Relevant bibliographies by topics / Multi-parametric brain imaging data

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  2. Dissertations / Theses
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Academic literature on the topic 'Multi-parametric brain imaging data'

Author: Grafiati
Published: 20 February 2023

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Journal articles on the topic "Multi-parametric brain imaging data"

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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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Dissertations / Theses on the topic "Multi-parametric brain imaging data"

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Wang, Dingqian. "Quantitative analysis with machine learning models for multi-parametric brain imaging data." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/22245.

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Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping.
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Ion-Margineanu, Adrian. "Machine learning for classifying abnormal brain tissue progression based on multi-parametric Magnetic Resonance data." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1224/document.

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«Machine Learning» est un champ d'étude de l'intelligence artificielle qui se concentre sur des algorithmes capables d'adapter leur paramètres en se basant sur les données observées par l'optimisation d'une fonction objective ou d'une fonction de cout. Cette discipline a soulevé l'intérêt de la communauté de la recherche biomédicale puisqu'elle permet d'améliorer la sensibilité et la spécificité de la détection et du diagnostic de nombreuses pathologies tout en augmentant l'objectivité dans le processus de prise de décision thérapeutique. L'imagerie biomédicale est devenue indispensable en médecine, puisque plusieurs modalités comme l'imagerie par résonance magnétique (IRM), la tomodensitométrie et la tomographie par émission de positron sont de plus en plus utilisées en recherche et en clinique. L'IRM est la technique d'imagerie non-invasive de référence pour l'étude du cerveau humain puisqu'elle permet dans un temps d'acquisition raisonnable d'obtenir à la fois des cartographies structurelles et fonctionnelles avec une résolution spatiale élevée. Cependant, avec l'augmentation du volume et de la complexité des données IRM, il devient de plus en plus long et difficile pour le clinicien d'intégrer toutes les données afin de prendre des décisions précises. Le but de cette thèse est de développer des méthodes de « machine learning » automatisées pour la détection de tissu cérébral anormal, en particulier dans le cas de suivi de glioblastome multiforme (GBM) et de sclérose en plaques (SEP). Les techniques d'IRM conventionnelles (IRMc) actuelles sont très utiles pour détecter les principales caractéristiques des tumeurs cérébrales et les lésions de SEP, telles que leur localisation et leur taille, mais ne sont pas suffisantes pour spécifier le grade ou prédire l'évolution de la maladie. Ainsi, les techniques d'IRM avancées, telles que l'imagerie de perfusion (PWI), de diffusion (DKI) et la spectroscopie par résonance magnétique (SRM), sont nécessaires pour apporter des informations complémentaires sur les variations du flux sanguin, de l'organisation tissulaire et du métabolisme induits par la maladie. Dans une première étude de suivi de patients GBM, seuls les paramètres d'IRM avancés ont été explorés dans un relativement petit sous-groupe de patients. Les paramètres de PWI moyens, mesurés dans les régions d'intérêts (ROI) délimités manuellement, se sont avérés être d'excellents marqueurs, puisqu'ils permettent de prédire l'évolution du GBM en moyenne un mois plus tôt que le clinicien. Dans une seconde étude, réalisée sur un échantillon plus important que la précédente, la SRM a été remplacée par l'IRMc et la quantification de la PWI et du kurtosis de diffusion (DKI) a été réalisée de manière automatique. L'extraction des paramètres d'imagerie a été effectuée sur des segmentations semi-automatiques des tumeurs, réduisant ainsi le temps nécessaire au clinicien pour la délimitation du ROI de la partie de la lésion rehaussée au produit de contraste (CE-ROI). L'application d'un algorithme modifié de «boosting» sur les paramètres extraits des ROIs a montré une grande précision pour le diagnostic du GBM. Dans une troisième, une version modifiée des cartes paramétriques de réponse (PRM) est proposée pour prendre en compte la région d'infiltration de la tumeur, réduisant toujours plus le temps nécessaire pour la délimitation de la tumeur par le clinicien, puisque toutes les images IRM sont recalées sur la première. Deux façons de générer les RPM ont été comparées, l'une basée sur l'IRMc et l'autre basée sur la PWI, ces deux paramètres étant les meilleurs pour la discrimination de l'évolution du GBM, comme le montrent les deux études précédentes. Les résultats de cette étude montrent que l'emploi de PRM basés sur l'IRMc permet d'obtenir des résultats supérieurs à ceux obtenus avec les PRM basés sur la PWI [etc…]
Machine learning is a subdiscipline in the field of artificial intelligence, which focuses on algorithms capable of adapting their parameters based on a set of observed data, by optimizing an objective or cost function. Machine learning has been the subject of large interest in the biomedical community because it can improve sensitivity and/or specificity of detection and diagnosis of any disease, while increasing the objectivity of the decision-making process. With the late increase in volume and complexity of medical data being collected, there is a clear need for applying machine learning algorithms in multi-parametric analysis for new detection and diagnostic modalities. Biomedical imaging is becoming indispensable for healthcare, as multiple modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography, and Positron Emission Tomography, are being increasingly used in both research and clinical settings. The non-invasive standard for brain imaging is MRI, as it can provide structural and functional brain maps with high resolution, all within acceptable scanning times. However, with the increase of MRI data volume and complexity, it is becoming more time consuming and difficult for clinicians to integrate all data and make accurate decisions. The aim of this thesis is to develop machine learning methods for automated preprocessing and diagnosis of abnormal brain tissues, in particular for the followup of glioblastoma multiforme (GBM) and multiple sclerosis (MS). Current conventional MRI (cMRI) techniques are very useful in detecting the main features of brain tumours and MS lesions, such as size and location, but are insufficient in specifying the grade or evolution of the disease. Therefore, the acquisition of advanced MRI, such as perfusion weighted imaging (PWI), diffusion kurtosis imaging (DKI), and magnetic resonance spectroscopic imaging (MRSI), is necessary to provide complementary information such as blood flow, tissue organisation, and metabolism, induced by pathological changes. In the GBM experiments our aim is to discriminate and predict the evolution of patients treated with standard radiochemotherapy and immunotherapy based on conventional and advanced MRI data. In the MS experiments our aim is to discriminate between healthy subjects and MS patients, as well as between different MS forms, based only on clinical and MRSI data. As a first experiment in GBM follow-up, only advanced MRI parameters were explored on a relatively small subset of patients. Average PWI parameters computed on manually delineated regions of interest (ROI) were found to be perfect biomarkers for predicting GBM evolution one month prior to the clinicians. In a second experiment in GBM follow-up of a larger subset of patients, MRSI was replaced by cMRI, while PWI and DKI parameter quantification was automated. Feature extraction was done on semi-manual tumour delineations, thereby reducing the time put by the clinician for manual delineating the contrast enhancing (CE) ROI. Learning a modified boosting algorithm on features extracted from semi-manual ROIs was shown to provide very high accuracy results for GBM diagnosis. In a third experiment in GBM follow-up of an extended subset of patients, a modified version of parametric response maps (PRM) was proposed to take into account the most likely infiltration area of the tumour, reducing even further the time a clinician would have to put for manual delineating the tumour, because all subsequent MRI scans were registered to the first one. Two types of computing PRM were compared, one based on cMRI and one based on PWI, as features extracted with these two modalities were the best in discriminating the GBM evolution, according to results from the previous two experiments. Results obtained within this last GBM analysis showed that using PRM based on cMRI is clearly superior to using PRM based on PWI [etc…]
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Martens, Corentin. "Patient-Derived Tumour Growth Modelling from Multi-Parametric Analysis of Combined Dynamic PET/MR Data." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/320127/5/contratCM.pdf.

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Gliomas are the most common primary brain tumours and are associated with poor prognosis. Among them, diffuse gliomas – which include their most aggressive form glioblastoma (GBM) – are known to be highly infiltrative. The diagnosis and follow-up of gliomas rely on positron emission tomography (PET) and magnetic resonance imaging (MRI). However, these imaging techniques do not currently allow to assess the whole extent of such infiltrative tumours nor to anticipate their preferred invasion patterns, leading to sub-optimal treatment planning. Mathematical tumour growth modelling has been proposed to address this problem. Reaction-diffusion tumour growth models, which are probably the most commonly used for diffuse gliomas growth modelling, propose to capture the proliferation and migration of glioma cells by means of a partial differential equation. Although the potential of such models has been shown in many works for patient follow-up and therapy planning, only few limited clinical applications have seemed to emerge from these works. This thesis aims at revisiting reaction-diffusion tumour growth models using state-of-the-art medical imaging and data processing technologies, with the objective of integrating multi-parametric PET/MRI data to further personalise the model. Brain tissue segmentation on MR images is first addressed with the aim of defining a patient-specific domain to solve the model. A previously proposed method to derive a tumour cell diffusion tensor from the water diffusion tensor assessed by diffusion-tensor imaging (DTI) is then implemented to guide the anisotropic migration of tumour cells along white matter tracts. The use of dynamic [S-methyl-11C]methionine ([11C]MET) PET is also investigated to derive patient-specific proliferation potential maps for the model. These investigations lead to the development of a microscopic compartmental model for amino acid PET tracer transport in gliomas. Based on the compartmental model results, a novel methodology is proposed to extract parametric maps from dynamic [11C]MET PET data using principal component analysis (PCA). The problem of estimating the initial conditions of the model from MR images is then addressed by means of a translational MRI/histology study in a case of non-operated GBM. Numerical solving strategies based on the widely used finite difference and finite element methods are finally implemented and compared. All these developments are embedded within a common framework allowing to study glioma growth in silico and providing a solid basis for further research in this field. However, commonly accepted hypothesis relating the outlines of abnormalities visible on MRI to tumour cell density iso-contours have been invalidated by the translational study carried out, leaving opened the questions of the initialisation and the validation of the model. Furthermore, the analysis of the temporal evolution of real multi-treated glioma patients demonstrates the limitations of the formulated model. These latter statements highlight current obstacles to the clinical application of reaction-diffusion tumour growth models and pave the way to further improvements.
Les gliomes sont les tumeurs cérébrales primitives les plus communes et sont associés à un mauvais pronostic. Parmi ces derniers, les gliomes diffus – qui incluent la forme la plus agressive, le glioblastome (GBM) – sont connus pour être hautement infiltrants. Le diagnostic et le suivi des gliomes s'appuient sur la tomographie par émission de positons (TEP) ainsi que l'imagerie par résonance magnétique (IRM). Cependant, ces techniques d'imagerie ne permettent actuellement pas d'évaluer l'étendue totale de tumeurs aussi infiltrantes ni d'anticiper leurs schémas d'invasion préférentiels, conduisant à une planification sous-optimale du traitement. La modélisation mathématique de la croissance tumorale a été proposée pour répondre à ce problème. Les modèles de croissance tumorale de type réaction-diffusion, qui sont probablement les plus communément utilisés pour la modélisation de la croissance des gliomes diffus, proposent de capturer la prolifération et la migration des cellules tumorales au moyen d'une équation aux dérivées partielles. Bien que le potentiel de tels modèles ait été démontré dans de nombreux travaux pour le suivi des patients et la planification de thérapies, seules quelques applications cliniques restreintes semblent avoir émergé de ces derniers. Ce travail de thèse a pour but de revisiter les modèles de croissance tumorale de type réaction-diffusion en utilisant des technologies de pointe en imagerie médicale et traitement de données, avec pour objectif d'y intégrer des données TEP/IRM multi-paramétriques pour personnaliser davantage le modèle. Le problème de la segmentation des tissus cérébraux dans les images IRM est d'abord adressé, avec pour but de définir un domaine propre au patient pour la résolution du modèle. Une méthode proposée précédemment permettant de dériver un tenseur de diffusion tumoral à partir du tenseur de diffusion de l'eau évalué par imagerie DTI a ensuite été implémentée afin de guider la migration anisotrope des cellules tumorales le long des fibres de matière blanche. L'utilisation de l'imagerie TEP dynamique à la [S-méthyl-11C]méthionine ([11C]MET) est également investiguée pour la génération de cartes de potentiel prolifératif propre au patient afin de nourrir le modèle. Ces investigations ont mené au développement d'un modèle compartimental pour le transport des traceurs TEP dérivés des acides aminés dans les gliomes. Sur base des résultats du modèle compartimental, une nouvelle méthodologie est proposée utilisant l'analyse en composantes principales pour extraire des cartes paramétriques à partir de données TEP dynamiques à la [11C]MET. Le problème de l'estimation des conditions initiales du modèle à partir d'images IRM est ensuite adressé par le biais d'une étude translationelle combinant IRM et histologie menée sur un cas de GBM non-opéré. Différentes stratégies de résolution numérique basées sur les méthodes des différences et éléments finis sont finalement implémentées et comparées. Tous ces développements sont embarqués dans un framework commun permettant d'étudier in silico la croissance des gliomes et fournissant une base solide pour de futures recherches dans le domaine. Cependant, certaines hypothèses communément admises reliant les délimitations des anormalités visibles en IRM à des iso-contours de densité de cellules tumorales ont été invalidée par l'étude translationelle menée, laissant ouverte les questions de l'initialisation et de la validation du modèle. Par ailleurs, l'analyse de l'évolution temporelle de cas réels de gliomes multi-traités démontre les limitations du modèle. Ces dernières affirmations mettent en évidence les obstacles actuels à l'application clinique de tels modèles et ouvrent la voie à de nouvelles possibilités d'amélioration.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Laruelo, Fernandez Andrea. "Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30126/document.

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L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en routine clinique notamment lorsqu'il est possible d'en extraire des informations précises et fiables. Malgré les nombreuses publications sur le sujet, l'interprétation des données d'ISRM est toujours un problème difficile en raison de différents facteurs tels que le faible rapport signal sur bruit des signaux, le chevauchement des raies spectrales ou la présence de signaux de nuisance. Cette thèse aborde le problème de l'interprétation des données d'ISRM et la caractérisation de la rechute des patients souffrant de tumeurs cérébrales. Ces objectifs sont abordés à travers une approche méthodologique intégrant des connaissances a priori sur les données d'ISRM avec une régularisation spatio-spectrale. Concernant le cadre applicatif, cette thèse contribue à l'intégration de l'ISRM dans le workflow de traitement en radiothérapie dans le cadre du projet européen SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) financé par la Commission européenne (FP7-PEOPLE-ITN)
The aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)
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Winkler, Anderson M. "Widening the applicability of permutation inference." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:ce166876-0aa3-449e-8496-f28bf189960c.

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This thesis is divided into three main parts. In the first, we discuss that, although permutation tests can provide exact control of false positives under the reasonable assumption of exchangeability, there are common examples in which global exchangeability does not hold, such as in experiments with repeated measurements or tests in which subjects are related to each other. To allow permutation inference in such cases, we propose an extension of the well known concept of exchangeability blocks, allowing these to be nested in a hierarchical, multi-level definition. This definition allows permutations that retain the original joint distribution unaltered, thus preserving exchangeability. The null hypothesis is tested using only a subset of all otherwise possible permutations. We do not need to explicitly model the degree of dependence between observations; rather the use of such permutation scheme leaves any dependence intact. The strategy is compatible with heteroscedasticity and can be used with permutations, sign flippings, or both combined. In the second part, we exploit properties of test statistics to obtain accelerations irrespective of generic software or hardware improvements. We compare six different approaches using synthetic and real data, assessing the methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). In the third part, we investigate and compare the different methods for assessment of cortical volume and area from magnetic resonance images using surface-based methods. Using data from young adults born with very low birth weight and coetaneous controls, we show that instead of volume, the permutation-based non-parametric combination (NPC) of thickness and area is a more sensitive option for studying joint effects on these two quantities, giving equal weight to variation in both, and allowing a better characterisation of biological processes that can affect brain morphology.
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"Multi-view machine learning for integration of brain imaging and (epi)genomics data." Tulane University, 2021.

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"Multi-parametric MRI Study of Brain Insults (Traumatic Brain Injury and Brain Tumor) in Animal Models." Master's thesis, 2014. http://hdl.handle.net/2286/R.I.25894.

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abstract: The objective of this small animal pre-clinical research project was to study quantitatively the long-term micro- and macro- structural brain changes employing multiparametric MRI (Magnetic Resonance Imaging) techniques. Two separate projects make up the basis of this thesis. The first part focuses on obtaining prognostic information at early stages in the case of Traumatic Brain Injury (TBI) in rat animal model using imaging data acquired at 24-hours and 7-days post injury. The obtained parametric T2 and diffusion values from DTI (Diffusion Tensor Imaging) showed significant deviations in the signal intensities from the control and were potentially useful as an early indicator of the severity of post-traumatic injury damage. DTI was especially critical in distinguishing between the cytotoxic and vasogenic edema and in identification of injury regions resolving to normal control values by day-7. These results indicate the potential of quantitative MRI as a clinical marker in predicting prognosis following TBI. The second part of this thesis focuses on studying the effect of novel therapeutic strategies employing dendritic cell (DC) based vaccinations in mice glioma model. The treatment cohorts included comparing a single dose of Azacytidine drug vs. mice getting three doses of drug per week. Another cohort was used as an untreated control group. The MRI results did not show any significant changes in between the two treated cohorts with no reduction in tumor volumes compared to the control group. The future studies would be focused on issues regarding the optimal dose for the application of DC vaccine. Together, the quantitative MRI plays an important role in the prognosis and diagnosis of the above mentioned pathologies, providing essential information about the anatomical location, micro-structural tissue environment, lesion volume and treatment response.
Dissertation/Thesis
Masters Thesis Bioengineering 2014
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Book chapters on the topic "Multi-parametric brain imaging data"

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Ning, Munan, Cheng Bian, Chenglang Yuan, Kai Ma, and Yefeng Zheng. "Ensembled ResUnet for Anatomical Brain Barriers Segmentation." In Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, 27–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71827-5_3.

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Kawai, Shigeharu, Yositaka Oku, Yasumasa Okada, Fumikazu Miwakeichi, Makio Ishiguro, and Yoshiyasu Tamura. "Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain." In Computational Neuroscience, 213–25. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-88630-5_12.

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Gay, Skylar S., Cenji Yu, Dong Joo Rhee, Carlos Sjogreen, Raymond P. Mumme, Callistus M. Nguyen, Tucker J. Netherton, Carlos E. Cardenas, and Laurence E. Court. "A Bi-directional, Multi-modality Framework for Segmentation of Brain Structures." In Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, 49–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71827-5_6.

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Mardal, Kent-André, Marie E. Rognes, Travis B. Thompson, and Lars Magnus Valnes. "Concluding Remarks and Outlook." In Mathematical Modeling of the Human Brain, 109–10. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95136-8_7.

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Langhans, Marco, Tobias Fechter, Dimos Baltas, Harald Binder, and Thomas Bortfeld. "Automatic Segmentation of Brain Structures for Treatment Planning Optimization and Target Volume Definition." In Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, 40–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71827-5_5.

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Zou, Xiaoyang, and Qi Dou. "Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread." In Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, 16–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71827-5_2.

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Shusharina, Nadya, Thomas Bortfeld, Carlos Cardenas, Brian De, Kevin Diao, Soleil Hernandez, Yufei Liu, Sean Maroongroge, Jonas Söderberg, and Moaaz Soliman. "Cross-Modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization." In Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, 3–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71827-5_1.

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Yan, Zhennan, Shaoting Zhang, Xiaofeng Liu, Dimitris N. Metaxas, and Albert Montillo. "Accurate Whole-Brain Segmentation for Alzheimer’s Disease Combining an Adaptive Statistical Atlas and Multi-atlas." In Medical Computer Vision. Large Data in Medical Imaging, 65–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05530-5_7.

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Yan, Zhennan, Shaoting Zhang, Xiaofeng Liu, Dimitris N. Metaxas, and Albert Montillo. "Accurate Whole-Brain Segmentation for Alzheimer’s Disease Combining an Adaptive Statistical Atlas and Multi-atlas." In Medical Computer Vision. Large Data in Medical Imaging, 65–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14104-6_7.

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Baniasadi, Mehri, Andreas Husch, Daniele Proverbio, Isabel Fernandes Arroteia, Frank Hertel, and Jorge Gonçalves. "Initialisation of Deep Brain Stimulation Parameters with Multi-objective Optimisation Using Imaging Data." In Informatik aktuell, 297–302. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36932-3_62.

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Conference papers on the topic "Multi-parametric brain imaging data"

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Wang, Yongmei Michelle, and Chunxiao Zhou. "Integrating and classifying parametric features from fMRI data for brain function characterization." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.653646.

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Kalicka, Renata, Anna Pietrenko-Dabrowska, Rafal Nowicki, and Seweryn Lipinski. "Brain perfusion imaging with the use of parametric modelling basing on DSC-MRI data." In 2008 1st International Conference on Information Technology (IT 2008). IEEE, 2008. http://dx.doi.org/10.1109/inftech.2008.4621660.

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Khmelinskii, A., L. Mengler, P. Kitslaar, M. Staring, M. Hoehn, and B. P. F. Lelieveldt. "A visualization platform for high-throughput, follow-up, co-registered multi-contrast MRI rat brain data." In SPIE Medical Imaging, edited by John B. Weaver and Robert C. Molthen. SPIE, 2013. http://dx.doi.org/10.1117/12.2006529.

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Zeraatkar, Navid, Kesava S. Kalluri, Benjamin Auer, Neil C. Momsen, Micaehla May, R. Garrett Richards, Lars R. Furenlid, Phillip H. Kuo, and Michael A. King. "Demultiplexing of Projection Data in Adaptive Brain SPECT with Multi-Pinhole Collimation." In 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2020. http://dx.doi.org/10.1109/nss/mic42677.2020.9507924.

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Tolstokulakov, N., E. Pavlovskiy, B. Tuchinov, E. Amelina, M. Amelin, A. Letyagin, S. Golushko, and V. Groza. "Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation." In 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). IEEE, 2020. http://dx.doi.org/10.1109/isbiworkshops50223.2020.9153416.

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Yu, Jun, Zhaoming Kong, Liang Zhan, Li Shen, and Lifang He. "Tensor-based Multi-Modality Feature Selection and Regression for Alzheimer’s Disease Diagnosis." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121812.

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The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multimodality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.
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Tan, X. Gary, Maria M. D’Souza, Subhash Khushu, Raj K. Gupta, Virginia G. DeGiorgi, Ajay K. Singh, and Amit Bagchi. "Computational Modeling of Blunt Impact to Head and Correlation of Biomechanical Measures With Medical Images." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88026.

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Mild traumatic brain injury (TBI) is a very common injury to service members in recent conflicts. Computational models can offer insights in understanding the underlying mechanism of brain injury, which can aid in the development of effective personal protective equipment. This paper attempts to correlate simulation results with clinical data from advanced techniques such as magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), MR spectroscopy and susceptibility weighted imaging (SWI), to identify TBI related subtle alterations in brain morphology, function and metabolism. High-resolution image data were obtained from the MRI scan of a young adult male, from a concussive head injury caused by a road traffic accident. The falling accident of human was modeled by combing high-resolution human head model with an articulated human body model. This mixed, multi-fidelity computational modeling approach can efficiently investigate such accident-related TBI. A high-fidelity computational head model was used to accurately reproduce the complex structures of the head. For most soft materials, the hyper-viscoelastic model was used to captures the strain rate dependence and finite strain nonlinearity. Stiffer materials, such as bony structure were simulated using an elasto-plastic material model to capture the permanent deformation. We used the enhanced linear tetrahedral elements to remove the parasitic locking problem in modeling such incompressible biological tissues. The bio-fidelity of human head model was validated from human cadaver tests. The accidental fall was reconstructed using such multi-fidelity models. The localized large deformation in the head was simulated and compared with the MRI images. The shear stress and shear strain were used to correlate with the post-accident medical images with respect to the injury location and severity in the brain. The correspondence between model results and MRI findings further validates the human head models and enhances our understanding of the mechanism, extent and impact of TBI.
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Jazzar, Nesrine, and Ali Douik. "A New Deep-Net Architecture for Ischemic Stroke Lesion Segmentation." In 4th International Conference on Machine Learning & Applications (CMLA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121108.

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Ischemic stroke, brain cells death due to a lack of oxygen, is a leading cause of long-term disability and death. Accurate diagnosis and timely intervention can effectively improve the blood supply of the ischemic stroke area and minimize brain damage. Recent studies have shown the potential to use magnetic resonance imaging (MRI) to provide contrast imaging to visualize and detect lesions. However, manual segmentation of the stroke lesion produced by MRI is a tedious and time-consuming task. Therefore, the automatic ischemic stroke lesion segmentation method may show excellent advantages. In this paper, we propose a novel deep learning method used to detect and localize brain ischemic stroke, a generalization encoderdecoder by modifying U-Net architecture. We integrate multi-path architecture into both encoder and decoder blocks to captures different levels of the encoded state, which helps in more robust decision-making for stroke lesion segmentation. In bottleneck of the architecture, we applied dilated blocks to improve the underlying predictive capabilities. The proposed method has been tested on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The results demonstrate that the proposed method achieves a mean dice coefficient 0.91 of with the training and 0.84 with the testing data respectively.
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Reddy, Kishore K., Berkan Solmaz, Pingkun Yan, Nicholas G. Avgeropoulos, David J. Rippe, and Mubarak Shah. "Confidence guided enhancing brain tumor segmentation in multi-parametric MRI." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235560.

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Tiwari, Pallavi, Prateek Prasanna, Lisa Rogers, Leo Wolansky, Chaitra Badve, Andrew Sloan, Mark Cohen, and Anant Madabhushi. "Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI." In SPIE Medical Imaging, edited by Stephen Aylward and Lubomir M. Hadjiiski. SPIE, 2014. http://dx.doi.org/10.1117/12.2043969.

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