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Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising.
Invest Radiol
January 2025
From the Department of Radiology, Stanford University, Stanford, CA (K.W., M.J.M., A.M.L., A.B.S., A.J.H., D.B.E., R.L.B.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (K.W.); GE HealthCare, Houston, TX (X.W.); GE HealthCare, Boston, MA (A.G.); and GE HealthCare, Menlo Park, CA (P.L.).
Objectives: Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal.
View Article and Find Full Text PDFClinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.
Sci Rep
January 2025
Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks).
View Article and Find Full Text PDFDevelopment and external validation of the SMASHERS risk prediction model for aneurysmal subarachnoid haemorrhage in the general population: a population-based prospective cohort study.
BMJ Open
January 2025
Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
Objectives: Aneurysmal subarachnoid haemorrhage (ASAH) is a severe stroke type, preventable by screening for intracranial aneurysms followed by treatment in high-risk individuals. We aimed to develop and validate a risk prediction model for ASAH in the general population to identify high-risk individuals.
Design: We used the population-based prospective cohort studies of the United Kingdom (UK) Biobank for model development and the Trøndelag Health (HUNT) Study for model validation.
Transformative Gamified Binocular Therapy for Unilateral Amblyopia in Young Children: Pilot Prospective Efficacy and Safety Study.
JMIR Serious Games
January 2025
Department of Ophthalmology, Eye and ENT Hospital of Fudan University, No.83 Fenyang Road, Xuhui District, Shanghai, 200031, China, 86 021-64377134.
Background: Amblyopia is a common cause of visual impairment in children. Compliance with traditional treatments for amblyopia is challenging due to negative psychosocial impacts. Recent shifts in amblyopia treatment have moved from suppressing the dominant eye to enhancing binocular visual function.
View Article and Find Full Text PDFPulse wave parameters as a predictor of the development of post-transplant diabetes mellitus after kidney transplantation.
BMC Nephrol
January 2025
3rd Department of Internal Medicine, University Hospital Olomouc, Olomouc, Czech Republic.
Background: Kidney transplantation is the preferred treatment for patients with end-stage renal disease, significantly preserving kidney function and patient quality of life. However, post-transplant diabetes mellitus (PTDM) is a common complication, occurring in approximately one-third of renal transplant recipients. This study aims to evaluate the role of pulse wave parameters in predicting PTDM and to identify other pre-transplant risk factors.
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