Radiology advances最新文献

{"title":"CT trachea surface roughness is associated with chronic obstructive pulmonary disease symptoms.","authors":"Jason T Bartlett, James C Hogg, Jean Bourbeau, Wan C Tan, Miranda Kirby","doi":"10.1093/radadv/umae002","DOIUrl":"10.1093/radadv/umae002","url":null,"abstract":"<p><strong>Background: </strong>Trachea structural abnormalities occur in patients with chronic obstructive pulmonary disease (COPD), yet there are few methods for quantifying trachea surface topology.</p><p><strong>Purpose: </strong>To develop a method to quantify trachea surface roughness on CT imaging and investigate the association with airflow limitation and symptoms in COPD.</p><p><strong>Materials and methods: </strong>Participants from the multicenter prospective Canadian Cohort Obstructive Lung Disease study between 2009 and 2015 underwent CT imaging and analysis. Established CT measurements included: tracheal index (TI), defined as the smallest ratio of coronal-to-sagittal trachea diameter, low attenuation areas below -950 HU, and wall thickness of a theoretical 10-mm airway. Trachea surface roughness shape (SR_S) was calculated as the percent fraction of the measurement box filled by the surface mesh. Multivariable regression models were used to determine association for CT measurements with forced expiratory volume in 1 second (FEV₁) and forced vital capacity (FVC), and Medical Research Council dyspnea scale (MRC)≥3, adjusting for covariates.</p><p><strong>Results: </strong>A total of 1253 participants (mean age, 66 ± 10 years; 727 men) from 9 centers were investigated: <i>n</i> = 267 never smokers, <i>n</i> = 369 ever smokers, <i>n</i> = 352 mild COPD, and <i>n</i> = 265 moderate-to-severe COPD. There were no differences between groups for age or race (<i>P</i> < .05). In models including SR_S and TI, a 1-standard deviation (SD) increase in SR_S was independently associated with a 0.11-SD decrease in FEV₁ (β = -0.11; <i>P</i> < .001) and a 0.16-SD decrease in FEV₁/FVC (β = -0.16; <i>P</i> < .001); a 1-point increase in SR_S was associated with a 13% increased likelihood of MRC ≥ 3 (odds ratio = 1.13; <i>P</i> = .003). In models including SR_S, low attenuation areas below -950 HU and wall thickness of a theoretical 10-mm airway, a 1-SD increase in SR_S was associated with a 0.21-SD decrease in FEV₁ (β = -0.21; <i>P</i> < .001) and a 0.13-SD decrease in FEV₁/FVC (β = -0.13; <i>P</i> < .001); a 1-point increase in SR_S was associated with a 12% increased likelihood of MRC ≥ 3 (odds ratio = 1.12; <i>P</i> = .006).</p><p><strong>Conclusion: </strong>Increased trachea surface shape roughness is independently associated with worse airflow and increased symptom burden in COPD.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 1","pages":"umae002"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seven tesla knee MRI T2*-mapping detects intrasubstance meniscus degeneration in patients with posterior root tears.","authors":"Abdul Wahed Kajabi, Štefan Zbýň, Jesse S Smith, Eisa Hedayati, Karsten Knutsen, Luke V Tollefson, Morgan Homan, Hasan Abbasguliyev, Takashi Takahashi, Gregor J Metzger, Robert F LaPrade, Jutta M Ellermann","doi":"10.1093/radadv/umae005","DOIUrl":"10.1093/radadv/umae005","url":null,"abstract":"<p><strong>Background: </strong>Medial meniscus root tears often lead to knee osteoarthritis. The extent of meniscal tissue changes beyond the localized root tear is unknown.</p><p><strong>Purpose: </strong>To evaluate if 7 Tesla 3D T2*-mapping can detect intrasubstance meniscal degeneration in patients with arthroscopically verified medial meniscus posterior root tears (MMPRTs), and assess if tissue changes extend beyond the immediate site of the posterior root tear detected on surface examination by arthroscopy.</p><p><strong>Methods: </strong>In this prospective study we acquired 7 T knee MRIs from patients with MMPRTs and asymptomatic controls. Using a linear mixed model, we compared T2* values between patients and controls, and across different meniscal regions. Patients underwent arthroscopic assessment before MMPRT repair. Changes in pain levels before and after repair were calculated using Knee Injury & Osteoarthritis Outcome Score (KOOS). Pain changes and meniscal extrusion were correlated with T2* using Pearson correlation (<i>r</i>).</p><p><strong>Results: </strong>Twenty patients (mean age 53 ± 8; 16 females) demonstrated significantly higher T2* values across the medial meniscus (anterior horn, posterior body and posterior horn: all <i>P </i><<i> </i>.001; anterior body: <i>P </i>=<i> </i>.007), and lateral meniscus anterior (<i>P </i>=<i> </i>.024) and posterior (<i>P </i><<i> </i>.001) horns when compared to the corresponding regions in ten matched controls (mean age 53 ± 12; 8 females). Elevated T2* values were inversely correlated with the change in pain levels before and after repair. All patients had medial meniscal extrusion of ≥2 mm. Arthroscopy did not reveal surface abnormalities in 70% of patients (14 out of 20).</p><p><strong>Conclusions: </strong>Elevated T2* values across both medial and lateral menisci indicate that degenerative changes in patients with MMPRTs extend beyond the immediate vicinity of the posterior root tear. This suggests more widespread meniscal degeneration, often undetected by surface examinations in arthroscopy.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 1","pages":"umae005"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11159571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Super-resolution deep learning reconstruction to improve image quality of coronary CT angiography.","authors":"Nobuo Tomizawa, Yui Nozaki, Hideyuki Sato, Yuko Kawaguchi, Ayako Kudo, Daigo Takahashi, Kazuhisa Takamura, Makoto Hiki, Shinichiro Fujimoto, Iwao Okai, Seiji Koga, Shinya Okazaki, Kanako K Kumamaru, Tohru Minamino, Shigeki Aoki","doi":"10.1093/radadv/umae001","DOIUrl":"10.1093/radadv/umae001","url":null,"abstract":"<p><strong>Purpose: </strong>To compare the objective and subjective image quality and diagnostic performance for coronary stenosis of normal-dose model-based iterative reconstruction and reduced-dose super-resolution deep learning reconstruction in coronary CT angiography.</p><p><strong>Materials and methods: </strong>This single-center retrospective study included 52 patients (mean age, 68 years ± 10 [SD]; 41 men) who underwent serial coronary CT angiography and subsequent invasive coronary angiography between January and November 2022. The first 25 patients were scanned with a standard dose using model-based iterative reconstruction. The last 27 patients were scanned with a reduced dose using super-resolution deep learning reconstruction. Per-patient objective and subjective image qualities were compared. Diagnostic performance of model-based iterative reconstruction and super-resolution deep learning reconstruction to diagnose significant stenosis on coronary angiography was compared per-vessel using receiver operating characteristics curve analysis.</p><p><strong>Results: </strong>The median tube current of super-resolution deep learning reconstruction was lower than that of model-based iterative reconstruction (median [IQR], 890 mA [680, 900] vs. 900 mA [895, 900], <i>P</i> = 0.03). Image noise of super-resolution deep learning reconstruction was lower than that of model-based iterative reconstruction (14.6 Hounsfield units ± 1.3 vs. 22.7 Hounsfield units ± 4.4, <i>P</i> < .001). Super-resolution deep learning reconstruction improved the overall subjective image quality compared with model-based iterative reconstruction (median [IQR], 4 [3, 4] vs 3 [3, 3], <i>P</i> = .006). No difference in the area under the receiver operating characteristic curve in diagnosing coronary stenosis using super-resolution deep learning reconstruction (0.96; 95% CI, 0.92-0.99) and model-based iterative reconstruction (0.96; 95% CI, 0.92-0.98; <i>P</i> = .98) was observed.</p><p><strong>Conclusion: </strong>Our exploratory analysis suggests that super-resolution deep learning reconstruction could improve image quality with lower tube current settings than model-based iterative reconstruction with similar diagnostic performance to diagnose coronary stenosis in coronary CT angiography.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 1","pages":"umae001"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12428329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI to identify radiographic features of pulmonary edema.","authors":"Viacheslav V Danilov, Anton O Makoveev, Alex Proutski, Irina Ryndova, Alex Karpovsky, Yuriy Gankin","doi":"10.1093/radadv/umae003","DOIUrl":"10.1093/radadv/umae003","url":null,"abstract":"<p><strong>Background: </strong>Pulmonary edema is a leading cause for requiring hospitalization in patients with congestive heart failure. Assessing the severity of this condition with radiological imaging becomes paramount in determining the optimal course of patient care.</p><p><strong>Purpose: </strong>This study aimed to develop a deep learning methodology for the identification of radiographic features associated with pulmonary edema.</p><p><strong>Materials and methods: </strong>This retrospective study used a dataset from the Medical Information Mart for Intensive Care database comprising 1000 chest radiograph images from 741 patients with suspected pulmonary edema. The images were annotated by an experienced radiologist, who labeled radiographic manifestations of cephalization, Kerley lines, pleural effusion, bat wings, and infiltrate features of edema. The proposed methodology involves 2 consecutive stages: lung segmentation and edema feature localization. The segmentation stage is implemented using an ensemble of 3 networks. In the subsequent localization stage, we evaluated 8 object detection networks, assessing their performance with average precision (AP) and mean AP.</p><p><strong>Results: </strong>Effusion, infiltrate, and bat wing features were best detected by the Side-Aware Boundary Localization (SABL) network with corresponding APs of 0.599, 0.395, and 0.926, respectively. Furthermore, SABL achieved the highest overall mean AP of 0.568. The Cascade Region Proposal Network network attained the highest AP of 0.417 for Kerley lines and the Probabilistic Anchor Assignment network achieved the highest AP of 0.533 for cephalization.</p><p><strong>Conclusion: </strong>The proposed methodology, with the application of SABL, Cascade Region Proposal Network, and Probabilistic Anchor Assignment detection networks, is accurate and efficient in localizing and identifying pulmonary edema features and is therefore a promising diagnostic candidate for interpretable severity assessment of pulmonary edema.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 1","pages":"umae003"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatGPT-4: a breakthrough in ultrasound image analysis.","authors":"Laith R Sultan, Mohamed Kh Mohamed, Savvas Andronikou","doi":"10.1093/radadv/umae006","DOIUrl":"10.1093/radadv/umae006","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 1","pages":"umae006"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Radiology Advances</i> offers reliable, accessible, and timely science.","authors":"Susanna I Lee","doi":"10.1093/radadv/umae004","DOIUrl":"10.1093/radadv/umae004","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 1","pages":"umae004"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}