Purpose: The purpose of this work is to compare the behavior of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in contrast-enhanced MR angiography with background suppression performed by either a Dixon-type or subtraction-type method.
Theory And Methods: Theoretical expressions for the SNR and CNR for both background suppression techniques were derived. The theoretical Dixon:subtraction SNR and CNR ratios were compared to empirical ratios measured from phantom and in vivo studies for Dixon techniques utilizing one, two, and three echoes. Specifically, the SNR and CNR ratios were compared as the concentration of contrast material in the blood changed.
Results: Empirical measurements of the SNR and CNR ratios compared favorably with the ratios predicted by theory. As the contrast concentration was reduced, the SNR advantage of the Dixon techniques increased asymptotically. In the ideal case, the SNR improvement over subtraction contrast-enhanced MR angiography was at least twofold for one- and two-echo Dixon techniques and at least a factor of 6 for the three-echo Dixon technique.
Conclusion: Expressions showing a contrast concentration-dependent SNR and CNR improvement of at least a factor of two when Dixon-type contrast-enhanced MR angiography is used in place of subtraction-type contrast-enhanced MR angiography were derived and validated with phantom and in vivo experiments. Magn Reson Med 74:81-92, 2015. © 2014 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/mrm.25374 | DOI Listing |
Magn Reson Imaging
January 2025
Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang Uygur Autonomous Region, China. Electronic address:
3D BFFE and TRANCE can provide a visualization of pulmonary vessels without injection of contrast agent. T-SLIP can observe a large area of vessels using an arterial spin labeling technique. 3D BFFE and TRANCE with two T-SLIP placement strategies were compared on pulmonary artery imaging.
View Article and Find Full Text PDFKorean J Radiol
January 2025
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI.
Materials And Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.
Korean J Radiol
January 2025
Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T2-w FSE Dixon sequence (T2) for breast magnetic resonance imaging (MRI).
Materials And Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2 and T2 sequences were acquired for each patient.
AJNR Am J Neuroradiol
January 2025
Department of Radiology (M.Z., N.W., S.H., X.L., H.Z., C.Y., Q.S.), The First Affiliated Hospital of Dalian Medical University, Dalian, China
Background And Purpose: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany.
Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction-volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20-24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30-100%), and FBP.
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