Tomography最新文献

{"title":"Multiparametric Magnetic Resonance Imaging Findings of the Pancreas: A Comparison in Patients with Type 1 and 2 Diabetes.","authors":"Mayumi Higashi, Masahiro Tanabe, Katsuya Tanabe, Shigeru Okuya, Koumei Takeda, Yuko Nagao, Katsuyoshi Ito","doi":"10.3390/tomography11020016","DOIUrl":"10.3390/tomography11020016","url":null,"abstract":"<p><strong>Background/objectives: </strong>Diabetes-related pancreatic changes on MRI remain unclear. Thus, we evaluated the pancreatic changes on MRI in patients with both type 1 diabetes (T1D) and type 2 diabetes (T2D) using multiparametric MRI.</p><p><strong>Methods: </strong>This prospective study involved patients with T1D or T2D who underwent upper abdominal 3-T MRI. Additionally, patients without impaired glucose metabolism were retrospectively included as a control. The imaging data included pancreatic anteroposterior (AP) diameter, pancreas-to-muscle signal intensity ratio (SIR) on fat-suppressed T1-weighted image (FS-T1WI), apparent diffusion coefficient (ADC) value, T1 value on T1 map, proton density fat fraction (PDFF), and mean secretion grade of pancreatic juice flow on cine-dynamic magnetic resonance cholangiopancreatography (MRCP). The MR measurements were compared using one-way analysis of variance and the Kruskal-Wallis test.</p><p><strong>Results: </strong>Sixty-one patients with T1D (<i>n</i> = 7) or T2D (<i>n</i> = 54) and 21 control patients were evaluated. The pancreatic AP diameters were significantly smaller in patients with T1D than in patients with T2D (<i>p</i> < 0.05). The average SIR on FS-T1WI was significantly lower in patients with T1D than in controls (<i>p</i> < 0.001). The average ADC and T1 values of the pancreas were significantly higher in patients with T1D than in patients with T2D (<i>p</i> < 0.01) and controls (<i>p</i> < 0.05). The mean secretion grade of pancreatic juice flow was significantly lower in patients with T1D than in controls (<i>p</i> = 0.019). The average PDFF of the pancreas was significantly higher in patients with T2D than in controls (<i>p</i> = 0.029).</p><p><strong>Conclusions: </strong>Patients with T1D had reduced pancreas size, increased pancreatic T1 and ADC values, and decreased pancreatic juice flow on cine-dynamic MRCP, whereas patients with T2D had increased pancreatic fat content.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494710","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":"Graph Neural Network Learning on the Pediatric Structural Connectome.","authors":"Anand Srinivasan, Rajikha Raja, John O Glass, Melissa M Hudson, Noah D Sabin, Kevin R Krull, Wilburn E Reddick","doi":"10.3390/tomography11020014","DOIUrl":"10.3390/tomography11020014","url":null,"abstract":"<p><strong>Purpose: </strong>Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), specifically graph convolutional networks (GCNs), have gained popularity lately for their effectiveness in learning on graph data, achieving strong performance in adult sex classification tasks, their application to pediatric populations remains unexplored. We seek to characterize the capacity for GNN models to learn connectomic patterns on pediatric data through an exploration of training techniques and architectural design choices.</p><p><strong>Methods: </strong>Two datasets comprising an adult BRIGHT dataset (N = 147 Hodgkin's lymphoma survivors and N = 162 age similar controls) and a pediatric Human Connectome Project in Development (HCP-D) dataset (N = 135 healthy subjects) were utilized. Two GNN models (GCN simple and GCN residual), a deep neural network (multi-layer perceptron), and two standard machine learning models (random forest and support vector machine) were trained. Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. Models were assessed across a range of metrics including accuracy, AUC score, and adversarial robustness.</p><p><strong>Results: </strong>GNNs outperformed other models across both populations. Notably, adult GNN models achieved 85.1% accuracy in sex classification on unseen adult participants, consistent with prior studies. The extension of the adult models to the pediatric dataset and training on the smaller pediatric dataset were sub-optimal in their performance. Using adult data to augment pediatric models, the best GNN achieved comparable accuracy across unseen pediatric (83.0%) and adult (81.3%) participants. Adversarial sensitivity experiments showed that the simple GCN remained the most robust to perturbations, followed by the multi-layer perceptron and the residual GCN.</p><p><strong>Conclusions: </strong>These findings underscore the potential of GNNs in advancing our understanding of sex-specific neurological development and disorders and highlight the importance of data augmentation in overcoming challenges associated with small pediatric datasets. Further, they highlight relevant tradeoffs in the design landscape of connectomic GNNs. For example, while the simpler GNN model tested exhibits marginally worse accuracy and AUC scores in comparison to the more complex residual GNN, it demonstrates a higher degree of adversarial robustness.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494705","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":"Impact of Deep Learning 3D CT Super-Resolution on AI-Based Pulmonary Nodule Characterization.","authors":"Dongok Kim, Chulkyun Ahn, Jong Hyo Kim","doi":"10.3390/tomography11020013","DOIUrl":"10.3390/tomography11020013","url":null,"abstract":"<p><strong>Background/objectives: </strong>Correct pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thickness has an adverse effect on the accuracy of the pulmonary nodule volumetry.</p><p><strong>Methods: </strong>We propose a deep learning based super-resolution technique to generate thin-slice CT images from thick-slice CT images. Analysis of the lung nodule volumetry and categorization accuracy was performed using commercially available AI-based lung cancer screening software.</p><p><strong>Results: </strong>The accuracy of pulmonary nodule categorization increased from 72.7 percent to 94.5 percent when thick-slice CT images were converted to generated-thin-slice CT images.</p><p><strong>Conclusions: </strong>Applying the super-resolution-based slice generation on thick-slice CT images prior to automatic nodule evaluation significantly increases the accuracy of pulmonary nodule volumetry and corresponding pulmonary nodule category.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11860810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494707","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":"Ultrashort Echo Time Magnetic Resonance Morphology of Discovertebral Junction in Chronic Low Back Pain Subjects.","authors":"Palanan Siriwananrangsun, Tim Finkenstaedt, Karen C Chen, Won C Bae","doi":"10.3390/tomography11020012","DOIUrl":"10.3390/tomography11020012","url":null,"abstract":"<p><p><i>Background:</i> Chronic low back pain (LBP) has been associated with intervertebral disc (IVD) degeneration, but its association with abnormal morphology at the discovertebral junction (DVJ) is unclear. The goal of this study was to evaluate the DVJ morphology in asymptomatic (Asx) and symptomatic (Sx) subjects for LBP using ultrashort echo time (UTE) MRI. <i>Methods:</i> We recruited 42 subjects (12 Asx and 32 Sx). Lumbar IVD degeneration was assessed using Pfirrmann grading (1 to 5), while the abnormality of DVJ (0 = normal; 1 = focal; 2 = broad abnormality) was assessed using UTE MRI. The effects of LBP and level on the mean IVD and DVJ grades, the correlation between IVD and DVJ grade, and the effect of LBP and age on the number of abnormal DVJs within a subject were determined. <i>Results:</i> IVD grade was higher in Sx subjects (<i>p</i> = 0.013), varying with disc level (<i>p</i> = 0.033), adjusted for age (<i>p</i> < 0.01). Similarly, DVJ grade was also significantly higher in Sx subjects (<i>p</i> = 0.001), but it did not vary with DVJ level (<i>p</i> = 0.7), adjusted for age (<i>p</i> = 0.5). There was a weak positive (rho = 0.344; <i>p</i> < 0.001) correlation between DVJ and IVD grade. The total number of abnormal DVJs within a subject was higher in Sx subjects (<i>p</i> < 0.001), but not with respect to age (<i>p</i> = 0.6) due to a large spread throughout the age range. <i>Conclusions:</i> These results demonstrate the feasibility of using in vivo UTE MRI of the lumbar spine to evaluate the DVJ and the correlation of DVJ with LBP. This study highlights the need for a better understanding of DVJ pathology and the inclusion of DVJ assessment in routine lumbar MRI.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11860485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494720","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":"Correlation of Coronary Calcium Measured on Conventional Computed Tomography with Coronary Angiography Findings in Lung Transplant Patients.","authors":"Sergio Tapia Concha, Concepción Fariñas-Álvarez, Pedro Muñoz Cacho, José Manuel Cifrian Martínez, Javier Zueco Gil, José Antonio Parra Blanco","doi":"10.3390/tomography11020011","DOIUrl":"10.3390/tomography11020011","url":null,"abstract":"<p><p><b>Introduction and objective:</b> The pre-transplant protocol for lung transplant candidates includes a chest CT scan to assess disease progression and often coronary angiography (CA) to rule out coronary artery disease (CAD). Coronary artery calcium is commonly observed in these pre-transplant CT scans. This study aims to evaluate the relationship between coronary calcium detected on CT and findings from CA to determine whether calcium presence could serve as an additional criterion for selecting patients for CA. <b>Material and Methods</b>: We included 252 consecutive lung transplant patients who had both a CT scan and CA within 365 days of each other. Coronary calcium quantification was performed using artery-based, segment artery-based, and visual assessment methods. CA findings were classified by stenosis severity: ≤20%, 21-70%, and >70%. <b>Results:</b> This study showed very high concordance (kappa = 0.896; 95% CI: 0.843-0.948) between the three methods, especially in distinguishing patients without and with coronary calcium (kappa = 1.000; 95% CI: 0.929-1.071). ROC analysis identified the absence of coronary calcium as the best cutoff to differentiate patients with ≤20% stenosis from those with >21%, with a sensitivity of 73.5%, specificity of 55.7%, PPV of 28.5%, and NPV of 90%. Only 11 patients (8.7%) without coronary calcium had stenosis of 21-70%, and only 2 (1.6%) had stenosis > 70%. <b>Conclusions:</b> The visual assessment method yielded results similar to the other two quantification methods. The absence of coronary calcium in pre-transplant CT may be a useful criterion for selecting patients for CA.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11860483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494704","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":"Enhanced Detection of Residual Breast Cancer Post-Excisional Biopsy: Comparative Analysis of Contrast-Enhanced MRI with and Without Diffusion-Weighted Imaging.","authors":"Han Song Mun, Bong Joo Kang, Sung Hun Kim, Ga Eun Park","doi":"10.3390/tomography11010010","DOIUrl":"10.3390/tomography11010010","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the effectiveness of breast MRI, including diffusion-weighted imaging (DWI), in detecting residual lesions in patients with malignancy after excisional biopsy.</p><p><strong>Methods: </strong>From January 2018 to December 2023, 3T breast MRI was performed to assess lesion morphology, residual size, and enhancement kinetics. The apparent diffusion coefficient (ADC) values were measured, and the diagnostic outcomes of CE-MRI, CE-MRI with DWI, mammography (MG), and ultrasound (US) were compared with clinical and histopathological data.</p><p><strong>Results: </strong>A total of 152 lesions were analyzed, with 36.2% showing residual malignancy. Both CE-MRI and CE-MRI with DWI effectively identified residual lesions, with significant differences in morphology, size, kinetic patterns, and ADC values (all <i>p</i> < 0.001). CE-MRI with DWI showed a sensitivity of 90.9% and an NPV of 93.6%, compared with 89.1% sensitivity and 92.2% NPV for CE-MRI alone. Sensitivities for MG and US were 57.1% and 38.7%, with NPVs of 64.7% and 59.6%, respectively. Diagnostic accuracy was highest for CE-MRI with DWI (80.9%), followed by CE-MRI (79.0%), MG (60.3%), and US (59.7%). The AUC for CE-MRI with DWI (0.831) was slightly higher than CE-MRI alone (0.811), though not significant (<i>p</i> = 0.095). AUCs for MG and US were lower at 0.623 and 0.563, with no significant difference between MG and US (<i>p</i> = 0.234).</p><p><strong>Conclusions: </strong>CE-MRI with DWI and CE-MRI alone were comparable and demonstrated excellent performance in discriminating between women with and without residual disease. Integrating CE-MRI with DWI could become a standard protocol for patients with suspected residual malignancy after excisional biopsy.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11769435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034830","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":"Comparative Sensitivity of MRI Indices for Myelin Assessment in Spinal Cord Regions.","authors":"Philip Kyeremeh Jnr Oppong, Hiroyuki Hamaguchi, Maho Kitagawa, Nina Patzke, Kevin C Wakeman, Khin Khin Tha","doi":"10.3390/tomography11010008","DOIUrl":"10.3390/tomography11010008","url":null,"abstract":"<p><p><b>Background/Objectives:</b> Although multiple magnetic resonance imaging (MRI) indices are known to be sensitive to the noninvasive assessment of myelin integrity, their relative sensitivities have not been directly compared. This study aimed to identify the most sensitive MRI index for characterizing myelin composition in the spinal cord's gray matter (GM) and white matter (WM). <b>Methods:</b> MRI was performed on a deer's ex vivo cervical spinal cord. Quantitative indices known to be sensitive to myelin, including the myelin water fraction (MWF), magnetization transfer ratio (MTR), the signal ratio between T1- and T2-weighted images (T1W/T2W), fractional anisotropy (FA), mean diffusivity (MD), electrical conductivity (σ), and T1, T2, and T1ρ relaxation times were calculated. Their mean values were compared using repeated measures analysis of variance (ANOVA) and post hoc Bonferroni tests or Friedman and post hoc Wilcoxon tests to identify differences across GM and WM columns possessing distinct myelin distributions, as revealed by histological analysis. Relationships among the indices were examined using Spearman's rank-order correlation analysis. Corrected <i>p</i> < 0.05 was considered statistically significant. <b>Results:</b> All indices except σ differed significantly between GM and all WM columns. Two of the three WM columns had significantly different MWF, FA, MD, and T2, whereas one WM column had significantly different MTR, σ, T1, and T1ρ from the others. A significant moderate to very strong correlation was observed among most indices. <b>Conclusions:</b> The sensitivity of MRI indices in distinguishing spinal cord regions varied. A strategic combination of two or more indices may allow the accurate differentiation of spinal cord regions.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11769071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034792","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":"CT Angiography Assessment of Dorsal Pancreatic Artery and Intrapancreatic Arcade Anatomy: Impact on Whipple Surgery Outcomes.","authors":"Gorkem Ozdemir, Tolga Olmez, Okan Dilek, Berkay Eyi, Alper Sozutek, Ahmet Seker","doi":"10.3390/tomography11010009","DOIUrl":"10.3390/tomography11010009","url":null,"abstract":"<p><strong>Background/objectives: </strong>The aim was to investigate the association between variations in the dorsal pancreatic artery (DPA) and intrapancreatic arcade anatomy with Whipple procedure outcomes and postoperative complications.</p><p><strong>Methods: </strong>This retrospective study was conducted with 362 patients who underwent a Whipple procedure at the Department of Gastroenterological Surgery of Adana City Training and Research Hospital between January 2018 and April 2024. All data collected from medical records were compared and statistically analyzed according to the patients' survival status and arcade subtypes.</p><p><strong>Results: </strong>After excluding cases that did not meet the study criteria, a total of 284 patients were included in the study. DPA was visualized in 55.98% (159/284) of patients, while the intrapancreatic arcade was observed in 25% (71/284). The most common origin of the DPA was the splenic artery in 69.2% (n = 110) of patients, followed by the superior mesenteric artery in 17.6% (n = 28). The frequency of intrapancreatic arcade anatomy variations was as follows: type 1: 28.2% (n = 20), type 2: 49.3% (n = 35) and type 3: 22.5% (n = 16). Arcade type 4 anatomy was not detected. Postoperative pancreatic fistula (POPF) complication was found to be statistically significantly higher in patients with type 3 anatomy (<i>p</i> = 0.042). The 90-day mortality and long-term mortality rates did not differ among the groups based on the variations in both DPA and intrapancreatic arcade anatomy types.</p><p><strong>Conclusions: </strong>Patients with intrapancreatic arcade type 3 anatomy had a higher risk of POPF complications. Determination of preoperative arcade type by computed tomography (CT) angiography may help to predict the risk of POPF.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11769064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034795","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":"Fast Hadamard-Encoded 7T Spectroscopic Imaging of Human Brain.","authors":"Chan Hong Moon, Frank S Lieberman, Hoby P Hetherington, Jullie W Pan","doi":"10.3390/tomography11010007","DOIUrl":"10.3390/tomography11010007","url":null,"abstract":"<p><p><b>Background/Objectives</b>: The increased SNR available at 7T combined with fast readout trajectories enables accelerated spectroscopic imaging acquisitions for clinical applications. In this report, we evaluate the performance of a Hadamard slice encoding strategy with a 2D rosette trajectory for multi-slice fast spectroscopic imaging at 7T. <b>Methods</b>: Moderate-TE (~40 ms) spin echo and J-refocused polarization transfer sequences were acquired with simultaneous Hadamard multi-slice excitations and rosette in-plane encoding. The moderate spin echo sequence, which targets singlet compounds (i.e., N-acetyl aspartate, creatine, and choline), uses cascaded multi-slice RF excitation pulses to minimize the chemical shift dispersion error. The J-refocused sequence targets coupled spin systems (i.e., glutamate and myo-inositol) using simultaneous multi-slice excitation to maintain the same TE across all slices. A modified Hadamard slice encoding strategy was used to decrease the peak RF pulse amplitude of the simultaneous multi-slice excitation pulse for the J-refocused acquisition. <b>Results</b>: The accuracy of multi-slice and single-slice rosette spectroscopic imaging (RSI) is comparable to conventional Cartesian-encoded spectroscopic imaging (CSI). Spectral analyses for the J-refocused studies of glutamate and myo-inositol show that the Cramer Rao lower bounds are not significantly different between the fast RSI and conventional CSI studies. Linear regressions of creatine/N-acetyl aspartate and glutamate/N-acetyl aspartate with tissue gray matter content are consistent with literature values. <b>Conclusions</b>: With minimal gradient demands and fast acquisition times, the 2.2 min to 9 min for single- to four-slice RSI acquisitions are well tolerated by healthy subjects and tumor patients, and show results that are consistent with clinical outcomes.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11769540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034858","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":"Unraveling the Invisible: Topological Data Analysis as the New Frontier in Radiology's Diagnostic Arsenal.","authors":"Yashbir Singh, Emilio Quaia","doi":"10.3390/tomography11010006","DOIUrl":"10.3390/tomography11010006","url":null,"abstract":"<p><p>This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential in medical image interpretation. TDA, which is grounded in mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA's applications across medical imaging domains, including tumor characterization, cardiovascular imaging, and COVID-19 detection, where it demonstrates 15-20% improvements over traditional methods. The synergy between TDA and artificial intelligence presents promising opportunities for enhanced diagnostic accuracy. While implementation challenges exist, TDA's ability to uncover hidden patterns positions it as a transformative tool in modern radiology.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11768448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034881","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}