Cortical multiple sclerosis lesions are disease-specific, yet inconspicuous on magnetic resonance images (MRI). Double inversion recovery (DIR) images are sensitive, but often unavailable in clinical routine and clinical trials. Artificially generated images can mitigate this issue, but lack histopathological validation. In this work, artificial DIR images were generated from postmortem 3D-T1 and proton-density (PD)/T2 or 3D-T1 and 3D fluid-inversion recovery (FLAIR) images, using a generative adversarial network. All sequences were scored for cortical lesions, blinded to histopathology. Subsequently, tissue samples were stained for proteolipid protein (myelin) and scored for cortical lesions type I-IV (leukocortical, intracortical, subpial and cortex-spanning, respectively). Histopathological scorings were then (unblinded) compared to MRI using linear mixed models. Images from 38 patients (26 female, mean age 64.3 ± 10.7) were included. A total of 142 cortical lesions were detected, predominantly subpial. Histopathology-blinded/unblinded sensitivity was 13.4/35.2% for artificial DIR generated from T1-PD/T2, 14.1/41.5% for artificial DIR from T1-FLAIR, 17.6/49.3% for conventional DIR and 10.6/34.5% for 3D-T1. When blinded to histopathology, there were no differences; with histopathological feedback at hand, conventional DIR and artificial DIR from T1-FLAIR outperformed the other sequences. Differences between histopathology-blinded/unblinded sensitivity could be minified through adjustment of the scoring criteria. In conclusion, artificial DIR images, particularly generated from T1-FLAIR could potentially substitute conventional DIR images when these are unavailable.
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http://dx.doi.org/10.1038/s41598-022-06546-4 | DOI Listing |
Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary.
we evaluated regression models based on quantitative ultrasound (QUS) parameters and compared them with a vendor-provided method for calculating the ultrasound fat fraction (USFF) in metabolic dysfunction-associated steatotic liver disease (MASLD). We measured the attenuation coefficient (AC) and the backscatter-distribution coefficient (BSC-D) and determined the USFF during a liver ultrasound and calculated the magnetic resonance imaging proton-density fat fraction (MRI-PDFF) and steatosis grade (S0-S4) in a combined retrospective-prospective cohort. We trained multiple models using single or various QUS parameters as independent variables to forecast MRI-PDFF.
View Article and Find Full Text PDFJ Imaging
January 2025
Department of Physiology, Centro Universitario de Ciencias de la Salud, University of Guadalajara, Guadalajara 44340, Mexico.
The area postrema (AP) is a key circumventricular organ involved in the regulation of autonomic functions. Accurate identification of the AP via MRI is essential in neuroimaging but it is challenging. This study evaluated 3D FSE Cube T2WI, 3D FSE Cube FLAIR, and 3D DIR sequences to improve AP detection in patients with and without multiple sclerosis (MS).
View Article and Find Full Text PDFConf Proc Int Conf Image Form Xray Comput Tomogr
August 2024
Department of Radiology, Perelman School of Medicine, Philadelphia, PA, USA.
Respiratory motion phantoms can be used for evaluation of CT imaging technologies such as motion artifact reduction algorithms and deformable image registration. However, current respiratory motion phantoms do not exhibit detailed lung tissue structures and thus do not provide a realistic testing environment. This paper presents PixelPrint, a method for 3D-printing deformable lung phantoms featuring highly realistic internal structures, suitable for a broad range of CT evaluations, optimizations, and research.
View Article and Find Full Text PDFInt J Part Ther
March 2025
Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan.
Purpose: The study's purpose was to use a simple geometry phantom to validate the deformable image registration (DIR) accuracy and dose warping accuracy in carbon ion radiotherapy (CIRT) and to provide an index for dosimetry in CIRT.
Materials And Methods: We used geometric and anatomical phantoms provided by AAPM TG-132. The DIRs of 3 different settings were performed between reference and translational images for each phantom.
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