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Journal articles on the topic 'PET/CT image processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Rossi, Farli, and Ashrani Aizzuddin Abd Rahni. "Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review." International Journal of Engineering & Technology 7, no. 3.32 (2018): 137. http://dx.doi.org/10.14419/ijet.v7i3.32.18414.

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Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation

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Malczewski, Krzysztof. "Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals." Algorithms 13, no. 5 (2020): 129. http://dx.doi.org/10.3390/a13050129.

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One of the most challenging aspects of medical modalities such as Computed Tomography (CT) as well hybrid techniques such as CT/PET (Computed Tomography/Positron emission tomography) and PET/MRI is finding a balance between examination time, radiation dose, and image quality. The need for a dense sampling grid is associated with two major factors: image resolution enhancement, which leads to a strengthening of human perception, and image features interpretation. All these aspects make an unsupervised image processing much easier. The presented algorithm employs super-resolution-reconstruction

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Pietrzyk, U. "Does PET/CT render software registration obsolete?" Nuklearmedizin 44, S 01 (2005): S13—S17. http://dx.doi.org/10.1055/s-0038-1625209.

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Summary:It was the success of software-based image registration that eventually led to the introduction of hardware-based concepts for image fusion, such as combined PET/CT tomographs. A prototype PET/CT was first presented in 1998, with various commercial designs to follow since 2000. PET/ CT is used primarily as a diagnostic modality in the field of extra-cerebral oncology imaging. The major advantage of combined imaging over retrospective software registration is the nearly identical position of the patient during both complementary examination, and therefore tomograms of identical parts of

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Marinelli, Martina, Vincenzo Positano, Francesco Tucci, Danilo Neglia, and Luigi Landini. "Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms." Scientific World Journal 2012 (2012): 1–12. http://dx.doi.org/10.1100/2012/567067.

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Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time.

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Liu, Jiahui, Xiangjun Zhang, Tana Bai, and Yiming Liu. "Analysis on Brain Image Characteristics of Patients with Parkinson's Disease Under Multimodal Magnetic Resonance Technology." Journal of Medical Imaging and Health Informatics 11, no. 2 (2021): 606–11. http://dx.doi.org/10.1166/jmihi.2021.3371.

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In order to improve the early diagnosis rate of Parkinson's disease (PD), reduce the complications of PD in the later stage, and make the clinical intervention to alleviate the pain of Parkinson's patients early, Magnetic resonance imaging (MRI), positron emission tomography (PET), and computerized tomography (CT) were used to evaluate the characteristics of PD. A total of 34 patients diagnosed with PD admitted to Qilu Hospital of Shandong University from January 2017 to December 2018 were included in the research. According to the severity of the disease, the patients were divided into the in

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Deleu, Anne-Leen, Machaba Junior Sathekge, Alex Maes, Bart De Spiegeleer, Mike Sathekge, and Christophe Van de Wiele. "Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review." Biomedicines 8, no. 9 (2020): 304. http://dx.doi.org/10.3390/biomedicines8090304.

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Radiomics or textural feature extraction obtained from positron emission tomography (PET) images through complex mathematical models of the spatial relationship between multiple image voxels is currently emerging as a new tool for assessing intra-tumoral heterogeneity in medical imaging. In this paper, available literature on texture analysis using FDG PET imaging in patients suffering from tumors of the gastro-intestinal tract is reviewed. While texture analysis of FDG PET images appears clinically promising, due to the lack of technical specifications, a large variability in the implemented

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Elaiyaraja, K., and M. Senthil Kumar. "Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review." International Journal of Engineering & Technology 7, no. 3.12 (2018): 175. http://dx.doi.org/10.14419/ijet.v7i3.12.15913.

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Image Processing is an art to get an enriched image or it can be used to retrieve information. This image processing methods are used in medical field also. Numerous modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) etc. are used to analyze and diagnose diseases.Pixel-level image fusion is a combination of several images collected from various inputs and gives more information than any other input messages. Pixel-level image fusion shows a vital role in medical imaging. In this paper, pixel-level image fusionsmethods are survived

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Bercier, Y., M. Schwaiger, S. I. Ziegler, and M. J. Martínez. "PET/CT BiographTM Sensation 16." Nuklearmedizin 45, no. 03 (2006): 126–33. http://dx.doi.org/10.1055/s-0038-1625926.

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SummaryAim: The new PET/CT Biograph Sensation 16 (BS16) tomographs have faster detector electronics which allow a reduced timing coincidence window and an increased lower energy threshold (from 350 to 400 keV). This paper evaluates the performance of the BS16 PET scanner before and after the Pico-3D electronics upgrade. Methods: Four NEMA NU 2–2001 protocols, (i) spatial resolution, (ii) scatter fraction, count losses and random measurement, (iii) sensitivity, and (iv) image quality, have been performed. Results: A considerable change in both PET count-rate performance and image quality is obs

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Syed Inthiyaz, Hasane Ahammad Sk, Praveen SR Konduri, et al. "A novel approach of MRI-CT Image fusion using CWT for finding Disease location." International Journal of Research in Pharmaceutical Sciences 11, no. 1 (2020): 497–506. http://dx.doi.org/10.26452/ijrps.v11i1.1850.

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Medical Image processing has tremendous applications in medical diagnosis. This broadsheet offerings the idea of a fusion of MRI(Magnetic Resource Imaging)-CT (Computed tomography) using Coverlet wavelet transform(CWT), which is used to find the disease location in an image. In the Medical field, CT provides maximum information on denser tissue with less amount of distortion and higher resolution images. Whereas, on the other hand, MRI provides information on softer tissue with much distortion. However, both are similar; the main difference lies where CT uses X-rays to produce images while MRI

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Rossi, Farli. "APPLICATION OF A SEMI-AUTOMATED TECHNIQUE IN LUNG LESION SEGMENTATION." Jurnal Teknoinfo 15, no. 1 (2021): 56. http://dx.doi.org/10.33365/jti.v15i1.945.

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Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this study, we apply a semi-automated technique that combines an active contour and low-level processing techniques in lung lesion segmentation by extracting lung lesions from thoracic Positron Emission Tomography (PET)/Computed Tomography (CT) images. The lesions were first segmented in Positron Emission Tomography (PET) images which have been converted previously to Standardised Uptake Values (SUVs). The segmented PET images then serve as an initia

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Li, Zhen Wei, Xiao Li Yang, and Wei Dong Song. "A Novel Multi-Planar Fusion System for PET/CT Images." Applied Mechanics and Materials 373-375 (August 2013): 608–12. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.608.

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Objective To propose a PET/CT multi-planar fusion system based on PACS. Methods Firstly, the system got PET and CT slices and relevant DICOM header information from PACS workstation, preprocessed them and formed CT volume and PET volume, and then displayed the axial, coronal and sagittal planes of the two volumes by MPR. Afterwards, the results of MPR were enhanced by pseudo color processing, then were fused with the MRP results of CT correspondingly. Finally, the SUVs of PET images and the CT value of CT images were calculated according to the parameters obtained from the DICOM header, and we

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Bajaj, Aaishwarya Sanjay, and Usha Chouhan. "A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 8 (2020): 937–45. http://dx.doi.org/10.2174/1573405615666190903144419.

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Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better perform

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Khan A, Mohsin, and Anuj Jain. "A Survey on Diagnosis of US Image Thyroid Nodules and Automated Classification." International Journal of Engineering & Technology 7, no. 3.12 (2018): 384. http://dx.doi.org/10.14419/ijet.v7i3.12.16112.

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Different types of human diseases are detected by medical image analysis which plays an important role. Studies that are developed for automated thyroid cancer classification is reviewed in this paper, especially to analyze the benign and malignant thyroid nodules features and comparisons. Hypothyroidism, hyperthyroidism, goitre and thyroid nodules (benign/malignant) are thyroid disorders. Ultrasound imaging, CT, MR imaging, nuclear medicine (NM) with positron emission tomography (PET), single photon emission computed tomography (SPECT) are the different medical techniques used to identify and

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Yang, Hang. "Application of nano-metric synthetic materials in medical imaging diagnosis." Materials Express 11, no. 7 (2021): 1168–76. http://dx.doi.org/10.1166/mex.2021.2020.

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This paper investigates the application of nanometer synthetic materials in medical imaging diagnosis. The main synthesis methods of nanomaterials are described, and nanomaterials are applied to X-ray CT imaging, magnetic resonance imaging, PAT imaging, fluorescence imaging, photoacoustic imaging and PET imaging. The diagnostic results can be obtained by applying spatial filtering to filter operation, medical imaging and the filtering processing of image segmentation. By extracting the medical image texture feature using gray level cooccurrence matrix, studying shallow network features and com

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Chauvie, Stephane, Federico Dalmasso, Larry Pierce, et al. "A core laboratory approach to large-scale radiomics and machine-learning prediction of DLBCL outcomes after first-line treatment using results from the phase III GOYA study." Journal of Clinical Oncology 37, no. 15_suppl (2019): e19042-e19042. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e19042.

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e19042 Background: We are using the phase 3 GOYA study to retrospectively investigate using quantitative image texture features (i.e. radiomics, evaluating tumor heterogeneity) to improve the prognostic value of baseline FDG-PET. We report on the considerations for analysis, which is ongoing, and solutions to methodological challenges. Methods: GOYA, an open-label, randomized, phase 3 study, enrolled 1418 patients from 207 centers; 1334 had a baseline FDG-PET scan with detectable lesions. Image data with annotated regions of interest (ROIs) defined by qualified physicians were transferred to t

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Lo Faso, Enrico Antonio, Orazio Gambino, and Roberto Pirrone. "Head–Neck Cancer Delineation." Applied Sciences 11, no. 6 (2021): 2721. http://dx.doi.org/10.3390/app11062721.

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Head–Neck Cancer (HNC) has a relevant impact on the oncology patient population and for this reason, the present review is dedicated to this type of neoplastic disease. In particular, a collection of methods aimed at tumor delineation is presented, because this is a fundamental task to perform efficient radiotherapy. Such a segmentation task is often performed on uni-modal data (usually Positron Emission Tomography (PET)) even though multi-modal images are preferred (PET-Computerized Tomography (CT)/PET-Magnetic Resonance (MR)). Datasets can be private or freely provided by online repositories

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Ignatova, Mariya, Marina Tlostanova, and Andrey Stanzhevskiy. "THE FIRST EXPERIENCE OF PERFORMING COMBINED POSITRONEMISSION WITH COMPUTED TOMOGRAPHY WITH PROSTATE-SPECIFIC MEMBRANE ANTIGEN LABELED WITH GALLIUM-68 IN PATIENTS WITH MINIMAL LEVEL OF PROSTATE-SPECIFIC ANTIGEN AFTER RADICAL PROSTATECTOMY." Problems in oncology 64, no. 4 (2018): 508–14. http://dx.doi.org/10.37469/0507-3758-2018-64-4-508-514.

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To assess diagnostic capabilities of new receptor-specific radiopharmaceutical - prostate-specific membrane antigen labeled with gallium-68 (68Ga-PSMA) 66 patients with prostate cancer (PC) after radical prostatectomy underwent combined positron emission with computed tomography (PET/CT). In 61/66 cases PSA level was 0,2-1,0 ng/ml, in 5/66 it did not exceed 0,2 ng/ml. The processing of the results concluded in a visual analysis of the images. In 45/66 patients with PET foci of hyperfaxation of radiopharmaceutical were detected. At the same time structural changes in CT were determined only in

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Pike, Lucy C., Christopher M. Thomas, Teresa Guerrero-Urbano, et al. "Guidance on the use of PET for treatment planning in radiotherapy clinical trials." British Journal of Radiology 92, no. 1103 (2019): 20190180. http://dx.doi.org/10.1259/bjr.20190180.

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The aim of this article is to propose meaningful guidance covering the practical and technical issues involved when planning or conducting clinical trials involving positron emission tomography (PET)-guided radiotherapy. The complexity of imaging requirements will depend on the study aims, design and PET methods used. Where PET is used to adapt radiotherapy, a high level of accuracy and reproducibility is required to ensure effective and safe treatment delivery. The guidance in this document is intended to assist researchers designing clinical trials involving PET-guided radiotherapy to provid

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Nestle, U., S. Kremp, D. Hellwig, et al. "Multi-centre calibration of an adaptive thresholding method for PET-based delineation of tumour volumes in radiotherapy planning of lung cancer." Nuklearmedizin 51, no. 03 (2012): 101–10. http://dx.doi.org/10.3413/nukmed-0452-11-12.

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SummaryPurpose: To evaluate the calibration of an adaptive thresholding algorithm (contrastoriented algorithm) for FDG PET-based delineation of tumour volumes in eleven centres with respect to scanner types and image data processing by phantom measurements. Methods: A cylindrical phantom with spheres of different diameters was filled with FDG realizing different signal-to-background ratios and scanned using 5 Siemens Biograph PET/CT scanners, 5 Philips Gemini PET/CT scanners, and one Siemens ECAT-ART PET scanner. All scans were analysed by the contrast-oriented algorithm implemented in two dif

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Kaur, Pawandeep, and Rekha Bhatia. "Development of a Novel Lung Cancer Detection Technique based upon Micro Vessel Density Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 157. http://dx.doi.org/10.23956/ijarcsse/v7i7/0170.

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In the medical field, Image processing methods are widely used. It is a method for the improvement of image, the image which is obtained after processing is useful for earlier detection and various stages of cancer. In cancer tumors such as lung cancer time factor is the important key point because in the targated images of lung cancer time factor is use to discover the abnormality. Basically the development of numerous uncontrolled cells in the tissues create abnormality which later on leads to tumor in lungs. It is necessary to detect lung cancer in earlier stages, if left untreated its grow

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Fedorov, Andriy, David Clunie, Ethan Ulrich, et al. "DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research." PeerJ 4 (May 24, 2016): e2057. http://dx.doi.org/10.7717/peerj.2057.

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Background.Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support

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Krajnc, Denis, Laszlo Papp, Thomas S. Nakuz, et al. "Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics." Cancers 13, no. 6 (2021): 1249. http://dx.doi.org/10.3390/cancers13061249.

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Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were ext

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Wang, Xiuying, Chaoqun Fang, Yong Xia, and Dagan Feng. "Airway segmentation for low-contrast CT images from combined PET/CT scanners based on airway modelling and seed prediction." Biomedical Signal Processing and Control 6, no. 1 (2011): 48–56. http://dx.doi.org/10.1016/j.bspc.2010.05.002.

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Choi, Bo-Hye, Donghwi Hwang, Seung-Kwan Kang та ін. "Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network". Electronics 10, № 15 (2021): 1836. http://dx.doi.org/10.3390/electronics10151836.

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The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained t

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Szumowski, Piotr, Artur Szklarzewski, Łukasz Żukowski, et al. "Pre-Processing Method for Contouring the Uptake Levels of [18F] FDG for Enhanced Specificity of PET Imaging of Solitary Hypermetabolic Pulmonary Nodules." Journal of Clinical Medicine 10, no. 7 (2021): 1430. http://dx.doi.org/10.3390/jcm10071430.

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Background: The paper presents a pre-processing method which, based on positron-emission tomography (PET) images of 18F-fluorodeoxyglucose ([18F] FDG) hypermetabolic pulmonary nodules, makes it possible to obtain additional visual characteristics and use them to enhance the specificity of imaging. Material and Methods: A retrospective analysis of 69 FDG-PET/CT scans of solitary hypermetabolic pulmonary nodules (40 cases of lung cancer and 29 benign tumours), where in each case, the standardised uptake value of the hottest voxel within the defined volume of interest was greater than 2.5 (SUVmax

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Pandey, AnilKumar, Kartik Saroha, ParamDev Sharma, et al. "Development and validation of the suprathreshold stochastic resonance-based image processing method for the detection of abdomino-pelvic tumor on PET/CT scans." Indian Journal of Nuclear Medicine 32, no. 2 (2017): 103. http://dx.doi.org/10.4103/0972-3919.202247.

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Somai, Vencel, David Legrady, and Gabor Tolnai. "Singular value decomposition analysis of back projection operator of maximum likelihood expectation maximization PET image reconstruction." Radiology and Oncology 52, no. 3 (2018): 337–45. http://dx.doi.org/10.2478/raon-2018-0013.

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Abstract Background In emission tomography maximum likelihood expectation maximization reconstruction technique has replaced the analytical approaches in several applications. The most important drawback of this iterative method is its linear rate of convergence and the corresponding computational burden. Therefore, simplifications are usually required in the Monte Carlo simulation of the back projection step. In order to overcome these problems, a reconstruction code has been developed with graphical processing unit based Monte Carlo engine which enabled full physical modelling in the back pr

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Sivasangari, A., D. Deepa, L. Lakshmanan, A. Jesudoss, and M. S. Roobini. "Lung Nodule Classification on Computed Tomography Using Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3427–31. http://dx.doi.org/10.1166/jctn.2020.9199.

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Lung cancer is a leading health issue and the major cause of death among all types of cancers. CT scanning is the popular method for lung cancer diagnosis detection. Manual processing of tomograms take long time for diagnosis. It is not an easy task. This complex work can also reduce the quality of diagnosis. Machine learning and neural network algorithm can be used to automatically process X-ray pictures, tomograms and PET images to detect diseases. The goal of the proposed work is to find any abnormal thing in lungs. Convolutional neural network is trained to classify abnormal area from the

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Duncan, John S. "Brain imaging in epilepsy." Practical Neurology 19, no. 5 (2019): 438–43. http://dx.doi.org/10.1136/practneurol-2018-002180.

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Brain imaging with MRI identifies structural cerebral pathology that may give rise to seizures. The greatest yield is from MRI at 3T using epilepsy protocols, and reported by expert neuroradiologists who possess the full clinical data. X-ray CT scanning has a role in assessing patients with seizures in the context of an acute neurological illness. Identifying a relevant structural lesion with MRI is fundamental in the consideration of epilepsy surgery; it is crucial to establish if a lesion is relevant to the epilepsy or not. If no lesion is identified, developmental MRI and image processing m

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Ahmed, Zeeshan, and Thomas Dandekar. "MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format." F1000Research 4 (December 16, 2015): 1453. http://dx.doi.org/10.12688/f1000research.7329.1.

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Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providi

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Ahmed, Zeeshan, and Thomas Dandekar. "MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format." F1000Research 4 (April 12, 2017): 1453. http://dx.doi.org/10.12688/f1000research.7329.2.

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Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providi

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Ahmed, Zeeshan, and Thomas Dandekar. "MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format." F1000Research 4 (April 4, 2018): 1453. http://dx.doi.org/10.12688/f1000research.7329.3.

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Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providi

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Jemaa, Skander, Jill Fredrickson, Alexandre Coimbra, et al. "A Fully Automated Measurement of Total Metabolic Tumor Burden in Diffuse Large B-Cell Lymphoma and Follicular Lymphoma." Blood 134, Supplement_1 (2019): 4666. http://dx.doi.org/10.1182/blood-2019-124793.

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Introduction: Baseline total metabolic tumor volume (TMTV) from FDG-PET/CT scans has been shown to be prognostic for progression-free survival (PFS) in diffuse large B-cell lymphoma (DLBCL; Kostakoglu et al. Blood 2017) and follicular lymphoma (FL; Meignan et al. J Clin Oncol 2016). Fully automated TMTV measurements could increase reproducibility and enable results in real-time after a PET/CT scan. Although numerous methods for tumor segmentation on FDG PET images are published, they typically involve a manual step to identify a point within each tumor, performed by a trained reader, followed

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Sexauer, Raphael, Thomas Weikert, Kevin Mader, et al. "Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer." Contrast Media & Molecular Imaging 2018 (November 1, 2018): 1–10. http://dx.doi.org/10.1155/2018/5693058.

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Results of PET/CT examinations are communicated as text-based reports which are frequently not fully structured. Incomplete or missing staging information can be a significant source of staging and treatment errors. We compared standard text-based reports to a manual full 3D-segmentation-based approach with respect to TNM completeness and processing time. TNM information was extracted retrospectively from 395 reports. Moreover, the RIS time stamps of these reports were analyzed. 2995 lesions using a set of 41 classification labels (TNM features + location) were manually segmented on the corres

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Zhou, Xiaoliang, Peter Cipriano, Brian Kim, et al. "Detection of nociceptive-related metabolic activity in the spinal cord of low back pain patients using 18F-FDG PET/CT." Scandinavian Journal of Pain 15, no. 1 (2017): 53–57. http://dx.doi.org/10.1016/j.sjpain.2016.11.017.

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AbstractBackgroundOver the past couple of decades, a number of centers in the brain have been identified as important sites of nociceptive processing and are collectively known as the ‘pain matrix.’ Imaging tools such as functional magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) have played roles in defining these pain-relevant, physiologically active brain regions. Similarly, certain segments of the spinal cord are likely more metabolically active in the setting of pain conditions, the location of which is dependent upon location of sym

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Khagi, Bijen, and Goo-Rak Kwon. "3D CNN based Alzheimer’s diseases classification using segmented Grey matter extracted from whole-brain MRI." JOIV : International Journal on Informatics Visualization 5, no. 2 (2021): 200. http://dx.doi.org/10.30630/joiv.5.2.572.

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A recent study from MRI has revealed that there is a minor increase in cerebral-spinal fluid (CSF) content in brain ventricles and sulci, along with a substantial decrease in grey matter (GM) content and brain volume among Alzheimer's disease (AD) patients. It has been discovered that the grey matter volume shrinkage may indicate the possible case of dementia and related diseases like AD. Clinicians and radiologists use imaging techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scan, and Positron Emission Tomography (PET) to diagnose and visualize the tissue contents of

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Zhang, Lin, and Guanglei Zhang. "Brief review on learning-based methods for optical tomography." Journal of Innovative Optical Health Sciences 12, no. 06 (2019): 1930011. http://dx.doi.org/10.1142/s1793545819300118.

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Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities includ

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Dahlsson Leitao, Charles, Sara Rinne, Bogdan Mitran, et al. "Molecular Design of HER3-Targeting Affibody Molecules: Influence of Chelator and Presence of HEHEHE-Tag on Biodistribution of 68Ga-Labeled Tracers." International Journal of Molecular Sciences 20, no. 5 (2019): 1080. http://dx.doi.org/10.3390/ijms20051080.

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Affibody-based imaging of HER3 is a promising approach for patient stratification. We investigated the influence of a hydrophilic HEHEHE-tag ((HE)3-tag) and two different gallium-68/chelator-complexes on the biodistribution of Z08698 with the aim to improve the tracer for PET imaging. Affibody molecules (HE)3-Z08698-X and Z08698-X (X = NOTA, NODAGA) were produced and labeled with gallium-68. Binding specificity and cellular processing were studied in HER3-expressing human cancer cell lines BxPC-3 and DU145. Biodistribution was studied 3 h p.i. in Balb/c nu/nu mice bearing BxPC-3 xenografts. Mi

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Yap, Jeffrey T., Jonathan P. J. Carney, Nathan C. Hall, and David W. Townsend. "Image-Guided Cancer Therapy Using PET/CT." Cancer Journal 10, no. 4 (2004): 221–33. http://dx.doi.org/10.1097/00130404-200407000-00003.

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Antoch, G., and H. Kueh. "How much CT do we need for PET/CT?" Nuklearmedizin 44, S 01 (2005): S24—S31. http://dx.doi.org/10.1055/s-0038-1625211.

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Summary:In clinical practice different CT imaging protocols are implemented within combined PET/CT acquisition protocols. From a radiologist´s perspective the full potential of PET/ CT for oncology imaging can be used only by exploring the full capacity of the CT. This mandates the use of oral and intravenous contrast material as well as a CT image quality which proves diagnostically sufficient by radiology standards. The integration of a fully-diagnostic CT exam portion into combined PET/CT is demanding and requires adjustments of CT acquisition protocols. Given the use of the CT for CT-based

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Krause, B. J., S. M. Eschmann, K. U. Juergens, et al. "Lesion concordance, image quality and artefacts in PET/CT." Nuklearmedizin 49, no. 04 (2010): 129–37. http://dx.doi.org/10.3413/nukmed-0275.

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Summary Aim: This study had three major objectives: 1.) to record the number of concordant (both in PET and CT) pathological lesions in different body regions/organs, 2.) to evaluate the image quality and 3.) to determine both, the quantity and the quality of artefacts in whole body FDG PET/CT scans. Patients, methods: Routine whole body scans of 353 patients referred to FDG-PET/ CT exams at 4 university hospitals were employed. All potentially malignant lesions in 13 different body regions/organs were classified as either concordant or suspicious in FDG-PET or CT only. In the latter case the

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Ghoshal, Abhishek, Aditya Aspat, and Elton Lemos. "OpenCV Image Processing for AI Pet Robot." International Journal of Applied Sciences and Smart Technologies 03, no. 01 (2021): 65–82. http://dx.doi.org/10.24071/ijasst.v3i1.2765.

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The Artificial Intelligence (AI) Pet Robot is a culmination of multiple fields of computer science. This paper showcases the capabilities of our robot. Most of the functionalities stem from image processing made available through OpenCV. The functions of the robot discussed in this paper are face tracking, emotion recognition and a colour-based follow routine. Face tracking allows the robot to keep the face of the user constantly in the frame to allow capturing of facial data. Using this data, emotion recognition achieved an accuracy of 66% on the FER-2013 dataset. The colour-based follow rout

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Römer, Wolfgang, Margaret Chung, Andrew Chan, et al. "Single-Detector Helical CT in PET–CT: Assessment of Image Quality." American Journal of Roentgenology 182, no. 6 (2004): 1571–77. http://dx.doi.org/10.2214/ajr.182.6.1821571.

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Xia, Kai-jian, Jian-qiang Wang, and Jian Cai. "A Novel Adaptive PET/CT Image Fusion Algorithm." Current Bioinformatics 14, no. 7 (2019): 658–66. http://dx.doi.org/10.2174/1574893613666180704153946.

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Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the

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Tu, Dom-Gene, Cheng-Ren Chen, Yu-Wen Wang, Chi-Wen Tu, and Yung Cheng Huang. "Bowel-cleansing methods affecting PET-CT image interpretation." Nuclear Medicine Communications 32, no. 7 (2011): 570–74. http://dx.doi.org/10.1097/mnm.0b013e328345327b.

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Siman, Wendy, Osama Mawlawi, and Cheenu Kappadath. "90Y PET/CT quantitative accuracy and image quality." International Journal of Cancer Therapy and Oncology 2, no. 2 (2014): 020235. http://dx.doi.org/10.14319/ijcto.0202.35.

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Calabria, Ferdinando, Agostino Chiaravalloti, and Orazio Schillaci. "18F-Choline PET/CT Pitfalls in Image Interpretation." Clinical Nuclear Medicine 39, no. 2 (2014): 122–30. http://dx.doi.org/10.1097/rlu.0000000000000303.

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Onishi, Hideo, Keishi Kitamura, Taiga Yamaya, Kazuya Sakaguchi, and Makoto Hishinuma. "Property and Problems of Image Reconstruction Methods in PET and PET/CT." Japanese Journal of Radiological Technology 67, no. 7 (2011): 805–20. http://dx.doi.org/10.6009/jjrt.67.805.

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Duan Liming, 段黎明, and 周远非 Zhou Yuanfei. "Optimization of industrial CT image processing system." High Power Laser and Particle Beams 23, no. 2 (2011): 541–44. http://dx.doi.org/10.3788/hplpb20112302.0541.

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OGURA, TOSHIHIRO. "CT-pancreatography Using Various Image Processing Technique." Japanese Journal of Radiological Technology 59, no. 1 (2003): 55–59. http://dx.doi.org/10.6009/jjrt.kj00000921594.

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