Tomography最新文献

{"title":"Preliminary Results of Clinical Experience with Consolidative High-Dose Thoracic Radiotherapy for Patients with Extensive-Stage Small Cell Lung Cancer.","authors":"Hakyoung Kim, Jeongeun Hwang, Sun Myung Kim, Dae Sik Yang","doi":"10.3390/tomography11050055","DOIUrl":"10.3390/tomography11050055","url":null,"abstract":"<p><strong>Objectives: </strong>Extensive-stage small-cell lung cancer (SCLC) has a poor prognosis, but recently, the combination of immunotherapy and chemotherapy has improved treatment outcomes in some patients, and treatment plans may vary depending on the individual's general condition and tumor response. In addition, intrathoracic tumor control remains a major challenge for this disease. In the current study, we aim to share our clinical experience and demonstrate that consolidative high-dose thoracic radiotherapy effectively reduces intrathoracic tumor recurrence while maintaining acceptable treatment-related toxicities.</p><p><strong>Materials and methods: </strong>The medical records of 81 SCLC patients treated at Korea University Guro Hospital from January 2019 to December 2023 were reviewed retrospectively. Among them, 22 patients with extensive-stage SCLC who had a favorable tumor response after systemic therapy, including those with oligo-progressive disease limited to the thoracic region and suitable for curative local therapy, received consolidative radiotherapy. A total dose of 52.5 Gy in 25 fractions was administered over 5 weeks to all patients with extensive-stage SCLC.</p><p><strong>Results and conclusions: </strong>The median follow-up time was 22 months (range: 8-59 months), with the median follow-up period after completing consolidative radiotherapy being 13 months (range: 4-35 months). In-field local recurrence occurred in only one patient after consolidative thoracic radiotherapy. Most importantly, 10 patients with oligo-progressive disease at the thoracic site, at the time of tumor response, remained stable without further intrathoracic in-field recurrence. Additionally, no severe cases of radiation pneumonitis or esophagitis were observed. Based on our institution's experience, consolidative high-dose thoracic radiotherapy is well-tolerated and associated with fewer intrathoracic recurrences, leading to improved long-term survival in carefully selected patients with extensive-stage SCLC. Given these findings, we believe consolidative radiotherapy should be considered more proactively in clinical practice. Furthermore, these results may help guide the design of future clinical trials.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12115686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152855","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 Perfusion Imaging in Patients with Acute Ischemic Stroke: The Role of Premorbid Statin Treatment.","authors":"Eliseo Picchi, Francesca Di Giuliano, Noemi Pucci, Fabrizio Sallustio, Silvia Minosse, Alfredo Paolo Mascolo, Federico Marrama, Valentina Ferrazzoli, Valerio Da Ros, Marina Diomedi, Massimo Federici, Francesco Garaci","doi":"10.3390/tomography11050054","DOIUrl":"10.3390/tomography11050054","url":null,"abstract":"<p><strong>Background: </strong>Statins appear to be useful in patients with acute ischemic stroke. Our aim was to evaluate the association between premorbid statin treatment and CT perfusion characteristics of acute ischemic stroke.</p><p><strong>Methods: </strong>A retrospective analysis of patients with acute stroke secondary to occlusion of large vessels in the anterior circulation was performed to assess collateral flow, ischemic core volume, and ischemic penumbra using CT angiography and CT perfusion maps. Fisher's exact test was used to compare baseline characteristics of patients in the two groups. The Wilcoxon rank-sum test for independent groups was used to compare all variables obtained for the two different groups with and without statin use.</p><p><strong>Results: </strong>We identified 61 patients, including 29 treated with statins and 32 not treated with statins before stroke onset matched by age, gender, and vascular risk factors except for hypercholesterolemia. The statin group showed lower National Institutes of health Stroke Scale scores at onset (14 ± 6.1 vs. 16 ± 4.5; <i>p</i> = 0.04) and lower volumes of brain tissue characterized by impaired cerebral blood flow (CBF), cerebral blood volume (CBV), and Tmax9.5-25s; otherwise, no statistically significant difference was found in the volume of the Tmax16-25s between the two groups.</p><p><strong>Conclusions: </strong>Premorbid statin treatment is associated with a favorable imaging condition of acute ischemic stroke in terms of ischemic core and ischemic penumbra volume.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12115380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152804","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":"Shoulder Injury Related to Vaccine Administration (SIRVA) Following COVID-19 Vaccination: Correlating MRI Findings with Patient Demographics.","authors":"Naser Obeidat, Ruba Khasawneh, Ahmad Alrawashdeh, Ali M Abdel Kareem, Mohammad K Al-Na'asan, Mohammad Alkhatatba, Suhaib Bani Essa","doi":"10.3390/tomography11050053","DOIUrl":"10.3390/tomography11050053","url":null,"abstract":"<p><strong>Objectives: </strong>Shoulder injury related to vaccine administration (SIRVA), previously observed with influenza vaccines, has gained clinical significance with widespread COVID-19 vaccination. However, few studies correlate vaccine types and demographic factors with the MRI findings of SIRVA. This study aimed to evaluate MRI findings of SIRVA following COVID-19 vaccination and assess associations with vaccine type and patient characteristics.</p><p><strong>Methods: </strong>A retrospective cohort study was conducted on 35 patients with new-onset shoulder complaints within six weeks of COVID-19 vaccination between May 2021 and May 2022. MRI findings suggestive of SIRVA were reviewed, including subacromial bursitis, rotator cuff tears, and adhesive capsulitis. Demographic data, vaccine type, clinical symptoms, and treatments were collected. Follow-up interviews (1-30 September 2024) assessed symptom persistence and vaccine hesitancy. Descriptive statistics and Chi-square tests were used to explore associations.</p><p><strong>Results: </strong>Of the 35 patients (mean age 53.6 ± 9.0 years; 54.3% female), subacromial bursitis was the most common MRI finding (89.5%), followed by tendonitis (47.4%) and adhesive capsulitis (36.8%). Tendonitis correlated with older age (<i>p</i> = 0.024) and AstraZeneca vaccination (<i>p</i> = 0.033). Subacromial bursitis was linked to female sex (<i>p</i> = 0.013) and higher BMI (<i>p</i> = 0.023). Adhesive capsulitis was associated with receiving the Sinopharm vaccine (<i>p</i> = 0.029). Persistent symptoms (22.9%) were more common in younger patients, women, and those with right-sided injections.</p><p><strong>Conclusions: </strong>SIRVA following COVID-19 vaccination showed different MRI patterns associated with female sex, higher BMI, and vaccine type. Awareness of these patterns may expedite recognition of COVID-19-associated SIRVA in routine practice.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12115978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152866","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":"Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.","authors":"Yuxiao Gao, Yang Jiang, Yanhong Peng, Fujiang Yuan, Xinyue Zhang, Jianfeng Wang","doi":"10.3390/tomography11050052","DOIUrl":"10.3390/tomography11050052","url":null,"abstract":"<p><p>Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12115501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152852","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":"Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.","authors":"Haoyan Li, Zhenpeng Chen, Shuaiyi Gao, Jiaqi Hu, Zhihao Yang, Yun Peng, Jihang Sun","doi":"10.3390/tomography11050051","DOIUrl":"10.3390/tomography11050051","url":null,"abstract":"<p><p><b>Objectives</b>: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. <b>Methods</b>: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. <b>Results</b>: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. <b>Conclusions</b>: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152854","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":"Long-Term Effects of COVID-19: Analysis of Imaging Findings in Patients Evaluated by Computed Tomography from 2020 to 2024.","authors":"Zeynep Keskin, Mihrican Yeşildağ, Ömer Özberk, Kemal Ödev, Fatih Ateş, Bengü Özkan Bakdık, Şehriban Çağlak Kardaş","doi":"10.3390/tomography11050049","DOIUrl":"10.3390/tomography11050049","url":null,"abstract":"<p><p><b>Background:</b> This study aims to systematically evaluate the findings from computed tomography (CT) examinations conducted at least three months post-diagnosis of COVID-19 in patients diagnosed between 2020 and 2024. <b>Objective:</b> To determine the frequency and characteristics of CT findings in the post-COVID-19 period, analyze long-term effects on lung parenchyma, and contribute to the development of clinical follow-up and treatment strategies based on the collected data. <b>Materials and Methods:</b> Ethical approval was obtained for this retrospective study, and individual consent was waived. A total of 76 patients were included in the study, aged 18 and older, diagnosed with COVID-19 between March 2020 and November 2024, who underwent follow-up chest CT scans at 3-6 months, 6-12 months, and/or 12 months post-diagnosis. CT images were obtained in the supine position without contrast and evaluated by two experienced radiologists using a CT severity score (CT-SS) system, which quantifies lung involvement. Statistical analyses were performed using IBM SPSS 23.0, with significance set at <i>p</i> < 0.05. <b>Results:</b> The results indicated a mean CT-SS of 10.58 ± 0.659. Significant associations were found between age, CT scores, and the necessity for intensive care or mechanical ventilation. The most common CT findings included ground-glass opacities, reticular patterns, and traction bronchiectasis, particularly increasing with age and over time. <b>Conclusion:</b> This study emphasizes the persistent alterations in lung parenchyma following COVID-19, highlighting the importance of continuous monitoring and tailored treatment strategies for affected patients to improve long-term outcomes.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12115870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152850","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":"Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.","authors":"Debasmita Das, Chayna Sarkar, Biswadeep Das","doi":"10.3390/tomography11050050","DOIUrl":"10.3390/tomography11050050","url":null,"abstract":"<p><strong>Background/objectives: </strong>Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area.</p><p><strong>Methods: </strong>Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG's convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU.</p><p><strong>Results: </strong>According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model's high accuracy in brain tumor classification.</p><p><strong>Conclusions: </strong>The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12115478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152862","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":"Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.","authors":"Giovanni Foti, Flavio Spoto, Thomas Mignolli, Alessandro Spezia, Luigi Romano, Guglielmo Manenti, Nicolò Cardobi, Paolo Avanzi","doi":"10.3390/tomography11040048","DOIUrl":"https://doi.org/10.3390/tomography11040048","url":null,"abstract":"<p><strong>Background: </strong>Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexplored in clinical practice.</p><p><strong>Purpose: </strong>The purpose of this study was to evaluate the diagnostic accuracy of 2-fold and 4-fold DL-accelerated shoulder MRI protocols compared to standard protocols in clinical practice.</p><p><strong>Materials and methods: </strong>In this prospective single-center study, 88 consecutive patients (49 males, 39 females; mean age, 51 years) underwent shoulder MRI examinations using standard, 2-fold (DL2), and 4-fold (DL4) accelerated protocols between June 2023 and January 2024. Four independent radiologists (experience range: 4-25 years) evaluated the presence of bone marrow edema (BME), rotator cuff tears, and labral lesions. The sensitivity, specificity, and interobserver agreement were calculated. Diagnostic confidence was assessed using a 4-point scale. The impact of reader experience was analyzed by stratifying the radiologists into ≤10 and >10 years of experience.</p><p><strong>Results: </strong>Both accelerated protocols demonstrated high diagnostic accuracy. For BME detection, DL2 and DL4 achieved 100% sensitivity and specificity. In rotator cuff evaluation, DL2 showed a sensitivity of 98-100% and specificity of 99-100%, while DL4 maintained a sensitivity of 95-98% and specificity of 99-100%. Labral tear detection showed perfect sensitivity (100%) with DL2 and slightly lower sensitivity (89-100%) with DL4. Interobserver agreement was excellent across the protocols (Kendall's W = 0.92-0.98). Reader experience did not significantly impact diagnostic performance. The area under the ROC curve was 0.94 for DL2 and 0.90 for DL4 (<i>p</i> = 0.32).</p><p><strong>Clinical implications: </strong>The implementation of DL-accelerated protocols, particularly DL2, could improve workflow efficiency by reducing acquisition times by 50% while maintaining diagnostic reliability. This could increase patient throughput and accessibility to MRI examinations without compromising diagnostic quality.</p><p><strong>Conclusions: </strong>DL-accelerated shoulder MRI protocols demonstrate high diagnostic accuracy, with DL2 showing performance nearly identical to that of the standard protocol. While DL4 maintains acceptable diagnostic accuracy, it shows a slight sensitivity reduction for subtle pathologies, particularly among less experienced readers. The DL2 protocol represents an optimal balance between acquisition time reduction and diagnostic confidence.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056923","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":"Use of Open-Source Large Language Models for Automatic Synthesis of the Entire Imaging Medical Records of Patients: A Feasibility Study.","authors":"Fabio Mattiussi, Francesco Magoga, Simone Schiaffino, Vittorio Ferrari, Ermidio Rezzonico, Filippo Del Grande, Stefania Rizzo","doi":"10.3390/tomography11040047","DOIUrl":"https://doi.org/10.3390/tomography11040047","url":null,"abstract":"<p><strong>Background/objectives: </strong>Reviewing the entire history of imaging exams of a single patient's records is an essential step in clinical practice, but it is time and resource consuming, with potential negative effects on workflow and on the quality of medical decisions. The main objective of this study was to evaluate the applicability of three open-source large language models (LLMs) for the automatic generation of concise summaries of patient's imaging records. Secondary objectives were to assess correlations among the LLMs and to evaluate the length reduction provided by each model.</p><p><strong>Methods: </strong>Three state-of-the-art open-source large language models were selected: Llama 3.2 11B, Mistral 7B, and Falcon 7B. Each model was given a set of radiology reports. The summaries produced by the models were evaluated by two experienced radiologists and one experienced clinical physician using standardized metrics.</p><p><strong>Results: </strong>A variable number of radiological reports (n = 12-56) from four patients were selected and evaluated. The summaries generated by the three LLM showed a good level of accuracy compared with the information contained in the original reports, with positive ratings on both clinical relevance and ease of reference. According to the experts' evaluations, the use of the summaries generated by LLMs could help to reduce the time spent on reviewing the previous imaging examinations performed, preserving the quality of clinical data.</p><p><strong>Conclusions: </strong>Our results suggest that LLMs are able to generate summaries of the imaging history of patients, and these summaries could improve radiology workflow making it easier to manage large volumes of reports.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057187","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":"Temporal Shift When Comparing Contrast-Agent Concentration Curves Estimated Using Quantitative Susceptibility Mapping (QSM) and ΔR2*: The Association Between Vortex Parameters and Oxygen Extraction Fraction.","authors":"Ronnie Wirestam, Anna Lundberg, Linda Knutsson, Emelie Lind","doi":"10.3390/tomography11040046","DOIUrl":"https://doi.org/10.3390/tomography11040046","url":null,"abstract":"<p><strong>Background: </strong>When plotting data points corresponding to the contrast-agent-induced change in transverse relaxation rate from a dynamic gradient-echo (GRE) magnetic resonance imaging (MRI) study versus a corresponding spin-echo study, a loop or vortex curve rather than a reversible line is formed. The vortex curve area is likely to reflect vessel architecture and oxygenation level. In this study, the vortex effect seen when using only GRE-based estimates, i.e., contrast-agent concentration based on GRE transverse relaxation rate and contrast-agent concentration based on quantitative susceptibility mapping (QSM), was investigated.</p><p><strong>Methods: </strong>Twenty healthy volunteers were examined using 3 T MRI. Magnitude and phase dynamic contrast-enhanced MRI (DSC-MRI) data were obtained using GRE echo-planar imaging. Vortex curves for grey-matter (GM) regions and for arterial input function (AIF) data were constructed by plotting concentration based on GRE transverse relaxation rate versus concentration based on QSM. Vortex parameters (vortex area and normalised vortex width) were compared with QSM-based whole-brain OEF estimates obtained using 3D GRE.</p><p><strong>Results: </strong>An obvious vortex effect was observed, and both GM vortex parameters showed a moderate and significant correlation with OEF (r = -0.51, <i>p</i> = 0.02). The vortex parameters for AIF data showed no significant correlation with OEF.</p><p><strong>Conclusions: </strong>GRE-based GM vortex parameters correlated significantly with whole-brain OEF. In agreement with expectations, the corresponding AIF data, representing high fractions of arterial blood, showed no significant correlation. Novel parameters, based solely on standard GRE protocols, are of relevance to investigate, considering that GRE-based DSC-MRI is very common in brain tumour applications.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143994161","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}