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Purpose: To evaluate a prototype fast spin echo (FSE) triple-echo-Dixon (fTED) technique for breath-hold, fat-suppressed, T2-weighted abdominal imaging.
Materials And Methods: Forty patients underwent breath-hold T2-weighted abdominal imaging with fTED and conventional fast recovery (FR) FSE with chemical shift-selective saturation (CHESS). FRFSE and fTED images were compared for overall image quality, homogeneity of fat suppression, image sharpness, anatomic detail, and phase artifact. Depiction of disease was recorded separately for FRFSE and fTED images.
Results: FTED successfully reconstructed water-only and fat-only images from source images in all 40 cases. Water and fat separation was perfect in 36 (0.90) patients. Homogeneity of fat suppression was superior on the fTED images in 38 (0.95) of 40 cases. FTED images showed better anatomic detail in 27 (0.68), and less susceptibility artifact in 20 (0.50). FRFSE images showed less vascular pulsation artifact in 30 (0.75) cases, and less phase artifact in 21 (0.53) cases. There was no difference in depiction of disease for FRFSE and fTED images.
Conclusion: FTED is a robust sequence providing breath-hold T2-weighted images with superior fat suppression, excellent image quality, and at least equal depiction of disease compared to conventional breath-hold T2-weighted FRFSE imaging.
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http://dx.doi.org/10.1002/jmri.21880 | DOI Listing |
World J Radiol
August 2025
Department of Radiology, Huizhou Central People's Hospital, Huizhou 516001, Guangdong Province, China.
Background: Esophageal cancer (EC) is one of the most prevalent malignant gastrointestinal tumors; accurate prediction of EC staging has high significance before treatment.
Aim: To explore a rational radiomic approach for predicting preoperative staging of EC based on magnetic resonance imaging (MRI).
Methods: This retrospective study included 210 patients with pathologically confirmed EC, randomly divided into a primary cohort ( = 147) and a validation cohort ( = 63) in a ratio of 7:3.
Quant Imaging Med Surg
September 2025
Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Background: Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of abdominal conditions. A comprehensive assessment, especially of the liver, requires multi-planar T2-weighted sequences. To mitigate the effect of respiratory motion on image quality, the combination of acquisition and reconstruction with motion suppression (ARMS) and respiratory triggering (RT) is commonly employed.
View Article and Find Full Text PDFAbdom Radiol (NY)
August 2025
Department of Radiology, Mayo Clinic, Rochester, USA.
Objectives: To determine whether deep learning (DL)-based image quality (IQ) assessment of T2-weighted images (T2WI) could be biased by the presence of clinically significant prostate cancer (csPCa).
Methods: In this three-center retrospective study, five abdominal radiologists categorized IQ of 2,105 transverse T2WI series into optimal, mild, moderate, and severe degradation. An IQ classification model was developed using 1,719 series (development set).
J Hum Kinet
July 2025
Center for Liberal Arts, Meiji Gakuin University, Tokyo, Japan.
There are few studies that clarify the level of muscle activity in the trunk and pelvis muscles during sprinting. This study aimed to investigate muscle activity in the trunk and pelvis muscles during sprinting using T2-weighted magnetic resonance imaging (MRI). The pre- and post-test designs were employed by measuring trunk and pelvis muscle activity using T2-weighted MRI before and after 60-m round-trip sprints.
View Article and Find Full Text PDFMagn Reson Med
August 2025
Program in Applied Mathematics, The University of Arizona, Tucson, Arizona, USA.
Purpose: To accelerate respiratory triggered free-breathing T2 mapping of the abdomen while maintaining high-quality anatomical images, accurate T2 maps, and fast reconstruction times.
Methods: We developed a flexible deep learning framework that can be trained in a fully supervised manner to improve T2-weighted images or in a self-supervised manner to reconstruct T2 maps.
Results: For retrospectively undersampled data, anatomical images and T2 maps reconstructed by the proposed deep learning method demonstrated reduced voxel-wise error compared to existing traditional and compressed sensing techniques.