module alone. The concordance index (C-index) of integrated model is 0.68, compared to 0.62 with imaging data only, and 0.66 with genomic data only.
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Grosser, Malte, Susanne Gellißen, Patrick Borchert, Jan Sedlacik, Jawed Nawabi, Jens Fiehler, and Nils D. Forkert. "Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets." PLOS ONE 15, no. 11 (November 5, 2020): e0241917. http://dx.doi.org/10.1371/journal.pone.0241917.
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Background An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences. Material and methods Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics. Results Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small. Conclusion The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.
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Crosby, Kyle, Anna Lena Eberle, and Dirk Zeidler. "Multi-beam SEM Technology for High Throughput Imaging." MRS Advances 1, no. 26 (2016): 1915–20. http://dx.doi.org/10.1557/adv.2016.363.
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ABSTRACTRecent developments in a number of fields call for high-throughput, high-resolution imaging of large areas. Examples are reconstruction of macroscopic volumes of mouse brain tissue, or wafer defect inspection. To address these needs, we have developed a multi-beam, single column SEM which utilizes an array of 61 or 91 electron beams and detectors in parallel. The total possible detection speed of the multiple beam SEM is the single detection speed times the number of beams. In the same time a single beam SEM creates an image of several million pixels size, the multi-beam SEM produces between several hundred million and one billion pixels. Herein we demonstrate the capabilities of generating massive data sets using the multi-beam SEM on a variety of samples including brain tissue serial sections and semiconductor test wafers.
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Li, Qingyun, Zhibin Yu, Yubo Wang, and Haiyong Zheng. "TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation." Sensors 20, no. 15 (July 28, 2020): 4203. http://dx.doi.org/10.3390/s20154203.
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The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.
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Du, Lei, Kefei Liu, Lei Zhu, Xiaohui Yao, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, and Li Shen. "Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort." Bioinformatics 35, no. 14 (July 2019): i474—i483. http://dx.doi.org/10.1093/bioinformatics/btz320.
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Abstract Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online.
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Piersson, Albert Dayor, Buhari Ibrahim, Subapriya Suppiah, Mazlyfarina Mohamad, Hasyma Abu Hassan, Nur Farhayu Omar, Mohd Izuan Ibrahim, et al. "Multiparametric MRI for the improved diagnostic accuracy of Alzheimer’s disease and mild cognitive impairment: Research protocol of a case-control study design." PLOS ONE 16, no. 9 (September 21, 2021): e0252883. http://dx.doi.org/10.1371/journal.pone.0252883.
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Background Alzheimer’s disease (AD) is a major neurocognitive disorder identified by memory loss and a significant cognitive decline based on previous level of performance in one or more cognitive domains that interferes in the independence of everyday activities. The accuracy of imaging helps to identify the neuropathological features that differentiate AD from its common precursor, mild cognitive impairment (MCI). Identification of early signs will aid in risk stratification of disease and ensures proper management is instituted to reduce the morbidity and mortality associated with AD. Magnetic resonance imaging (MRI) using structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (1H-MRS) performed alone is inadequate. Thus, the combination of multiparametric MRI is proposed to increase the accuracy of diagnosing MCI and AD when compared to elderly healthy controls. Methods This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and 1H-MRS will be analysed using the FSL (FMRIB’s Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini–Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores. Discussion The combination of sMRI, fMRI, DTI, and MRS sequences can provide information on the anatomical and functional changes in the brain such as regional grey matter volume atrophy, impaired functional connectivity among brain regions, and decreased metabolite levels specifically at the posterior cingulate cortex/precuneus. The combination of multiparametric MRI sequences can be used to stratify the management of MCI and AD patients. Accurate imaging can decide on the frequency of follow-up at memory clinics and select classifiers for machine learning that may aid in the disease identification and prognostication. Reliable and consistent quantification, using standardised protocols, are crucial to establish an optimal diagnostic capability in the early detection of Alzheimer’s disease.
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Roux, Franck-Emmanuel, Danielle Ibarrola, Michel Tremoulet, Yves Lazorthes, Patrice Henry, Jean-Christophe Sol, and Isabelle Berry. "Methodological and Technical Issues for Integrating Functional Magnetic Resonance Imaging Data in a Neuronavigational System." Neurosurgery 49, no. 5 (November 1, 2001): 1145–57. http://dx.doi.org/10.1097/00006123-200111000-00025.
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ABSTRACT OBJECTIVE The aim of this article was to analyze the technical and methodological issues resulting from the use of functional magnetic resonance image (fMRI) data in a frameless stereotactic device for brain tumor or pain surgery (chronic motor cortex stimulation). METHODS A total of 32 candidates, 26 for brain tumor surgery and six chronic motor cortex stimulation, were studied by fMRI scanning (61 procedures) and intraoperative cortical brain mapping under general anesthesia. The fMRI data obtained were analyzed with the Statistical Parametric Mapping 99 software, with an initial analysis threshold corresponding to P < 0.001. Subsequently, the fMRI data were registered in a frameless stereotactic neuronavigational device and correlated to brain mapping. RESULTS Correspondence between fMRI-activated areas and cortical mapping in primary motor areas was good in 28 patients (87%), although fMRI-activated areas were highly dependent on the choice of paradigms and analysis thresholds. Primary sensory- and secondary motor-activated areas were not correlated to cortical brain mapping. Functional mislocalization as a result of insufficient correction of the echo-planar distortion was identified in four patients (13%). Analysis thresholds (from P < 0.0001 to P < 10−12) more restrictive than the initial threshold (P < 0.001) had to be used in 25 of the 28 patients studied, so that fMRI motor data could be matched to cortical mapping spatial data. These analysis thresholds were not predictable preoperatively. Maximal tumor resection was accomplished in all patients with brain tumors. Chronic motor cortex electrode placement was successful in each patient (significant pain relief >50% on the visual analog pain scale). CONCLUSION In brain tumor surgery, fMRI data are helpful in surgical planning and guiding intraoperative brain mapping. The registration of fMRI data in anatomic slices or in the frameless stereotactic neuronavigational device, however, remained a potential source of functional mislocalization. Electrode placement for chronic motor cortex stimulation is a good indication to use fMRI data registered in a neuronavigational system and could replace somatosensory evoked potentials in detection of the central sulcus.
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Liu, Sa, Jun Nie, Yusha Li, Tingting Yu, Dan Zhu, and Peng Fei. "Three-dimensional, isotropic imaging of mouse brain using multi-view deconvolution light sheet microscopy." Journal of Innovative Optical Health Sciences 10, no. 05 (September 2017): 1743006. http://dx.doi.org/10.1142/s1793545817430064.
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We present a three-dimensional (3D) isotropic imaging of mouse brain using light-sheet fluorescent microscopy (LSFM) in conjunction with a multi-view imaging computation. Unlike common single view LSFM is used for mouse brain imaging, the brain tissue is 3D imaged under eight views in our study, by a home-built selective plane illumination microscopy (SPIM). An output image containing complete structural information as well as significantly improved resolution ([Formula: see text]4 times) are then computed based on these eight views of data, using a bead-guided multi-view registration and deconvolution. With superior imaging quality, the astrocyte and pyramidal neurons together with their subcellular nerve fibers can be clearly visualized and segmented. With further including other computational methods, this study can be potentially scaled up to map the connectome of whole mouse brain with a simple light-sheet microscope.
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Rodenacker, Karsten, Klaus Hahn, Gerhard Winkler, and Dorothea P. Auer. "SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA." Image Analysis & Stereology 19, no. 3 (May 3, 2011): 189. http://dx.doi.org/10.5566/ias.v19.p189-194.
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Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.
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Huang, Xinrui, Yun Zhou, Shangliang Bao, and Sung-Cheng Huang. "Clustering-Based Linear Least Square Fitting Method for Generation of Parametric Images in Dynamic FDG PET Studies." International Journal of Biomedical Imaging 2007 (2007): 1–8. http://dx.doi.org/10.1155/2007/65641.
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Parametric images generated from dynamic positron emission tomography (PET) studies are useful for presenting functional/biological information in the 3-dimensional space, but usually suffer from their high sensitivity to image noise. To improve the quality of these images, we proposed in this study a modified linear least square (LLS) fitting method named cLLS that incorporates a clustering-based spatial constraint for generation of parametric images from dynamic PET data of high noise levels. In this method, the combination of K-means and hierarchical cluster analysis was used to classify dynamic PET data. Compared with conventional LLS, cLLS can achieve high statistical reliability in the generated parametric images without incurring a high computational burden. The effectiveness of the method was demonstrated both with computer simulation and with a human brain dynamic FDG PET study. The cLLS method is expected to be useful for generation of parametric images from dynamic FDG PET study.
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Grassi, Daphine Centola, Ana Luiza Zaninotto, Fabrício Stewan Feltrin, Fabíola Bezerra de Carvalho Macruz, Maria Concepción García Otaduy, Claudia da Costa Leite, Vinicius Monteiro de Paula Guirado, Wellingson Silva Paiva, and Celi Santos Andrade. "Longitudinal whole-brain analysis of multi-subject diffusion data in diffuse axonal injury." Arquivos de Neuro-Psiquiatria 80, no. 3 (March 2022): 280–88. http://dx.doi.org/10.1590/0004-282x-anp-2020-0595.
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ABSTRACT Background: Diffuse axonal injury occurs with high acceleration and deceleration forces in traumatic brain injury (TBI). This lesion leads to disarrangement of the neuronal network, which can result in some degree of deficiency. The Extended Glasgow Outcome Scale (GOS-E) is the primary outcome instrument for the evaluation of TBI victims. Diffusion tensor imaging (DTI) assesses white matter (WM) microstructure based on the displacement distribution of water molecules. Objective: To investigate WM microstructure within the first year after TBI using DTI, the patient’s clinical outcomes, and associations. Methods: We scanned 20 moderate and severe TBI victims at 2 months and 1 year after the event. Imaging processing was done with the FMRIB software library; we used the tract-based spatial statistics software yielding fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) for statistical analyses. We computed the average difference between the two measures across subjects and performed a one-sample t-test and threshold-free cluster enhancement, using a corrected p-value < 0.05. Clinical outcomes were evaluated with the GOS-E. We tested for associations between outcome measures and significant mean FA clusters. Results: Significant clusters of altered FA were identified anatomically using the JHU WM atlas. We found increasing spotted areas of FA with time in the right brain hemisphere and left cerebellum. Extensive regions of increased MD, RD, and AD were observed. Patients presented an excellent overall recovery. Conclusions: There were no associations between FA and outcome scores, but we cannot exclude the existence of a small to moderate association.
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Toro, Roberto, Rembrandt Bakker, Thierry Delzescaux, Alan Evans, and Paul Tiesinga. "FIIND: Ferret Interactive Integrated Neurodevelopment Atlas." Research Ideas and Outcomes 4 (March 30, 2018): e25312. http://dx.doi.org/10.3897/rio.4.e25312.
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The first days after birth in ferrets provide a privileged view of the development of a complex mammalian brain. Unlike mice, ferrets develop a rich pattern of deep neocortical folds and cortico- cortical connections. Unlike humans and other primates, whose brains are well differentiated and folded at birth, ferrets are born with a very immature and completely smooth neocortex: folds, neocortical regionalisation and cortico-cortical connectivity develop in ferrets during the first postnatal days. After a period of fast neocortical expansion, during which brain volume increases by up to a factor of 4 in 2 weeks, the ferret brain reaches its adult volume at about 6 weeks of age. Ferrets could thus become a major animal model to investigate the neurobiological correlates of the phenomena observed in human neuroimaging. Many of these phenomena, such as the relationship between brain folding, cortico-cortical connectivity and neocortical regionalisation cannot be investigated in mice, but could be investigated in ferrets. Our aim is to provide the research community with a detailed description of the development of a complex brain, necessary to better understand the nature of human neuroimaging data, create models of brain development, or analyse the relationship between multiple spatial scales. We have already started a project to constitute an open, collaborative atlas of ferret brain development, integrating multi-modal and multi-scale data. We have acquired data for 28 ferrets (4 animals per time point from P0 to adults), using high-resolution MRI and diffusion tensor imaging (DTI). We have developed an open-source pipeline to segment and produce – online – 3D reconstructions of brain MRI data. We propose to process the brains of 16 of our specimens (from P0 to P16) using high-throughput 3D histology, staining for cytoarchitectonic landmarks, neuronal progenitors and neurogenesis. This would allow us to relate the MRI data that we have already acquired with multi-dimensional cell-scale information. Brains will be sectioned at 25 μm, stained, scanned at 0.25 μm of resolution, and processed for real-time multi-scale visualisation. We will extend our current web-platform to integrate an interactive multi-scale visualisation of the data. Using our combined expertise in computational neuroanatomy, multi-modal neuroimaging, neuroinformatics, and the development of inter-species atlases, we propose to build an open-source web platform to allow the collaborative, online, creation of atlases of the development of the ferret brain. The web platform will allow researchers to access and visualise interactively the MRI and histology data. It will also allow researchers to create collaborative, human curated, 3D segmentations of brain structures, as well as vectorial atlases. Our work will provide a first integrated atlas of ferret brain development, and the basis for an open platform for the creation of collaborative multi-modal, multi-scale, multi-species atlases.
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Zhang, Yan, Peter J. Passmore, and Richard H. Bayford. "Visualization of multidimensional and multimodal tomographic medical imaging data, a case study." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1900 (August 13, 2009): 3121–48. http://dx.doi.org/10.1098/rsta.2009.0084.
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Multidimensional tomographic datasets contain physical properties defined over four-dimensional (e.g. spatial–temporal, spatial–spectral), five-dimensional (e.g. spatial–temporal–spectral) or even higher-dimensional domains. Multimodal tomographic datasets contain physical properties obtained with different imaging modalities. In medicine, four-dimensional data are widely used, five-dimensional data are emerging, and multimodal data are being used more often every day. Visualization is vital for medical diagnosis and surgical planning to interpret the information included in imaging data. Visualization of multidimensional and multimodal tomographic imaging data is still a challenging task. As a case study, our work focuses on the visualization of five-dimensional (spatial–temporal–spectral) brain electrical impedance tomography (EIT) data. In this paper, a task-based subset definition scheme is proposed: a task model named Cubic Task Explorer (CTE) is derived to support the visualization task exploration for medical imaging data, and a structured method for visualization system development called Task-based Multi-Dimensional Visualization (TMDV) is proposed. A prototype system named EIT5DVis is developed using the CTE model and TMDV method to visualize five-dimensional brain EIT data.
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Rakhimberdina, Zarina, Xin Liu, and Tsuyoshi Murata. "Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder." Sensors 20, no. 21 (October 22, 2020): 6001. http://dx.doi.org/10.3390/s20216001.
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With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.
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Mang, A., A. Toma, T. A. Schuetz, S. Becker, and T. M. Buzug. "A Generic Framework for Modeling Brain Deformation as a Constrained Parametric Optimization Problem to Aid Non-diffeomorphic Image Registration in Brain Tumor Imaging." Methods of Information in Medicine 51, no. 05 (2012): 429–40. http://dx.doi.org/10.3414/me11-02-0036.
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SummaryObjectives: In the present paper a novel computational framework for modeling tumor induced brain deformation as a biophysical prior for non-rigid image registration is described. More precisely, we aim at providing a generic building block for non-rigid image registration that can be used to resolve inherent irregularities in non-diffeomorphic registration problems that naturally arise in serial and cross-population brain tumor imaging studies due to the presence (or progression) of pathology.Methods: The model for the description of brain cancer dynamics on a tissue level is based on an initial boundary value problem (IBVP). The IBVP follows the accepted assumption that the progression of primary brain tumors on a tissue level is governed by proliferation and migration of cancerous cells into surrounding healthy tissue. The model of tumor induced brain deformation is phrased as a parametric, constrained optimization problem. As a basis of comparison and to demonstrate generalizability additional soft constraints (penalties) are considered. A backtracking line search is implemented in conjunction with a limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method in order to handle the numerically delicate log-barrier strategy for confining volume change.Results: Numerical experiments are performed to test the flexible control of the computed deformation patterns in terms of varying model parameters. The results are qualitatively and quantitatively related to patterns in patient individual magnetic resonance imaging data.Conclusions: Numerical experiments demonstrate the flexible control of the computed deformation patterns. This in turn strongly suggests that the model can be adapted to patient individual imaging patterns of brain tumors. Qualitative and quantitative comparison of the computed cancer profiles to patterns in medical imaging data of an exemplary patient demonstrates plausibility. The designed optimization problem is based on computational tools widely used in non-rigid image registration, which in turn makes the model generally applicable for integration into non-rigid image registration algorithms.
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Masoudi, Babak, Sabalan Daneshvar, and Seyed Naser Razavi. "Multi-modal neuroimaging feature fusion via 3D Convolutional Neural Network architecture for schizophrenia diagnosis." Intelligent Data Analysis 25, no. 3 (April 20, 2021): 527–40. http://dx.doi.org/10.3233/ida-205113.
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Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.
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Saengpetch, Piyawat, Luepol Pipanmemekaporn, and Suwatchai Kamolsantiroj. "Functional magnetic resonance imaging-based brain decoding with visual semantic model." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6682. http://dx.doi.org/10.11591/ijece.v10i6.pp6682-6690.
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The activity pattern of the brain has been activated to identify a person in mind. Using the function magnetic resonance imaging (fMRI) to decipher brain decoding is the most accepted method. However, the accuracy of fMRI-based brain decoder is still restricted due to limited training samples. The limitations of the brain decoder using fMRI are passed through the design features proposed for many label coding and model training to predict these characteristics for a particular label. Moreover, what kind of semantic features for deciphering the neurological activity patterns are unclear. In current work, a new calculation model for learning decoding labels that is consistent with fMRI activity responses. The approach demonstrates the proposed corresponding label's success in terms of accuracy, which is decoded from brain activity patterns and compared with conventional text-derived feature technique. Besides, experimental studies present a training model based on multi-tasking to reduce the problems of limited training data sets. Therefore, the multi-task learning model is more efficient than modern methods of calculation, and decoding features may be easily obtained.
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Mensch, Arthur, Julien Mairal, Bertrand Thirion, and Gaël Varoquaux. "Extracting representations of cognition across neuroimaging studies improves brain decoding." PLOS Computational Biology 17, no. 5 (May 3, 2021): e1008795. http://dx.doi.org/10.1371/journal.pcbi.1008795.
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Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.
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Eklund, Anders, Mats Andersson, and Hans Knutsson. "Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis." International Journal of Biomedical Imaging 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/627947.
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Parametric statistical methods, such asZ-,t-, andF-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.
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Jahreis, Ina, Pablo Bascuñana, Tobias L. Ross, Jens P. Bankstahl, and Marion Bankstahl. "Choice of anesthesia and data analysis method strongly increases sensitivity of 18F-FDG PET imaging during experimental epileptogenesis." PLOS ONE 16, no. 11 (November 24, 2021): e0260482. http://dx.doi.org/10.1371/journal.pone.0260482.
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Purpose Alterations in brain glucose metabolism detected by 2-deoxy-2-[18F]-fluoro-D-glucose (18F-FDG) positron emission tomography (PET) may serve as an early predictive biomarker and treatment target for epileptogenesis. Here, we aimed to investigate changes in cerebral glucose metabolism before induction of epileptogenesis, during epileptogenesis as well as during chronic epilepsy. As anesthesia is usually unavoidable for preclinical PET imaging and influences the distribution of the radiotracer, four different protocols were compared. Procedures We investigated 18F-FDG uptake phase in conscious rats followed by a static scan as well as dynamic scans under continuous isoflurane, medetomidine-midazolam-fentanyl (MMF), or propofol anesthesia. Furthermore, we applied different analysis approaches: atlas-based regional analysis, statistical parametric mapping, and kinetic analysis. Results At baseline and compared to uptake in conscious rats, isoflurane and propofol anesthesia resulted in decreased cortical 18F-FDG uptake while MMF anesthesia led to a globally decreased tracer uptake. During epileptogenesis, MMF anesthesia was clearly best distinctive for visualization of prominently increased glucometabolism in epilepsy-related brain areas. Kinetic modeling further increased sensitivity, particularly for continuous isoflurane anesthesia. During chronic epilepsy, hypometabolism affecting more or less the whole brain was detectable with all protocols. Conclusion This study reveals evaluation of anesthesia protocols for preclinical 18F-FDG PET imaging as a critical step in the study design. Together with an appropriate data analysis workflow, the chosen anesthesia protocol may uncover otherwise concealed disease-associated regional glucometabolic changes.
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Akhonda, M. A. B. S., Yuri Levin-Schwartz, Vince D. Calhoun, and Tülay Adali. "Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data." Sensors 22, no. 3 (February 5, 2022): 1224. http://dx.doi.org/10.3390/s22031224.
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It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets of data—neuroimaging and non-neuroimaging—that we can understand and explain the evolution of neural and cognitive processes, and predict outcomes for intervention and treatment. Multiple methods for the joint analysis or fusion of multiple neuroimaging datasets or modalities exist; however, methods for the joint analysis of imaging and non-imaging data are still in their infancy. Current approaches for identifying brain networks related to cognitive assessments are still largely based on simple one-to-one correlation analyses and do not use the cross information available across multiple datasets. This work proposes two approaches based on independent vector analysis (IVA) to jointly analyze the imaging datasets and behavioral variables such that multivariate relationships across imaging data and behavioral features can be identified. The simulation results show that our proposed methods provide better accuracy in identifying associations across imaging and behavioral components than current approaches. With functional magnetic resonance imaging (fMRI) task data collected from 138 healthy controls and 109 patients with schizophrenia, results reveal that the central executive network (CEN) estimated in multiple datasets shows a strong correlation with the behavioral variable that measures working memory, a result that is not identified by traditional approaches. Most of the identified fMRI maps also show significant differences in activations across healthy controls and patients potentially providing a useful signature of mental disorders.
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Ichise, Masanori, Jeih-San Liow, Jian-Qiang Lu, Akihiro Takano, Kendra Model, Hiroshi Toyama, Tetsuya Suhara, Kazutoshi Suzuki, Robert B. Innis, and Richard E. Carson. "Linearized Reference Tissue Parametric Imaging Methods: Application to [11C]DASB Positron Emission Tomography Studies of the Serotonin Transporter in Human Brain." Journal of Cerebral Blood Flow & Metabolism 23, no. 9 (September 2003): 1096–112. http://dx.doi.org/10.1097/01.wcb.0000085441.37552.ca.
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The authors developed and applied two new linearized reference tissue models for parametric images of binding potential ( BP) and relative delivery ( R1) for [11C]DASB positron emission tomography imaging of serotonin transporters in human brain. The original multilinear reference tissue model (MRTMO) was modified (MRTM) and used to estimate a clearance rate ( k′2) from the cerebellum (reference). Then, the number of parameters was reduced from three (MRTM) to two (MRTM2) by fixing k′2. The resulting BP and R1 estimates were compared with the corresponding nonlinear reference tissue models, SRTM and SRTM2, and one-tissue kinetic analysis (1TKA), for simulated and actual [11C]DASB data. MRTM gave k′2 estimates with little bias (<1%) and small variability (<6%). MRTM2 was effectively identical to SRTM2 and 1TKA, reducing BP bias markedly over MRTMO from 12–70% to 1–4% at the expense of somewhat increased variability. MRTM2 substantially reduced BP variability by a factor of two or three over MRTM or SRTM. MRTM2, SRTM2, and 1TKA had R1 bias <0.3% and variability at least a factor of two lower than MRTM or SRTM. MRTM2 allowed rapid generation of parametric images with the noise reductions consistent with the simulations. Rapid parametric imaging by MRTM2 should be a useful method for human [11C]DASB positron emission tomography studies.
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Estanyol, Francesc, Xavier Rafael, Roman Roset, Miguel Lurgi, Mariola Mier, and Magi Lluch-Ariet. "A Web-accessible distributed data warehouse for brain tumour diagnosis." Knowledge Engineering Review 26, no. 3 (July 28, 2011): 329–51. http://dx.doi.org/10.1017/s0269888911000142.
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AbstractCurrently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.
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Rasmussen, Inge-Andre, Ida Kristin Antonsen, Erik Magnus Berntsen, Jian Xu, Jim Lagopoulos, and Asta Kristine Håberg. "Brain activation measured using functional magnetic resonance imaging during the Tower of London task." Acta Neuropsychiatrica 18, no. 5 (October 2006): 216–25. http://dx.doi.org/10.1111/j.1601-5215.2006.00145.x.
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Background:Individuals with traumatic brain injury (TBI) often suffer from a number of enduring cognitive impairments such as in attention, memory, speed of processing information and dual-task performance.Objective:The aim of this study was to assess the patterns of regional brain activation in response to the Tower of London (ToL) task in a group of patients suffering from chronic TBI using functional magnetic resonance imaging (fMRI).Methods:fMRI was performed during performance of the ToL planning task in 10 patients suffering from severe TBI and in 10 age- and sex-matched controls using a 3 T magnetic resonance scanner.Results:Performance data showed no difference in response accuracy between the TBI group and the healthy control group. Statistical parametric brain maps showed that the TBI group activates larger and additional areas of the cerebral cortex than the healthy control group both for tasks and for a subtraction contrast between the tasks.Conclusions:The results of this study are interpreted as a cortical reorganization inside the executive system of vigilance and working memory in patients with TBI. Both parietal and frontal areas are recruited to compensate for damaged brain tissue.
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Ceschin, Rafael, Ashok Panigrahy, and Vanathi Gopalakrishnan. "sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping." Cancer Informatics 14s2 (January 2015): CIN.S17293. http://dx.doi.org/10.4137/cin.s17293.
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A major challenge in the diagnosis and treatment of brain tumors is tissue heterogeneity leading to mixed treatment response. Additionally, they are often difficult or at very high risk for biopsy, further hindering the clinical management process. To overcome this, novel advanced imaging methods are increasingly being adapted clinically to identify useful noninvasive biomarkers capable of disease stage characterization and treatment response prediction. One promising technique is called functional diffusion mapping (fDM), which uses diffusion-weighted imaging (DWI) to generate parametric maps between two imaging time points in order to identify significant voxel-wise changes in water diffusion within the tumor tissue. Here we introduce serial functional diffusion mapping (sfDM), an extension of existing fDM methods, to analyze the entire tumor diffusion profile along the temporal course of the disease. sfDM provides the tools necessary to analyze a tumor data set in the context of spatiotemporal parametric mapping: the image registration pipeline, biomarker extraction, and visualization tools. We present the general workflow of the pipeline, along with a typical use case for the software. sfDM is written in Python and is freely available as an open-source package under the Berkley Software Distribution (BSD) license to promote transparency and reproducibility.
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Biver, Françoise, David Wikler, Françoise Lotstra, Philippe Damhaut, Serge Goldman, and Julien Mendlewicz. "Serotonin 5-HT2 receptor imaging in major depression: focal changes in orbito-insular cortex." British Journal of Psychiatry 171, no. 5 (November 1997): 444–48. http://dx.doi.org/10.1192/bjp.171.5.444.
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BackgroundSerotonin receptors may play an important role in the pathophysiology of affective disorders. We studied type-2 serotonin (5-HT2) receptors in the brain of patients with major depression.MethodUsing positron emission tomography (PET) and the selective radioligand [18F]altanserin, we investigated 5-HT2 receptor distribution in eight drug-free unipolar depressed patients and 22 healthy subjects. Data were analysed using Statistical Parametric Mapping 95.ResultsIn depressed patients, [18F]altanserin uptake was significantly reduced in a region of the right hemisphere including the posterolateral orbitofrontal cortex and the anterior insular cortex. A trend to similar changes was found in the left hemisphere. No correlation was found between the uptake and the Hamilton rating scale score.ConclusionsPathophysiology of depression may involve changes in 5-HT2 receptor in brain regions selectively implicated in mood regulation.
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Cetin-Karayumak, Suheyla, Fan Zhang, Lauren J. O'Donnell, and Yogesh Rathi. "Harmonization of Multi-Site Diffusion Magnetic Resonance Imaging Data From the Adolescent Brain Cognitive Development Study." Biological Psychiatry 91, no. 9 (May 2022): S84. http://dx.doi.org/10.1016/j.biopsych.2022.02.227.
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Coyle, Anne, Ariana Familiar, Chao Zhao, Komal Rathi, Madison Hollawell, Namrata Choudhari, Jessica Foster, et al. "BIOM-63. IDENTIFICATION OF MIRNA IN CEREBROSPINAL FLUID AND PLASMA AS A BIOMARKER TO SUPPORT MRI EVALUATION AND MONITORING OF PEDIATRIC BRAIN TUMORS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii19. http://dx.doi.org/10.1093/neuonc/noac209.073.
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Abstract MRI is the current gold standard imaging technique for diagnostic evaluation and monitoring of pediatric CNS tumors, however MRI measures are unable to fully relate to tumor biology and molecular stratification. Circulating in blood and cerebrospinal fluid (CSF), miRNAs are an abundant and stable nucleic acid which can be utilized as a tumor biomarker. Relating miRNA biomarkers and radiological tumor measurements may provide improved diagnostic and monitoring tools for pediatric brain tumors. Using a cohort of 54 pediatric brain tumors including low grade glioma, ependymoma, germ cell tumor, medulloblastoma, atypical teratoid rhabdoid tumor and high-grade glioma we attempted to combine MRI findings and circulating miRNA data. The miRNA expression was profiled in 33 CSF and 52 plasma samples using the HTG EdgeSeq platform. Clinically acquired, multi-parametric MRI scans at time-points close in proximity to liquid biopsy collection were collected retrospectively and used to generate volumetric tumor segmentations. We identified unique miRNA targets significantly correlated with MRI features, clinical findings, and patient outcomes. In both CSF and plasma, miRNA expression was identified to correlate with diagnosis and clinical features including tumor grade and survival status (p < 0.05). In CSF, miRNA expression was correlated with MRI measurements including cystic core volume, non-enhancing tumor volume, leptomeningeal disease, tumor size and location (p < 0.05). Combination of miRNA targets and radiomic tumor measurements improved diagnostic predictions between low- and high-grade tumors. In plasma, miRNA expression was correlated with MRI measurements including cystic core volume, location, and leptomeningeal disease (p < 0.05). These results demonstrate utility of miRNAs as a pediatric brain tumor biomarker which combined with imaging features can improve minimally to non-invasive diagnostics and management of pediatric brain tumors.
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Sun, Qing, Wenliang Fan, Yuan Liu, Yan Zou, Natalie Wiseman, Zhifeng Kou, and Ping Han. "Characterization of brain microstructural abnormalities in cirrhotic patients without overt hepatic encephalopathy using diffusion kurtosis imaging." Brain Imaging and Behavior 14, no. 2 (September 11, 2019): 627–38. http://dx.doi.org/10.1007/s11682-019-00141-4.
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Abstract Cirrhosis is a major public health concern. However, little is known about the neurobiological mechanisms underlying brain microstructure alterations in cirrhotic patients. The purpose of this prospective study was to investigate brain microstructural alterations in cirrhosis with or without minimal hepatic encephalopathy (MHE) and their relationship with patients’ neurocognitive performance and disease duration using voxel-based analysis of diffusion kurtosis imaging (DKI). DKI data were acquired from 30 cirrhotic patients with MHE, 31 patients without MHE (NMHE) and 59 healthy controls. All DKI-derived parametric maps were compared across the three groups to investigate their group differences. Correlation analyses were further performed to assess relationships between altered imaging parameters and clinical data. Voxel-based analysis of DKI data results showed that MHE/NMHE patients had increased radial diffusivity, axial diffusivity (AD) and mean diffusivity in addition to decreased axial kurtosis (AK) and fractional anisotropy of kurtosis in several regions. Compared to controls, these regions were primarily the cingulum, temporal and frontal cortices. The DKI metrics (i.e., AK and AD) were correlated with clinical variables in the two patient groups. In conclusion, DKI is useful for detecting brain microstructural abnormalities in MHE and NMHE patients. Abnormal DKI parameters suggest alterations in brain microstructural complexity in cirrhotic patients, which may contribute to the neurobiological basis of neurocognitive impairment. These results may provide additional information on the pathophysiology of cirrhosis.
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V, Bhavana, and Krishnappa H. K. "Multi-modal image fusion using contourlet and wavelet transforms: a multi-resolution approach." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (November 1, 2022): 762. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp762-768.
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In recent years, vast improvement and progress has been observed in the field of medical research, especially in digital medical imaging technology. Medical image fusion has been widely used in clinical diagnosis to get valuable information from different modalities of medical images to enhance its quality by fusing images like computed tomography (CT), and magnetic resonance imaging (MRI). MRI gives clear information on delicate tissue while CT gives details about denser tissues. A multi-resolution approach is proposed in this work for fusing medical images using non-sub-sampled contourlet transform (NSCT) and discrete wavelet transform (DWT). In this approach, initially the input images are decomposed using DWT at 4 levels and NSCT at 2 levels which helps to protect the vital data from the source images. This work shows significant enhancement in pixel clarity and preserves the information at the corners and edges of the fused image without any data loss. The proposed methodology with an improved entropy and mutual information helps the doctors in better clinical diagnosis of brain diseases.
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Nandhini, I., D. Manjula, and Vijayan Sugumaran. "Multi-Class Brain Disease Classification Using Modified Pre-Trained Convolutional Neural Networks Model with Substantial Data Augmentation." Journal of Medical Imaging and Health Informatics 12, no. 2 (February 1, 2022): 168–83. http://dx.doi.org/10.1166/jmihi.2022.3936.
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The integration of various algorithms in the medical field to diagnose brain disorders is significant. Generally, Computed Tomography, Magnetic Resonance Imaging techniques have been used to diagnose brain images. Subsequently, segmentation and classification of brain disease remain an exigent task in medical image processing. This paper presents an extended model for brain image classification based on a Modified pre-trained convolutional neural network model with extensive data augmentation. The proposed system has been efficiently trained using the technique of substantial data augmentation in the pre-processing stage. In the first phase, the pre-trained models namely AlexNet, VGGNet-19, and ResNet-50 are employed to classify the brain disease. In the second phase, the idea of integrating the existing pre-trained model with a multiclass linear support vector machine is incorporated. Hence, the SoftMax layer of pre-trained models is replaced with a multi class linear support vector machine classifier is proposed. These proposed modified pre-trained model is employed to classify brain images as normal, inflammatory, degenerative, neoplastic and cerebrovascular diseases. The training loss, mean square error, and classification accuracy have been improved through the concept of Cyclic Learning rate. The appropriateness of transfer learning has been demonstrated by applying three convolutional neural network models, namely, AlexNet, VGGNet-19, and ResNet-50. It has been observed that the modified pre-trained models achieved a higher classification rate of accuracies of 93.45% when compared with a finetuned pre-trained model of 89.65%. The best classification accuracy of 92.11%, 92.83% and 93.45% has been attained in the proposed method of the modified pre-trained model. A comparison of the proposed model with other pre-trained models is also presented.
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Dowrick, T., G. Sato Dos Santos, A. Vongerichten, and D. Holder. "Parallel, multi frequency EIT measurement, suitable for recording impedance changes during epilepsy." Journal of Electrical Bioimpedance 6, no. 1 (August 8, 2019): 37–43. http://dx.doi.org/10.5617/jeb.2573.
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Abstract Electrical Impedance Tomography (EIT) has been proposed as a method for imaging and localising epileptic activity in the brain. No existing EIT system meets all of the requirements for effective imaging of epilepsy. A parallel EIT system, employing frequency division multiplexing, is described, which is optimised for measuring impedance changes during epilepsy. The system is capable of imaging short duration, spontaneous events in a saline filled tank, using as little as 1ms of recorded data. In-vivo impedance measurements recorded during epilepsy in a rat model are presented.
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Andreassen, O. "Neuroimaging findings in bipolar disorder - opportunities for network projects." European Psychiatry 26, S2 (March 2011): 2215. http://dx.doi.org/10.1016/s0924-9338(11)73918-9.
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The overall aim of the Imaging work package in ENBREC is to develop a common magnetic resonance imaging (MRI) protocol which allows pooling of data acquired from different MRI scanners, that can be used in multi-site studies of bipolar disorders.Structural MRI studies usually provide global estimates of gray or white matter volume changes, or a small number of regions of interest (ROIs). Recent advances in structural imaging now allow for a more comprehensive evaluation of brain changes by providing continuous maps of cortical thickness and surface area, subcortical volumes, and measures of white matter microstructure throughout the brain. Such high-resolution structural MRI for morphometric analyses is now being used for automatic quantification of brain structures. There are recent reports of cortical abnormalities in bipolar disorder, which will be reviewed.White matter can now be quantified using diffusion tensor imaging (DTI). This MRI variant for quantifying the integrity of white matter structure throughout the brain measures water diffusion and its directionality. The degree of anisotropy of overall motion in a voxel is expressed as an index of the directionality of diffusion (fractional anisotropy; FA). Lower FA values are thought to reflect factors such as demyelination and axonal injury, and has been used to show white matter abnormalities in bipolar disorder.A suggestion for protocol for multi-site brain imaging study in bipolar disorder will be presented.
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Meng, Xianglian, Qingpeng Wei, Li Meng, Junlong Liu, Yue Wu, and Wenjie Liu. "Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data." Genes 13, no. 5 (May 7, 2022): 837. http://dx.doi.org/10.3390/genes13050837.
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Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.
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Wu, Chong. "Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank." Genetics 215, no. 4 (June 15, 2020): 947–58. http://dx.doi.org/10.1534/genetics.120.303242.
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Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.
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Thakur, Siddhesh, Jimit Doshi, Sung Min Ha, Gaurav Shukla, Aikaterini Kotrotsou, Sanjay Talbar, Uday Kulkarni, et al. "NIMG-40. ROBUST MODALITY-AGNOSTIC SKULL-STRIPPING IN PRESENCE OF DIFFUSE GLIOMA: A MULTI-INSTITUTIONAL STUDY." Neuro-Oncology 21, Supplement_6 (November 2019): vi170. http://dx.doi.org/10.1093/neuonc/noz175.710.
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Abstract BACKGROUND Skull-stripping describes essential pre-processing in neuro-imaging, directly impacting subsequent analyses. Existing skull-stripping algorithms are typically developed and validated only on T1-weighted MRI scans without apparent gliomas, hence may fail when applied on neuro-oncology scans. Furthermore, most algorithms have large computational footprint and lack generalization to different acquisition protocols, limiting their clinical use. We sought to identify a practical, generalizable, robust, and accurate solution to address all these limitations. METHODS We identified multi-institutional retrospective cohorts, describing pre-operative multi-parametric MRI modalities (T1,T1Gd,T2,T2-FLAIR) with distinct acquisition protocols (e.g., slice thickness, magnet strength), varying pre-applied image-based defacing techniques, and corresponding manually-delineated ground-truth brain masks. We developed a 3D fully convolutional deep learning architecture (3D-ResUNet). Following modality co-registration to a common anatomical template, the 3D-ResUNet was trained on 314 subjects from the University of Pennsylvania (UPenn), and evaluated on 91, 152, 25, and 29 unseen subjects from UPenn, Thomas Jefferson University (TJU), Washington University (WashU), and MD Anderson (MDACC), respectively. To achieve robustness against scanner/resolution variability and utilize all modalities, we introduced a novel “modality-agnostic” training approach, which allows application of the trained model on any single modality, without requiring a pre-determined modality as input. We calculate the final brain mask for any test subject by applying our trained modality-agnostic 3D-ResUNet model on the modality with the highest resolution. RESULTS The average(±stdDev) dice similarity coefficients achieved for our novel modality-agnostic model were equal to 97.81%+0.8, 95.59%+2.0, 91.61%+1.9, and 96.05%+1.4 for the unseen data from UPenn, TJU, WashU, and MDACC, respectively. CONCLUSIONS Our novel modality-agnostic skull-stripping approach produces robust near-human performance, generalizes across acquisition protocols, image-based defacing techniques, without requiring pre-determined input modalities or depending on the availability of a specific modality. Such an approach can facilitate tool standardization for harmonized pre-processing of neuro-oncology scans for multi-institutional collaborations, enabling further data sharing and computational analyses.
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Sui, Jing, Tülay Adali, Godfrey Pearlson, Honghui Yang, Scott R. Sponheim, Tonya White, and Vince D. Calhoun. "A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia." NeuroImage 51, no. 1 (May 2010): 123–34. http://dx.doi.org/10.1016/j.neuroimage.2010.01.069.
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Nam, Ahhyun, Jian Ren, Brett Bouma, and Benjamin Vakoc. "Demonstration of Triband Multi-Focal Imaging with Optical Coherence Tomography." Applied Sciences 8, no. 12 (November 26, 2018): 2395. http://dx.doi.org/10.3390/app8122395.
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We demonstrate an extended depth of focus optical coherence tomography (OCT) system based on the use of chromatic aberration to create displaced focal planes in the sample. The system uses a wavelength-swept source tuning over three spectral bands and three separate interferometers, each of which interfaces to a single illumination/collection fiber. The resulting three imaged volumes are merged in post-processing to generate an image with a larger depth of focus than is obtained from each band individually. The improvements are demonstrated in structural imaging of a porous phantom and a lipid-cleared murine brain, and by angiographic imaging of human skin. By using a coaxial approach with Gaussian beams, this approach enables an extended focus with relatively simple microscope optics and data-merging algorithms.
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Bonte, Frederick J., Linda Hynan, Thomas S. Harris, and Charles L. White. "TC-99m HMPAO Brain Blood Flow Imaging in the Dementias with Histopathologic Correlation in 73 Patients." International Journal of Molecular Imaging 2011 (December 1, 2011): 1–3. http://dx.doi.org/10.1155/2011/409101.
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The purpose of this study is to determine the value of Tc-99m HMPAO SPECT in the diagnosis of the dementias. Tc-99m HMPAO was used with a 3-camera scanner to produce 5 sets of sectional images of the brain. Images were further processed using Statistical Parametric Mapping. Diagnosis was made by a physician blinded to the clinical diagnosis. Results in 73 subjects were compared with a neuropathologic study of the brain at autopsy. Data were analyzed for sensitivity, specificity, positive and negative predictive values and accuracy. These results are compared with several other studies performed with Tc-99m HMPAO SPECT with histopathologic correlation. This procedure is widely available and relatively inexpensive and may be of value in patients with dementias and problematic diagnoses. Further, a degree of differential diagnosis between Alzheimer's and Frontotemporal diseases may be effected. The study was approved by our Institutional Review Board.