Pub Date : 2024-06-24DOI: 10.3390/tomography10070072
Domenico Albano, Umberto Viglino, Francesco Esposito, Aldo Rizzo, Carmelo Messina, Salvatore Gitto, Stefano Fusco, Francesca Serpi, Benedikt Kamp, Anja Müller-Lutz, Riccardo D'Ambrosi, Luca Maria Sconfienza, Philipp Sewerin
This review examines the latest advancements in compositional and quantitative cartilage MRI techniques, addressing both their potential and challenges. The integration of these advancements promises to improve disease detection, treatment monitoring, and overall patient care. We want to highlight the pivotal task of translating these techniques into widespread clinical use, the transition of cartilage MRI from technical validation to clinical application, emphasizing its critical role in identifying early signs of degenerative and inflammatory joint diseases. Recognizing these changes early may enable informed treatment decisions, thereby facilitating personalized medicine approaches. The evolving landscape of cartilage MRI underscores its increasing importance in clinical practice, offering valuable insights for patient management and therapeutic interventions. This review aims to discuss the old evidence and new insights about the evaluation of articular cartilage through MRI, with an update on the most recent literature published on novel quantitative sequences.
{"title":"Quantitative and Compositional MRI of the Articular Cartilage: A Narrative Review.","authors":"Domenico Albano, Umberto Viglino, Francesco Esposito, Aldo Rizzo, Carmelo Messina, Salvatore Gitto, Stefano Fusco, Francesca Serpi, Benedikt Kamp, Anja Müller-Lutz, Riccardo D'Ambrosi, Luca Maria Sconfienza, Philipp Sewerin","doi":"10.3390/tomography10070072","DOIUrl":"10.3390/tomography10070072","url":null,"abstract":"<p><p>This review examines the latest advancements in compositional and quantitative cartilage MRI techniques, addressing both their potential and challenges. The integration of these advancements promises to improve disease detection, treatment monitoring, and overall patient care. We want to highlight the pivotal task of translating these techniques into widespread clinical use, the transition of cartilage MRI from technical validation to clinical application, emphasizing its critical role in identifying early signs of degenerative and inflammatory joint diseases. Recognizing these changes early may enable informed treatment decisions, thereby facilitating personalized medicine approaches. The evolving landscape of cartilage MRI underscores its increasing importance in clinical practice, offering valuable insights for patient management and therapeutic interventions. This review aims to discuss the old evidence and new insights about the evaluation of articular cartilage through MRI, with an update on the most recent literature published on novel quantitative sequences.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11280587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762514","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}
Pub Date : 2024-06-14DOI: 10.3390/tomography10060071
Hafez Al-Momani
Background: Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.
Method: A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.
Findings: In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.
Conclusion: RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.
{"title":"A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis.","authors":"Hafez Al-Momani","doi":"10.3390/tomography10060071","DOIUrl":"10.3390/tomography10060071","url":null,"abstract":"<p><strong>Background: </strong>Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.</p><p><strong>Method: </strong>A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.</p><p><strong>Findings: </strong>In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.</p><p><strong>Conclusion: </strong>RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11209112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452075","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}
Pub Date : 2024-06-11DOI: 10.3390/tomography10060070
V. Calu, O. Enciu, E. Toma, R. Pârvuleţu, Dumitru Cătălin Pîrîianu, Adrian Miron
Cystic echinococcosis is a zoonotic parasitic disease that affects the liver in more than 70% of cases, and there is still an underestimated incidence in endemic areas. With a peculiar clinical presentation that ranges from paucisymptomatic illness to severe and possibly fatal complications, quality imaging and serological studies are required for diagnosis. The mainstay of treatment to date is surgery combined with antiparasitic agents. The surgical armamentarium consists of open and laparoscopic procedures for selected cases with growing confidence in parenchyma-sparing interventions. Endoscopic retrograde cholangiopancreatography (ERCP) is extremely useful for the diagnosis and treatment of biliary fistulas. Recent relevant studies in the literature are reviewed, and two complex cases are presented. The first patient underwent open surgery to treat 11 liver cysts, and during the follow-up, a right pulmonary cyst was diagnosed that was treated by minimally invasive surgery. The second case is represented by the peritoneal rupture of a giant liver cyst in a young woman who underwent laparoscopic surgery. Both patients developed biliary fistulas that were managed by ERCP. Both patients exhibited a non-specific clinical presentation and underwent several surgical procedures combined with antiparasitic agents, highlighting the necessity of customized treatment in order to decrease complications and successfully cure the disease.
{"title":"Complicated Liver Cystic Echinococcosis—A Comprehensive Literature Review and a Tale of Two Extreme Cases","authors":"V. Calu, O. Enciu, E. Toma, R. Pârvuleţu, Dumitru Cătălin Pîrîianu, Adrian Miron","doi":"10.3390/tomography10060070","DOIUrl":"https://doi.org/10.3390/tomography10060070","url":null,"abstract":"Cystic echinococcosis is a zoonotic parasitic disease that affects the liver in more than 70% of cases, and there is still an underestimated incidence in endemic areas. With a peculiar clinical presentation that ranges from paucisymptomatic illness to severe and possibly fatal complications, quality imaging and serological studies are required for diagnosis. The mainstay of treatment to date is surgery combined with antiparasitic agents. The surgical armamentarium consists of open and laparoscopic procedures for selected cases with growing confidence in parenchyma-sparing interventions. Endoscopic retrograde cholangiopancreatography (ERCP) is extremely useful for the diagnosis and treatment of biliary fistulas. Recent relevant studies in the literature are reviewed, and two complex cases are presented. The first patient underwent open surgery to treat 11 liver cysts, and during the follow-up, a right pulmonary cyst was diagnosed that was treated by minimally invasive surgery. The second case is represented by the peritoneal rupture of a giant liver cyst in a young woman who underwent laparoscopic surgery. Both patients developed biliary fistulas that were managed by ERCP. Both patients exhibited a non-specific clinical presentation and underwent several surgical procedures combined with antiparasitic agents, highlighting the necessity of customized treatment in order to decrease complications and successfully cure the disease.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.3390/tomography10060069
E. Quaia, Elena Kiyomi Lanza de Cristoforis, Elena Agostini, C. Zanon
Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.
{"title":"Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms","authors":"E. Quaia, Elena Kiyomi Lanza de Cristoforis, Elena Agostini, C. Zanon","doi":"10.3390/tomography10060069","DOIUrl":"https://doi.org/10.3390/tomography10060069","url":null,"abstract":"Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.3390/tomography10060068
Gabriel Solana-Lavalle, Michael D. Cusimano, Thomas Steeves, Roberto Rosas-Romero, P. N. Tyrrell
In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson’s disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson’s Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.
{"title":"Causal Forest Machine Learning Analysis of Parkinson’s Disease in Resting-State Functional Magnetic Resonance Imaging","authors":"Gabriel Solana-Lavalle, Michael D. Cusimano, Thomas Steeves, Roberto Rosas-Romero, P. N. Tyrrell","doi":"10.3390/tomography10060068","DOIUrl":"https://doi.org/10.3390/tomography10060068","url":null,"abstract":"In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson’s disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson’s Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.3390/tomography10060066
N. Quartuccio, Salvatore Ialuna, S. Pulizzi, Dante D’Oppido, Stefania Nicolosi, Antonino Maria Moreci
CAR-T-cell therapy, also referred to as chimeric antigen receptor T-cell therapy, is a novel method in the field of immunotherapy for the treatment of non-Hodgkin’s lymphoma (NHL). In patients receiving CAR-T-cell therapy, fluorodeoxyglucose Positron Emission Tomography/Computer Tomography ([18F]FDG PET/CT) plays a critical role in tracking treatment response and evaluating the immunotherapy’s overall efficacy. The aim of this study is to provide a systematic review of the literature on the studies aiming to assess and predict toxicity by means of [18F]FDG PET/CT in patients with NHL receiving CAR-T-cell therapy. PubMed/MEDLINE and Cochrane Central Register of Controlled Trials (CENTRAL) databases were interrogated by two investigators to seek studies involving the use of [18F]FDG PET/CT in patients with lymphoma undergoing CAR-T-cell therapy. The comprehensive computer literature search allowed 11 studies to be included. The risk of bias for the studies included in the systematic review was scored as low by using version 2 of the “Quality Assessment of Diagnostic Accuracy Studies” tool (QUADAS-2). The current literature emphasizes the role of [18F]FDG PET/CT in assessing and predicting toxicity in patients with NHL receiving CAR-T-cell therapy, highlighting the evolving nature of research in CAR-T-cell therapy. Additional studies are warranted to increase the collected evidence in the literature.
{"title":"The Role of [18F]FDG PET/CT in Predicting Toxicity in Patients with NHL Treated with CAR-T: A Systematic Review","authors":"N. Quartuccio, Salvatore Ialuna, S. Pulizzi, Dante D’Oppido, Stefania Nicolosi, Antonino Maria Moreci","doi":"10.3390/tomography10060066","DOIUrl":"https://doi.org/10.3390/tomography10060066","url":null,"abstract":"CAR-T-cell therapy, also referred to as chimeric antigen receptor T-cell therapy, is a novel method in the field of immunotherapy for the treatment of non-Hodgkin’s lymphoma (NHL). In patients receiving CAR-T-cell therapy, fluorodeoxyglucose Positron Emission Tomography/Computer Tomography ([18F]FDG PET/CT) plays a critical role in tracking treatment response and evaluating the immunotherapy’s overall efficacy. The aim of this study is to provide a systematic review of the literature on the studies aiming to assess and predict toxicity by means of [18F]FDG PET/CT in patients with NHL receiving CAR-T-cell therapy. PubMed/MEDLINE and Cochrane Central Register of Controlled Trials (CENTRAL) databases were interrogated by two investigators to seek studies involving the use of [18F]FDG PET/CT in patients with lymphoma undergoing CAR-T-cell therapy. The comprehensive computer literature search allowed 11 studies to be included. The risk of bias for the studies included in the systematic review was scored as low by using version 2 of the “Quality Assessment of Diagnostic Accuracy Studies” tool (QUADAS-2). The current literature emphasizes the role of [18F]FDG PET/CT in assessing and predicting toxicity in patients with NHL receiving CAR-T-cell therapy, highlighting the evolving nature of research in CAR-T-cell therapy. Additional studies are warranted to increase the collected evidence in the literature.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.3390/tomography10060067
Nerea Molina-Hernández, David Rodríguez-Sanz, J. L. Chicharro, R. Becerro-de-Bengoa-Vallejo, M. Losa-Iglesias, D. Vicente-Campos, Daniel Marugán-Rubio, Samuel Eloy Gutiérrez-Torre, César Calvo-Lobo
The aim of the present study was to determine the gender respiratory differences of bilateral diaphragm thickness, respiratory pressures, and pulmonary function in patients with low back pain (LBP). A sample of 90 participants with nonspecific LBP was recruited and matched paired by sex (45 women and 45 men). Respiratory outcomes included bilateral diaphragm thickness by ultrasonography, respiratory muscle strength by maximum inspiratory (MIP) and expiratory (MEP) pressures, and pulmonary function by forced expiratory volume during 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC spirometry parameters. The comparison of respiratory outcomes presented significant differences (p < 0.001), with a large effect size (d = 1.26–1.58) showing means differences (95% CI) for MIP of −32.26 (−42.99, −21.53) cm H2O, MEP of −50.66 (−64.08, −37.25) cm H2O, FEV1 of −0.92 (−1.18, −0.65) L, and FVC of −1.00 (−1.32, −0.69) L, with lower values for females versus males. Gender-based respiratory differences were presented for maximum respiratory pressures and pulmonary function in patients with nonspecific LBP. Women presented greater inspiratory and expiratory muscle weakness as well as worse lung function, although these differences were not linked to diaphragm thickness during normal breathing.
{"title":"A Secondary Analysis of Gender Respiratory Features for Ultrasonography Bilateral Diaphragm Thickness, Respiratory Pressures, and Pulmonary Function in Low Back Pain","authors":"Nerea Molina-Hernández, David Rodríguez-Sanz, J. L. Chicharro, R. Becerro-de-Bengoa-Vallejo, M. Losa-Iglesias, D. Vicente-Campos, Daniel Marugán-Rubio, Samuel Eloy Gutiérrez-Torre, César Calvo-Lobo","doi":"10.3390/tomography10060067","DOIUrl":"https://doi.org/10.3390/tomography10060067","url":null,"abstract":"The aim of the present study was to determine the gender respiratory differences of bilateral diaphragm thickness, respiratory pressures, and pulmonary function in patients with low back pain (LBP). A sample of 90 participants with nonspecific LBP was recruited and matched paired by sex (45 women and 45 men). Respiratory outcomes included bilateral diaphragm thickness by ultrasonography, respiratory muscle strength by maximum inspiratory (MIP) and expiratory (MEP) pressures, and pulmonary function by forced expiratory volume during 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC spirometry parameters. The comparison of respiratory outcomes presented significant differences (p < 0.001), with a large effect size (d = 1.26–1.58) showing means differences (95% CI) for MIP of −32.26 (−42.99, −21.53) cm H2O, MEP of −50.66 (−64.08, −37.25) cm H2O, FEV1 of −0.92 (−1.18, −0.65) L, and FVC of −1.00 (−1.32, −0.69) L, with lower values for females versus males. Gender-based respiratory differences were presented for maximum respiratory pressures and pulmonary function in patients with nonspecific LBP. Women presented greater inspiratory and expiratory muscle weakness as well as worse lung function, although these differences were not linked to diaphragm thickness during normal breathing.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141271478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.3390/tomography10060065
Xuesong Wen, Jianjun Li, Liyuan Yang
Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.
计算机辅助诊断系统在乳腺癌的诊断和早期检测中发挥着至关重要的作用。然而，目前大多数方法主要侧重于单侧乳房的双视角分析，从而忽略了双侧乳房 X 光照片之间潜在的宝贵信息。在本文中，我们提出了一种用于双侧乳房 X 光图像分类的四视图相关和对比联合学习网络（FV-Net）。具体来说，FV-Net 的重点是提取和匹配双侧乳房 X 光照片四个视图的特征，同时最大限度地利用它们的相似性和不相似性。通过跨乳房双通道注意模块，可实现双侧乳房 X 光照片视图之间的特征匹配，从而捕捉乳房 X 光照片之间的一致性和互补性特征，并有效减少特征错位。在由双侧乳房 X 光片得出的重组特征图中，双侧乳房 X 光片对比联合学习模块对每个局部区域内的正负样本对进行关联对比学习。这样做的目的是最大限度地提高相似局部特征之间的相关性，并增强双侧乳房 X 线照片表征中不同特征之间的区分度。我们在由 20% 的 Mini-DDSM 和 Vindr-mamo 合并数据集以及 INbreast 数据集组成的测试集上的实验结果表明，与其他竞争方法相比，我们的模型在乳腺癌分类方面表现出了卓越的性能。
{"title":"Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning","authors":"Xuesong Wen, Jianjun Li, Liyuan Yang","doi":"10.3390/tomography10060065","DOIUrl":"https://doi.org/10.3390/tomography10060065","url":null,"abstract":"Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141282216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.3390/tomography10060064
Xueke Qiu, Yang Liu, Fajin Lv
The clinical magnetic resonance scanner (field strength ≤ 3.0 T) has limited efficacy in the high-resolution imaging of experimental mice. This study introduces a novel magnetic resonance micro-coil designed to enhance the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thereby improving high-resolution imaging in experimental mice using clinical magnetic resonance scanners. Initially, a phantom was utilized to determine the maximum spatial resolution achievable by the novel micro-coil. Subsequently, 12 C57BL/6JGpt mice were included in this study, and the novel micro-coil was employed for their scanning. A clinical flexible coil was selected for comparative analysis. The scanning methodologies for both coils were consistent. The imaging clarity, noise, and artifacts produced by the two coils on mouse tissues and organs were subjectively evaluated, while the SNR and CNR of the brain, spinal cord, and liver were objectively measured. Differences in the images produced by the two coils were compared. The results indicated that the maximum spatial resolution of the novel micro-coil was 0.2 mm. Furthermore, the subjective evaluation of the images obtained using the novel micro-coil was superior to that of the flexible coil (p < 0.05). The SNR and CNR measurements for the brain, spinal cord, and liver using the novel micro-coil were significantly higher than those obtained with the flexible coil (p < 0.001). Our study suggests that the novel micro-coil is highly effective in enhancing the image quality of clinical magnetic resonance scanners in experimental mice.
{"title":"Application Value of a Novel Micro-Coil in High-Resolution Imaging of Experimental Mice Based on 3.0 T Clinical MR","authors":"Xueke Qiu, Yang Liu, Fajin Lv","doi":"10.3390/tomography10060064","DOIUrl":"https://doi.org/10.3390/tomography10060064","url":null,"abstract":"The clinical magnetic resonance scanner (field strength ≤ 3.0 T) has limited efficacy in the high-resolution imaging of experimental mice. This study introduces a novel magnetic resonance micro-coil designed to enhance the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thereby improving high-resolution imaging in experimental mice using clinical magnetic resonance scanners. Initially, a phantom was utilized to determine the maximum spatial resolution achievable by the novel micro-coil. Subsequently, 12 C57BL/6JGpt mice were included in this study, and the novel micro-coil was employed for their scanning. A clinical flexible coil was selected for comparative analysis. The scanning methodologies for both coils were consistent. The imaging clarity, noise, and artifacts produced by the two coils on mouse tissues and organs were subjectively evaluated, while the SNR and CNR of the brain, spinal cord, and liver were objectively measured. Differences in the images produced by the two coils were compared. The results indicated that the maximum spatial resolution of the novel micro-coil was 0.2 mm. Furthermore, the subjective evaluation of the images obtained using the novel micro-coil was superior to that of the flexible coil (p < 0.05). The SNR and CNR measurements for the brain, spinal cord, and liver using the novel micro-coil were significantly higher than those obtained with the flexible coil (p < 0.001). Our study suggests that the novel micro-coil is highly effective in enhancing the image quality of clinical magnetic resonance scanners in experimental mice.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.3390/tomography10060063
Lorenzo Barbarossa, M. D’Onghia, A. Cartocci, M. Suppa, L. Tognetti, S. Cappilli, Ketty Peris, J. Pérez-Anker, J. Malvehy, G. Baldino, Caterina Militello, Jean-Luc Perrot, P. Rubegni, E. Cinotti
Basal cell carcinoma (BCC) is the most frequent malignancy in the general population. To date, dermoscopy is considered a key tool for the diagnosis of BCC; nevertheless, line-field confocal optical coherence tomography (LC-OCT), a new non-invasive optical technique, has become increasingly important in clinical practice, allowing for in vivo imaging at cellular resolution. The present study aimed to investigate the possible correlation between the dermoscopic features of BCC and their LC-OCT counterparts. In total, 100 histopathologically confirmed BCC cases were collected at the Dermatologic Clinic of the University of Siena, Italy. Predefined dermoscopic and LC-OCT criteria were retrospectively evaluated, and their frequencies were calculated. The mean (SD) age of our cohort was 65.46 (13.36) years. Overall, BCC lesions were mainly located on the head (49%), and they were predominantly dermoscopically pigmented (59%). Interestingly, all dermoscopic features considered had a statistically significant agreement with the LC-OCT criteria (all p < 0.05). In conclusion, our results showed that dermoscopic patterns may be associated with LC-OCT findings, potentially increasing accuracy in BCC diagnosis. However, further studies are needed in this field.
{"title":"Understanding the Dermoscopic Patterns of Basal Cell Carcinoma Using Line-Field Confocal Tomography","authors":"Lorenzo Barbarossa, M. D’Onghia, A. Cartocci, M. Suppa, L. Tognetti, S. Cappilli, Ketty Peris, J. Pérez-Anker, J. Malvehy, G. Baldino, Caterina Militello, Jean-Luc Perrot, P. Rubegni, E. Cinotti","doi":"10.3390/tomography10060063","DOIUrl":"https://doi.org/10.3390/tomography10060063","url":null,"abstract":"Basal cell carcinoma (BCC) is the most frequent malignancy in the general population. To date, dermoscopy is considered a key tool for the diagnosis of BCC; nevertheless, line-field confocal optical coherence tomography (LC-OCT), a new non-invasive optical technique, has become increasingly important in clinical practice, allowing for in vivo imaging at cellular resolution. The present study aimed to investigate the possible correlation between the dermoscopic features of BCC and their LC-OCT counterparts. In total, 100 histopathologically confirmed BCC cases were collected at the Dermatologic Clinic of the University of Siena, Italy. Predefined dermoscopic and LC-OCT criteria were retrospectively evaluated, and their frequencies were calculated. The mean (SD) age of our cohort was 65.46 (13.36) years. Overall, BCC lesions were mainly located on the head (49%), and they were predominantly dermoscopically pigmented (59%). Interestingly, all dermoscopic features considered had a statistically significant agreement with the LC-OCT criteria (all p < 0.05). In conclusion, our results showed that dermoscopic patterns may be associated with LC-OCT findings, potentially increasing accuracy in BCC diagnosis. However, further studies are needed in this field.","PeriodicalId":51330,"journal":{"name":"Tomography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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