Purpose: To evaluate potential clinical acceleration factors of Compressed SENSE (CS) in direct comparison with SENSE for fat saturated (fs), proton density-weighted (PD) 2D and 3D sequences of the knee.
Method: Twenty healthy volunteers were scanned with a 3 T scanner, all receiving a standard, fs 2D PD, three CS (CS 2, CS 3, CS 5) as well as time-equivalent SENSE accelerations (S 2, S 3, S 5). The fs 3D PD sequence was acquired with four CS (CS 6, CS 8, CS 10, CS 15) and equivalent SENSE (S 5.72, S 7.69, S 9.57, S 14) factors. Three independent readers rated the images. Signal-to-noise, contrast-to-noise, root-mean-square error and structural similarity index were analyzed for objective evaluation.
Results: Scan time decreased with increasing CS factor (2D CS 2: 145 s, 2D CS 3: 95 s, 2D CS 5: 57 s, 3D CS 6: 293 s, 3D CS 8: 220 s, 3D CS 10: 176 s, 3D CS 15: 119 s). The 2D standard sequence was rated best for diagnostic certainty and overall image impression with an average of 4.97 ± 0.10 and 4.80 ± 0.24 (all p < 0.05), except for 2D CS 2 and 2D S 2. For the 3D sequences, the standard sequence performed better for both parameters for CS 15, S 9.57 and S 4, as well as S 7.69 for overall image impression while CS 8 was non-inferior for all tested criteria and CS 10 only inferior for delineation of the anterior cruciate ligament, both outperforming the time-equivalent SENSE accelerations.
Conclusion: Compressed SENSE can significantly decrease (34.39 % for 2D CS 2 and 54.17 % for 3D CS 10) scan time in knee imaging with unchanged diagnostic certainty and overall image impression compared to the clinical reference.
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http://dx.doi.org/10.1016/j.ejrad.2020.109273 | DOI Listing |
Radiat Oncol
January 2025
German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
Background: For radiotherapy of head and neck cancer (HNC) magnetic resonance imaging (MRI) plays a pivotal role due to its high soft tissue contrast. Moreover, it offers the potential to acquire functional information through diffusion weighted imaging (DWI) with the potential to personalize treatment. The aim of this study was to acquire repetitive DWI during the course of online adaptive radiotherapy on an 1.
View Article and Find Full Text PDFRadiol Artif Intell
January 2025
Human Phenome Institute and Shanghai Pudong Hospital, Fudan University, Shanghai, China.
. The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each one covers standardized and commonly used clinical protocols. ©RSNA, 2025.
View Article and Find Full Text PDFPurpose: To propose a domain-conditioned and temporal-guided diffusion modeling method, termed dynamic Diffusion Modeling (dDiMo), for accelerated dynamic MRI reconstruction, enabling diffusion process to characterize spatiotemporal information for time-resolved multi-coil Cartesian and non-Cartesian data.
Methods: The dDiMo framework integrates temporal information from time-resolved dimensions, allowing for the concurrent capture of intra-frame spatial features and inter-frame temporal dynamics in diffusion modeling. It employs additional spatiotemporal ($x$-$t$) and self-consistent frequency-temporal ($k$-$t$) priors to guide the diffusion process.
Medical imaging systems are commonly assessed and optimized by the use of objective measures of image quality (IQ). The performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016 (S.S.W., J.V., R.K., E.H.P., J.F.); Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, University Hospital Tübingen, Tübingen, Germany (S.S.W.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V.); Department of Radiology, Hospital do Coraçao, São Paulo, Brazil (T.C.R.); Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), London, United Kingdom (D.D.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (B.F.); Department of Radiology, Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Medscanlagos Radiology, Cabo Frio, Brazil (A.S.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Siemens Healthineers AG, Erlangen, Germany (I.B.); and Siemens Medical Solutions USA, Malvern, Pa (G.K.).
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022.
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