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Lessons Learned in Emergency Pelvic and First-Trimester US: Focus on Cognitive Biases.
Radiographics
February 2025
From the Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215.
Nonpregnant and pregnant women who present with acute pelvic pain can pose a diagnostic challenge in the emergency setting. The clinical presentation is often nonspecific, and the differential diagnosis may be very broad. These symptoms are often indications for pelvic US, which is the primary imaging modality when an obstetric or gynecologic cause is suspected.
View Article and Find Full Text PDFMRI-assessed Dynamic Hyperinflation Induced by Tachypnea in Chronic Obstructive Pulmonary Disease: The SPIROMICS-HF Study.
Radiol Cardiothorac Imaging
February 2025
From the Department of Biomedical Engineering (X.Z.) and Columbia Magnetic Resonance Research Center (CMRRC) (W.S.), Columbia University, New York, NY; Departments of Medicine (C.B.C., J.P.F.) and Radiology (J.P.F.), University of California at Los Angeles, Los Angeles, Calif; Department of Radiology, Weill Cornell Medicine, New York, NY (M.R.P.); Department of Radiology (M.R.P., S.M.D., S.J.), Department of Medicine (M.C.B., R.G.B.), Department of Epidemiology (R.G.B.), Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics (W.S.), and Institute of Human Nutrition (W.S.), Columbia University Irving Medical Center, 632 W 168th St, PH-17, New York, NY 10032; Department of Radiology (B.A.V., J.A.C.L.) and Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine (N.N.H.), Johns Hopkins University, Baltimore, Md; Department of Radiology, University of Michigan, Ann Arbor, Mich (P.P.A.); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wis (D.A.B.); Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC (D.C.); Departments of Radiology, Medicine, and the Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa (E.A.H.); Sections on Cardiology and Geriatrics, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (D.W.K.); Division of Pulmonary, Critical Care, Sleep, and Allergy (J.A.K.) and Department of Radiology, College of Medicine (M.G.M.), University of Illinois at Chicago, Chicago, Ill; Department of Radiology and Biomedical Imaging (Y.J.L., J.L.), Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, School of Medicine (P.G.W.), and Cardiovascular Research Institute (P.G.W.), University of California at San Francisco, San Francisco, Calif; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Wake Forest University, Winston-Salem, NC (J.O., S.P.P.); Division of Pulmonary Medicine, Department of Medicine, Mayo Clinic, Phoenix, Ariz (V.E.O.); Department of Medicine, University of Utah, Salt Lake City, Utah (R.P.); Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.S.); Department of Radiology, Hannover Medical School, Hannover, Germany (J.V.C.); and BREATH, Member of the German Center for Lung Research (DZL), Hannover, Germany (J.V.C.).
Purpose To assess the repeatability of real-time cine pulmonary MRI measures of metronome-paced tachypnea (MPT)-induced dynamic hyperinflation and its relationship with chronic obstructive pulmonary disease (COPD) severity. Materials and Methods SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) (ClinicalTrials.gov identifier no.
View Article and Find Full Text PDFNNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High- Resolution Short Echo Time MR Spectroscopy Datasets.
Radiol Artif Intell
January 2025
From the Department of Radiation Oncology (A.S.G., V.H., H.S.) and Department of Radiology and Imaging Sciences (B.D.W.), Emory University School of Medicine, 1701 Uppergate Dr, C5008 Winship Cancer Institute, Atlanta, GA 30322; Department of Radiology, University of Miami {School of Medicine?}, Miami, Fla (S.S., A.A.M.); Department of {Radiology?} Northwestern University {Feinberg School of Medicine?}, Chicago, Ill (L.A.D.C.); Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, Ga (Y.L.); Department of Psychology, Emory University, Atlanta, Ga (M.T.); and Department of Radiology, Duke University Medical Center, Durham, NC (B.J.S.).
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep-learning method for quantification of high-resolution short echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/GRAPPA scans from clinical trials for glioblastoma (Trial 1, May 2014-October 2018) and major-depressive-disorder (Trial 2, 2022- 2023). The training dataset included 685k spectra from 20 participants (60 scans) in Trial 1.
View Article and Find Full Text PDFPosttraining Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.
Radiol Artif Intell
January 2025
From the Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15,81377 Munich, Germany (T.W., J.D., M.I.); Department of Statistics, LMU Munich, Munich, Germany (T.W., D.R.); and Munich Center for Machine Learning, Munich, Germany (T.W., J.D., D.R., M.I.).
Purpose To investigate whether the computational effort of 3D CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, Tucker decomposition was applied to the convolutional kernels of the TotalSegmentator model, an nnU-Net model trained on a comprehensive CT dataset for automatic segmentation of 117 anatomic structures. The proposed approach reduced the floating-point operations (FLOPs) and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality.
View Article and Find Full Text PDFA Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.
Radiol Artif Intell
January 2025
From the Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou 510060, P. R. China (J.K., C.F.W., Z.H.C., G.Q.Z., Y.Q.W., L.L., Y.S.); Department of Radiation Therapy, Nanhai People's Hospital, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China (J.Y.P., L.J.L.); and Department of Electronic Engineering, Information School, Yunnan University, Kunming, China (W.B.L.).
Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally- advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This multicenter retrospective study included 1039 LA-NPC patients (779 male, 260 female, mean age 44 [standard deviation: 11]) diagnosed between April 2009 and December 2015. A radiomics- clinical prognostic model (Model RC) was developed using pre-and post-IC MRI and other clinical factors using graph convolutional neural networks (GCN).
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