Purpose: To evaluate the clinical application of the accelerated 3D T1-weighted turbo field echo (T1W-TFE) using the compressed sensing-sensitivity encoding (CS-SENSE) and identify the appropriate acceleration factor.
Methods: 33 healthy controls (HC), 10 multiple sclerosis (MS) and 10 Alzheimer's disease (AD) patients were prospectively recruited. A conventional 3D T1W-TFE sequence and accelerated sequences with CS-SENSE factors of 3, 4.5, 6 and with SENSE factors of 3, 4.5 were acquired for all participants on a 3.0T MR system. The visual evaluation was independently assessed by two experienced radiologists. Quantitative image quality metrics and intraclass correlation coefficients (ICCs) between the conventional and the accelerated sequences were performed at the voxel level. Group comparisons were performed between HC and AD or MS patients.
Results: There were no significant differences in the visual image quality metrics between conventional sequence and CS-SENSE factor of 3. The sequences with CS-SENSE factor of 6 and SENSE factors of 3, 4.5 showed significantly decreased overall image quality. The ICC values based on the voxel level of each accelerated scan and conventional scan were high (>0.9, 85.2%). For different accelerated sequences, AD and MS patients showed consistent results with the conventional sequence in gray matter atrophy when compared to HC.
Conclusions: CS-SENSE factor of 3 is the appropriate parameter to accelerate the 3D T1W-TFE (65% time reduction) with preserved visual image quality. The voxel-based analysis demonstrated high ICCs for brain volume measurements in the majority of brain regions, implying the feasibility of the accelerated technique.
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http://dx.doi.org/10.1016/j.ejrad.2020.109255 | DOI Listing |
Radiat Prot Dosimetry
January 2025
Medical Physics, Ghent University, Proeftuinstraat 86, 9000 Ghent, Belgium.
Quality control (QC) of personal radiation protective equipment (PRPE) is essential to detect tears and holes in the attenuating layers. Routinely, this QC is performed using fluoroscopy on a conventional X-ray table. However, such a QC procedure is laborious and time consuming.
View Article and Find Full Text PDFJ Comput Assist Tomogr
November 2024
From the Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
Objective: This preliminary study aims to assess the image quality of enhanced-resolution deep learning reconstruction (ER-DLR) in magnetic resonance cholangiopancreatography (MRCP) and compare it with non-ER-DLR MRCP images.
Methods: Our retrospective study incorporated 34 patients diagnosed with biliary and pancreatic disorders. We obtained MRCP images using a single breath-hold MRCP on a 3T MRI system.
J Comput Assist Tomogr
November 2024
From the Department of Radiology and Radiological Science, Divisions of Cardiovascular and Thoracic Imaging, Medical University of South Carolina. Charleston, SC.
Background: The latest generation of computed tomography (CT) systems based on photon-counting detector promises significant improvements in several clinical applications, including chest imaging.
Purpose: The aim of the study is to evaluate the image quality of ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung using four sharp reconstruction kernels.
Material And Methods: This retrospective study included 25 patients (11 women and 14 men; median age, 71 years) who underwent unenhanced chest CT from April to May 2023.
J Comput Assist Tomogr
November 2024
From the Carl E. Ravin Advanced Imaging Labs, Center for Virtual Imaging Trials, Department of Radiology.
Objective: Patient characteristics, iodine injection, and scanning parameters can impact the quality and consistency of contrast enhancement of hepatic parenchyma in CT imaging. Improving the consistency and adequacy of contrast enhancement can enhance diagnostic accuracy and reduce clinical practice variability, with added positive implications for safety and cost-effectiveness in the use of contrast medium. We developed a clinical tool that uses patient attributes (height, weight, sex, age) to predict hepatic enhancement and suggest alternative injection/scanning parameters to optimize the procedure.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Objective: This study aimed to assess the feasibility of the deep learning in generating T2 weighted (T2W) images from diffusion-weighted imaging b0 images.
Materials And Methods: This retrospective study included 53 patients who underwent head magnetic resonance imaging between September 1 and September 4, 2023. Each b0 image was matched with a corresponding T2-weighted image.
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