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Unit-cell parameters determination from a set of independent electron diffraction zonal patterns.
Acta Crystallogr A Found Adv
March 2025
Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-11 Mustuno, Atsuta-ku, Nagoya, 456-8587, Japan.
Due to the short de Broglie wavelength of electrons compared with X-rays, the curvature of their Ewald sphere is low, and individual electron diffraction patterns are nearly flat in reciprocal space. As a result, a reliable unit-cell determination from a set of randomly oriented electron diffraction patterns, an essential step in serial electron diffraction, becomes a non-trivial task. Here we describe an algorithm for unit-cell determination from a set of independent electron diffraction patterns, as implemented in the program PIEP (Program for Interpreting Electron diffraction Patterns), written in the early 1990s.
View Article and Find Full Text PDFAbdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.
J Imaging Inform Med
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Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Exam protocoling is a significant non-interpretive task burden for radiologists. The purpose of this work was to develop a natural language processing (NLP) artificial intelligence (AI) solution for automated protocoling of standard abdomen and pelvic magnetic resonance imaging (MRI) exams from basic associated order information and patient metadata. This Institutional Review Board exempt retrospective study used de-identified metadata from consecutive adult abdominal and pelvic MRI scans performed at our institution spanning 2.
View Article and Find Full Text PDFAutomatic Identification of Adenoid Hypertrophy via Ensemble Deep Learning Models Employing X-ray Adenoid Images.
J Imaging Inform Med
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Ear, Nose and Throat Department, Batman Training and Research Hospital, Batman, Turkey.
Adenoid hypertrophy, characterized by the abnormal enlargement of adenoid tissue, is a condition that can cause significant breathing and sleep disturbances, particularly in children. Accurate diagnosis of adenoid hypertrophy is critical, yet traditional methods, such as imaging and manual interpretation, are prone to errors. This study uses an ensemble deep learning-based approach for adenoid classification.
View Article and Find Full Text PDFAn approach to determine the net predictive multivariate association pattern between cardiorespiratory fitness and a high-resolution physical activity spectrum in the presence of multicollinear covariates.
J Sports Sci
January 2025
Department of Sport, Food and Natural Sciences, Western Norway University of Applied Sciences, Sogndal, Norway.
Multivariate pattern analysis was recently extended with covariate projections to solve the challenging task of modelling and interpreting associations in the presence of linear dependent multivariate covariates. Within a joint model, this approach allows quantification of the net association pattern between the outcome and the explanatory variables and between the individual covariates and these variables. The aim of this paper is to apply this methodology to establish the net multivariate association pattern between cardiorespiratory fitness (CRF) and a high-resolution linear dependent physical activity (PA) intensity descriptor derived from accelerometry in children and to validate the crucial sub-regions in the PA spectrum predicting CRF.
View Article and Find Full Text PDFBackground And Objective: To assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically significant cancers with the help of artificial intelligence methods.
Methods: This study included 2986 men who underwent biopsies at two institutions. We trained the PROstate Cancer detection on B-mode transrectal UltraSound images NETwork (ProCUSNet) to determine whether ultrasound can reliably detect cancer.
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