Purpose: To evaluate the feasibility of single breath-hold, multiarterial MRI of the liver using the THRIVE-CENTRA-keyhole technique.
Materials And Methods: Twenty-eight patients with 63 focal hepatic lesions underwent liver MR examinations that included the three-dimensional THRIVE-CENTRA-keyhole sequence. Three or six phases were obtained for arterial phase scanning during a single breath-hold. Central k-space data were collected for each phase but the remaining peripheral k-space data were collected only once. The enhancement pattern of each hepatic lesion was analyzed according to the specific diagnosis.
Results: Hepatocellular carcinomas (n = 24) enhancement patterns included: rim enhancing (n = 9), homogeneous (n = 7), nodule-in-nodule (n = 5), or heterogeneous (n = 3). A late peritumoral rim was observed in four (17%) of the hepatocellular carcinomas. Most metastases (17 of 18; 94%) demonstrated peripheral rim enhancement. The progressive centripetal enhancement of hemangiomas (n = 6) was clearly depicted. Focal nodular hyperplasia (n = 4) showed early homogeneous enhancement and one lesion demonstrated a central scar.
Conclusion: The THRIVE-CENTRA-keyhole technique can be used to acquire single breath-hold, multiarterial images depicting improved enhancement characteristics of focal hepatic lesions. This technique will allow accurate timing of arterial scanning with 3D acquisition and high temporal resolution.
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http://dx.doi.org/10.1002/jmri.21442 | DOI Listing |
Purpose: T1-weighted signal intensity ratios (SIR) comparing pancreas to spleen (SIRps) or muscle (SIRpm) can semiquantitatively assess T1 signal change associated with pancreatitis. However, there is no standardized methodology for generating these ratios. We set out to determine the impact of MRI sequence as well as region of interest (ROI) location, shape, and size on T1 SIR.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Radiation Oncology, Miami Cancer Institute, Miami, FL 33176, USA.
: Over the past decade, significant advances have been made in image-guided radiotherapy (RT) particularly with the introduction of magnetic resonance (MR)-guided radiotherapy (MRgRT). However, the optimal clinical applications of MRgRT are still evolving. The intent of this analysis was to describe our institutional MRgRT utilization patterns and evolution therein, specifically as an early adopter within a center endowed with multiple other technology platforms.
View Article and Find Full Text PDFCurr Oncol
January 2025
Department of Radiology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality and accelerate imaging acquisition by using single-breath-hold T2-weighted HASTE with deep learning (DL) reconstruction (DL-HASTE) with a 3 mm slice thickness. MRI of the upper abdomen with DL-HASTE was performed in 35 participants (5 healthy volunteers and 30 patients) at 3 Tesla.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology, Geneva University Hospitals, Geneva, Switzerland.
Objectives: Evaluating the impact of an AI-based automated cardiac MRI (CMR) planning software on procedure errors and scan times compared to manual planning alone.
Material And Methods: Consecutive patients undergoing non-stress CMR were prospectively enrolled at a single center (August 2023-February 2024) and randomized into manual, or automated scan execution using prototype software. Patients with pacemakers, targeted indications, or inability to consent were excluded.
Comput Med Imaging Graph
December 2024
Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany.
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accelerate imaging and enhance reconstruction quality. Existing networks exhibit some common limitations that constrain further acceleration possibilities, including single-domain learning, reliance on a single regularization term, and equal feature contribution.
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