Background: Cardiovascular magnetic resonance (CMR) phase-contrast is used to quantify blood flow. We sought to develop a complex-difference reconstruction for inline super-resolution of phase-contrast (CRISPFlow) to accelerate phase-contrast imaging.
Methods: CRISPFlow was built on the super-resolution generative adversarial network. The model was trained and tested (4:1 ratio) using retrospectively identified phase-contrast images from 2020 patients (56 ± 16 years; 56% men) referred for clinical 3T CMR at a single center from 2018 to 2023. For testing, ascending aortic flow images collected with 2.5 × 1.9 mm resolution using generalized autocalibrating partially parallel acquisitions (GRAPPA) were used to synthesize images with 7.5 × 1.9 mm resolution. CRISPFlow subsequently restored spatial resolution. In a prospective validation study of 38 participants (57 ± 15 years; 14 men) and 16 healthy individuals (42 ± 16 years; 6 men), CRISPFlow was applied to phase-contrast images collected with 7.5 × 1.9 mm resolution with use of GRAPPA and was compared to GRAPPA-accelerated images collected with 2.3 × 1.9 mm resolution. A blur metric was used to quantify sharpness. Aortic flow measurements were obtained semi-automatically. Statistical evaluation included analysis of variance, Bland-Altman analysis, Pearson correlation coefficient (r).
Results: CRISPFlow reconstruction was successful in all cases. CRISPFlow reduced blurring in retrospective (0.35 vs. 0.47, P < 0.001) and prospective (0.34 vs. 0.48, P < 0.001) images with 7.5 × 1.9 mm resolution. Blurring in CRISPFlow images was similar to blurring in images with 2.5 × 1.9 mm (0.35 vs. 0.35, P = 0.4082) and 2.3 × 1.9 mm (0.34 vs. 0.32, P < 0.001) resolution. Bland-Altman differences in forward volume (-2 ml [-8 to 3ml]), regurgitant volume (0ml [-3 to 2ml]), and a fraction (0% [-5 to 4%]) showed good agreement between the two techniques in a retrospective cohort. Differences in forward volume (1ml [-11 to 14ml]) also showed good agreement in the prospective cohort. There was a strong correlation (all r > 0.90) between GRAPPA and CRISPFlow measurements of flow in both studies.
Conclusion: We demonstrated the potential of complex-difference reconstruction for inline super-resolution of phase-contrast flow (CRISPFlow) to accelerate phase contrast.
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http://dx.doi.org/10.1016/j.jocmr.2024.101128 | DOI Listing |
Pol J Vet Sci
December 2024
Department of Epizootiology and the Clinic of Infectious Diseases, Faculty of Veterinary Medicine, University of Life Sciences in Lublin, Głęboka 30, 20-612 Lublin, Poland.
The effects of T4 are mainly manifested by positive ino- and chronotropism. The syndrome accompanying hypothyroidism in rabbits (impaired myocardial contractility and reduced ejection capacity) is caused by a deficiency of thyroid hormones - especially T4. The study group consisted of a total of 41 animals: 15 males and 26 females, ranging in age from 2 months to 8 years, with echocardiogram showing reduced fractional shortening (<30%), with normal results of heamatological and biochemical tests.
View Article and Find Full Text PDFIndian J Orthop
January 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China.
Introduction: The Steinberg classification system is commonly used by orthopedic surgeons to stage the severity of patients with osteonecrosis of the femoral head (ONFH), and it includes mild, moderate, and severe grading of each stage based on the area of the femoral head affected. However, clinicians mostly grade approximately by visual assessment or not at all. To accurately distinguish the mild, moderate, or severe grade of early stage ONFH, we propose a convolutional neural network (CNN) based on magnetic resonance imaging (MRI) of the hip joint of patients to accurately grade and aid diagnosis of ONFH.
View Article and Find Full Text PDFFront Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
Front Endocrinol (Lausanne)
December 2024
Department of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Objective: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after surgery.
Methods: Patients undergoing endoscopic transsphenoidal surgery (ETS) for pituitary adenoma were included in this retrospective and prospective study.
Front Oncol
November 2024
Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Purpose: This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC).
Methods: A total of 301 patients (training cohort, n = 210; testing cohort, n = 91) with GC were retrospectively collected. Relevant clinical predictors were determined through the application of univariate and multivariate logistic regression analyses.
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