Background: Accelerated magnetic resonance imaging sequences reconstructed using the vendor-provided Recon deep learning algorithm (DL-MRI) were found to be more likely than conventional magnetic resonance imaging (MRI) sequences to detect subacromial (SbA) bursal thickening. However, the extent of this thickening was not extensively explored. This study aimed to compare the image quality of DL-MRI with conventional MRI sequences reconstructed via conventional pipelines (Conventional-MRI) for shoulder examinations and evaluate the effectiveness of DL-MRI in accurately demonstrating the degree of SbA bursal and subcoracoid (SC) bursal thickening.
Methods: This prospective cross-sectional study enrolled 41 patients with chronic shoulder pain who underwent 3-T MRI (including both Conventional-MRI and accelerated MRI sequences) of the shoulder between December 2022 and April 2023. Each protocol consisted of oblique axial, coronal, and sagittal images, including proton density-weighted imaging (PDWI) with fat suppression (FS) and oblique coronal T1-weighted imaging (T1WI) with FS. The image quality and degree of artifacts were assessed using a 5-point Likert scale for both Conventional-MRI and DL-MRI. Additionally, the degree of SbA and SC bursal thickening, as well as the relative signal-to-noise ratio (rSNR) and relative contrast-to-noise ratio (rCNR) were analyzed using the paired Wilcoxon test. Statistical significance was defined as P<0.05.
Results: The utilization of accelerated sequences resulted in a remarkable 54.7% reduction in total scan time. Overall, DL-MRI exhibited superior image quality scores and fewer artifacts compared to Conventional-MRI. Specifically, DL-MRI demonstrated significantly higher measurements of SC bursal thickenings in the oblique sagittal PDWI sequence compared to Conventional-MRI [3.92 (2.83, 5.82) 3.74 (2.92, 5.96) mm, P=0.028]. Moreover, DL-MRI exhibited higher detection of SbA bursal thickenings in the oblique coronal PDWI sequence [2.61 (1.85, 3.46) 2.48 (1.84, 3.25) mm], with a notable trend towards significant differences (P=0.071). Furthermore, the rSNRs of the muscle in DL-MRI images were significantly higher than those in Conventional-MRI images across most sequences (P<0.001). However, the rSNRs of bone on Conventional-MRI of oblique axial and oblique coronal PDWI sequences showed adverse results [oblique axial: 1.000 (1.000, 1.000) 0.444 (0.367, 0.523); and oblique coronal: 1.000 (1.000, 1.000) 0.460 (0.387, 0.631); all P<0.001]. Additionally, all DL-MRI images exhibited significantly greater rSNRs and rCNRs compared to accelerated MRI sequences reconstructed using traditional pipelines (P<0.001).
Conclusions: In conclusion, the utilization of DL-MRI enhances image quality and improves diagnostic capabilities, making it a promising alternative to Conventional-MRI for shoulder imaging.
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http://dx.doi.org/10.21037/qims-23-1412 | DOI Listing |
Magn Reson Med
December 2024
Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands.
Purpose: To implement a low-rank and subspace model-based reconstruction for 3D deuterium metabolic imaging (DMI) and compare its performance against Fourier transform-based (FFT) reconstruction in terms of spectral fitting reliability.
Methods: Both reconstruction methods were applied on simulated and experimental DMI data. Numerical simulations were performed to evaluate the effect of increasing acceleration factors.
BMC Cancer
December 2024
Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 45008, China.
Background: It has been proposed that risk model-based strategies could serve as viable alternatives to traditional risk factor-based approaches in lung cancer screening; however, there has been no systematic discussion. In this review, we provide an overview of the benefits, harms, and cost-effectiveness of these two strategies in lung cancer screening application, as well as discussing possible future research directions.
Methods: Following the PRISMA guidelines, a comprehensive literature search was conducted across PubMed, Web of Science, Cochrane libraries, and EMBASE from January 1994 to April 2024.
Zhonghua Wei Chang Wai Ke Za Zhi
December 2024
Chronic constipation is a common functional bowel disease, and its diagnosis is based on history and physical examination. Laboratory examination is important for determining the cause, type, severity and treatment effect of chronic constipation. At present, the commonly used workup of chronic constipation includes X-ray, magnetic resonance, pelvic floor ultrasound, neuroelectrophysiology and colorectal manometry, etc.
View Article and Find Full Text PDFComput Biol Med
December 2024
Gandong University, Fuzhou, Jiangxi, 344000, China.
MRI-CT image fusion technology combines magnetic resonance imaging (MRI) and computed tomography (CT) imaging to provide more comprehensive and accurate image information. This fusion technology can play an important role in medical diagnosis and surgical planning. However, there are several issues with current MRI-CT image fusion, such as the presence of artifacts in both MRI and CT images, which may affect the quality and accuracy of the images during the fusion process.
View Article and Find Full Text PDFTalanta
December 2024
State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan, 030006, China; Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, 030006, China.
The combined application of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF) has achieved remarkable results in coal quality analysis by leveraging NIRS's sensitivity to organic compounds and XRF's reliability for inorganic composition. However, variations in particle size distribution negatively affect the diffuse reflectance of NIRS and the fluorescence signal intensities of XRF, leading to decreased accuracy and repeatability in predictions. To address this issue, this study innovatively proposes a particle size correction method that integrates image processing and deep learning.
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