Tomography最新文献

{"title":"Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.","authors":"Xingfeng Li, Yuan Zhang","doi":"10.3390/tomography10100115","DOIUrl":"https://doi.org/10.3390/tomography10100115","url":null,"abstract":"<p><p><b>Background:</b> Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. <b>Methods:</b> To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. <b>Results:</b> The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. <b>Conclusions:</b> System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 10","pages":"1564-1576"},"PeriodicalIF":2.2,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11511430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nailfold Video-Capillaroscopy in Sarcoidosis: New Perspectives and Challenges.","authors":"Maria Chianese, Gianluca Screm, Paola Confalonieri, Francesco Salton, Liliana Trotta, Beatrice Da Re, Antonio Romallo, Alessandra Galantino, Mario D'Oria, Michael Hughes, Giulia Bandini, Marco Confalonieri, Elisa Baratella, Lucrezia Mondini, Barbara Ruaro","doi":"10.3390/tomography10100114","DOIUrl":"https://doi.org/10.3390/tomography10100114","url":null,"abstract":"<p><strong>Introduction: </strong>Nailfold video-capillaroscopy (NVC) is a non-invasive cost-effective technique involving the microscopic examination of small blood vessels of the distal nailfold with a magnification device. It provides valuable information regarding the microcirculation including anomalies such as tortuous or dilated capillaries, hemorrhages, and avascular areas, which can characterize connective tissue diseases. The utility of NVC in the diagnosis and monitoring of systemic sclerosis (SSc) has been investigated in numerous studies allowing the distinction of the specific microvascular pattern of scleroderma from different conditions other than scleroderma (non-scleroderma pattern). Sarcoidosis (SA) is a systemic inflammatory disease that can affect various organs, including the lungs, skin, and lymph nodes. The purpose of our review was to evaluate the current state of the art in the use of NVC in the diagnosis of SA, to understand the indications for its use and any consequent advantages in the management of the disease in different settings in terms of benefits for patients.</p><p><strong>Materials and methods: </strong>We searched for the key terms \"sarcoidosis\" and \"video-capillaroscopy\" in a computerized search of Pub-Med, extending the search back in time without setting limits. We provided a critical overview of the literature, based on a precise evaluation. After our analysis, we examined the six yielded works looking for answers to our questions.</p><p><strong>Results: </strong>Few studies have evaluated that microcirculation is often compromised in SA, with alterations in blood flow and consequent tissue damage.</p><p><strong>Discussion: </strong>Basing on highlighted findings, NVC appears to be a useful tool in the initial evaluation of sarcoidosis patients. Furthermore, capillaroscopy is useful in the evaluation of the coexistence of sarcoidosis and scleroderma spectrum disorder or overlap syndromes.</p><p><strong>Conclusions: </strong>In conclusions, no specific pattern has been described for sarcoidosis, and further re-search is needed to fully understand the implications of nailfold capillaroscopy find-ings in this disease and to establish standardized guidelines for its use in clinical practice.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 10","pages":"1547-1563"},"PeriodicalIF":2.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11511209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Traumatic Brain Injury in Adult Patients with and without Facial Fractures.","authors":"Iulia Tatiana Lupascu, Sorin Hostiuc, Costin Aurelian Minoiu, Mihaela Hostiuc, Bogdan Valeriu Popa","doi":"10.3390/tomography10100113","DOIUrl":"https://doi.org/10.3390/tomography10100113","url":null,"abstract":"<p><strong>Objectives: </strong>Facial fractures and associated traumatic brain injuries represent a worldwide public health concern. Therefore, we aimed to determine the pattern of brain injury accompanying facial fractures by comparing adult patients with and without facial fractures in terms of demographic, clinical, and imaging features.</p><p><strong>Methods: </strong>This single-center, retrospective study included 492 polytrauma patients presenting at our emergency department from January 2019 to July 2023, which were divided in two groups: with facial fractures (FF) and without facial fractures (non-FF). The following data were collected: age, sex, mechanism of trauma (road traffic accident, fall, and other causes), Glasgow Coma Scale (GCS), the evolution of the patient (admitted to a medical ward or intensive care unit, neurosurgery performed, death), and imaging features of the injury. Data were analyzed using descriptive tests, Chi-square tests, and regression analyses. A <i>p</i>-value less than 0.05 was considered statistically significant.</p><p><strong>Results: </strong>In the FF group, there were 79% (<i>n</i> = 102) men and 21% (<i>n</i> = 27) women, with a mean age of 45 ± 17 years, while in the non-FF group, there were 70% (<i>n</i> = 253) men and 30% (<i>n</i> = 110) women, with a mean age 46 ± 17 years. There was a significant association between brain injuries and facial fractures (<i>p</i> < 0.001, AOR 1.7). The most frequent facial fracture affected the zygoma bone in 28.1% (<i>n</i> = 67) cases. The most frequent brain injury associated with FF was subdural hematoma 23.4% (<i>n</i> = 44), and in the non-FF group, the most common head injury was intraparenchymal hematoma 29% (<i>n</i> = 73); Conclusions: Both groups shared similarities regarding gender, age, cause of traumatic event, and outcome but had significant differences in association with brain injuries, ICU admission, and clinical status.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 10","pages":"1534-1546"},"PeriodicalIF":2.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11511231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reading Times of Common Musculoskeletal MRI Examinations: A Survey Study.","authors":"Robert M Kwee, Asaad A H Amasha, Thomas C Kwee","doi":"10.3390/tomography10090112","DOIUrl":"https://doi.org/10.3390/tomography10090112","url":null,"abstract":"<p><strong>Background: </strong>The workload of musculoskeletal radiologists has come under pressure. Our objective was to estimate the reading times of common musculoskeletal MRI examinations.</p><p><strong>Methods: </strong>A total of 144 radiologists were asked to estimate reading times (including interpretation and reporting) for MRI of the shoulder, elbow, wrist, hip, knee, and ankle. Multivariate linear regression analyses were performed.</p><p><strong>Results: </strong>Reported median reading times with interquartile range (IQR) for the shoulder, elbow, wrist, hip, knee, and ankle were 10 (IQR 6-14), 10 (IQR 6-14), 11 (IQR 7.5-14.5), 10 (IQR 6.6-13.4), 8 (IQR 4.6-11.4), and 10 (IQR 6.5-13.5) min, respectively. Radiologists aged 35-44 years reported shorter reading times for the shoulder (β coefficient [β] = B-3.412, <i>p</i> = 0.041), hip (β = -3.596, <i>p</i> = 0.023), and knee (β = -3.541, <i>p</i> = 0.013) than radiologists aged 45-54 years. Radiologists not working in an academic/teaching hospital reported shorter reading times for the hip (β = -3.611, <i>p</i> = 0.025) and knee (β = -3.038, <i>p</i> = 0.035). Female radiologists indicated longer reading times for all joints (β of 2.592 to 5.186, <i>p</i> ≤ 0.034). Radiologists without musculoskeletal fellowship training indicated longer reading times for the shoulder (β = 4.604, <i>p</i> = 0.005), elbow (β = 3.989, <i>p</i> = 0.038), wrist (β = 4.543, <i>p</i> = 0.014), and hip (β = 2.380, <i>p</i> = 0.119). Radiologists with <5 years of post-residency experience indicated longer reading times for all joints (β of 5.355 to 6.984, <i>p</i> ≤ 0.045), and radiologists with 5-10 years of post-residency experience reported longer reading time for the knee (β = 3.660, <i>p</i> = 0.045) than those with >10 years of post-residency experience.</p><p><strong>Conclusions: </strong>There is substantial variation among radiologists in reported reading times for common musculoskeletal MRI examinations. Several radiologist-related determinants appear to be associated with reading speed, including age, gender, hospital type, training, and experience.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1527-1533"},"PeriodicalIF":2.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.","authors":"Xuzhi Zhao, Yi Du, Haizhen Yue","doi":"10.3390/tomography10090111","DOIUrl":"https://doi.org/10.3390/tomography10090111","url":null,"abstract":"<p><strong>Background: </strong>The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images.</p><p><strong>Methods: </strong>This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference.</p><p><strong>Results: </strong>The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD.</p><p><strong>Conclusion: </strong>The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1513-1526"},"PeriodicalIF":2.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomic Analysis of Treatment Effect for Patients with Radiation Necrosis Treated with Pentoxifylline and Vitamin E.","authors":"Jimmy S Patel, Elahheh Salari, Xuxin Chen, Jeffrey Switchenko, Bree R Eaton, Jim Zhong, Xiaofeng Yang, Hui-Kuo G Shu, Lisa J Sudmeier","doi":"10.3390/tomography10090110","DOIUrl":"https://doi.org/10.3390/tomography10090110","url":null,"abstract":"<p><strong>Background: </strong>The combination of oral pentoxifylline (Ptx) and vitamin E (VitE) has been used to treat radiation-induced fibrosis and soft tissue injury. Here, we review outcomes and perform a radiomic analysis of treatment effects in patients prescribed Ptx + VitE at our institution for the treatment of radiation necrosis (RN).</p><p><strong>Methods: </strong>A total of 48 patients treated with stereotactic radiosurgery (SRS) had evidence of RN and had MRI before and after starting Ptx + VitE. The radiation oncologist's impression of the imaging in the electronic medical record was used to score response to treatment. Support Vector Machine (SVM) was used to train a model of radiomics features derived from radiation necrosis on pre- and 1st post-treatment T1 post-contrast MRIs that can classify the ultimate response to treatment with Ptx + VitE.</p><p><strong>Results: </strong>A total of 43.8% of patients showed evidence of improvement, 18.8% showed no change, and 25% showed worsening RN upon imaging after starting Ptx + VitE. The median time-to-response assessment was 3.17 months. Nine patients progressed significantly and required Bevacizumab, hyperbaric oxygen therapy, or surgery. Patients who had multiple lesions treated with SRS were less likely to show improvement (<i>p</i> = 0.037). A total of 34 patients were also prescribed dexamethasone, either before (7), with (16), or after starting (11) treatment. The use of dexamethasone was not associated with an improved response to Ptx + VitE (<i>p</i> = 0.471). Three patients stopped treatment due to side effects. Finally, we were able to develop a machine learning (SVM) model of radiomic features derived from pre- and 1st post-treatment MRIs that was able to predict the ultimate treatment response to Ptx + VitE with receiver operating characteristic (ROC) area under curve (AUC) of 0.69.</p><p><strong>Conclusions: </strong>Ptx + VitE appears safe for the treatment of RN, but randomized data are needed to assess efficacy and validate radiomic models, which may assist with prognostication.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1501-1512"},"PeriodicalIF":2.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.","authors":"Guoxiang Ma, Kai Wang, Ting Zeng, Bin Sun, Liping Yang","doi":"10.3390/tomography10090109","DOIUrl":"https://doi.org/10.3390/tomography10090109","url":null,"abstract":"<p><p>Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1488-1500"},"PeriodicalIF":2.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.","authors":"Arshpreet Singh Badesha, Russell Frood, Marc A Bailey, Patrick M Coughlin, Andrew F Scarsbrook","doi":"10.3390/tomography10090108","DOIUrl":"https://doi.org/10.3390/tomography10090108","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease.</p><p><strong>Methods: </strong>MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted.</p><p><strong>Results: </strong>Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning.</p><p><strong>Conclusion: </strong>Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1455-1487"},"PeriodicalIF":2.2,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.","authors":"Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A Rosenberg, Ghanim Ullah, Gage Redler, Kujtim Latifi, Eduardo G Moros, Issam El Naqa","doi":"10.3390/tomography10090107","DOIUrl":"https://doi.org/10.3390/tomography10090107","url":null,"abstract":"<p><p>Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1439-1454"},"PeriodicalIF":2.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142336628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic Resonance Imaging Biomarkers of Muscle.","authors":"Usha Sinha, Shantanu Sinha","doi":"10.3390/tomography10090106","DOIUrl":"https://doi.org/10.3390/tomography10090106","url":null,"abstract":"<p><p>This review is focused on the current status of quantitative MRI (qMRI) of skeletal muscle. The first section covers the techniques of qMRI in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1-weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, and diffusion tensor imaging to emerging qMRI features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMRI of muscle; the intent is to demonstrate the utility of qMRI in different disease states of the muscle rather than a complete comprehensive survey.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1411-1438"},"PeriodicalIF":2.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142336627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}