We demonstrate the ability to register easily and accurately volumetric ultrasound scans without significant data preprocessing or user intervention. Two volumetric ultrasound breast scan data sets were acquired from two different patients with breast cancer. Volumetric scan data were acquired by manually sweeping a linear array transducer mounted on a linear slider with a position encoder. The volumetric data set pairs consisted of color flow and/or power mode Doppler data sets acquired serially on the same patients. A previously described semiautomatic registration method based on maximizing mutual information was used to determine the transform between data sets. The results suggest that, even for the deformable breast, three-dimensional full affine transforms can be sufficient to obtain clinically useful registrations; warping may be necessary for increased registration accuracy. In conclusion, mutual information-based automatic registration as implemented on modern workstations is capable of yielding clinically useful registrations in times <35 min.
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http://dx.doi.org/10.1016/s0301-5629(98)00148-3 | DOI Listing |
Eur J Radiol Open
June 2025
Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Background: Deep learning (DL) accelerated controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), provides high spatial resolution T1-weighted imaging of the upper abdomen. We aimed to investigate whether DL-CAIPIRINHA-VIBE can improve image quality, vessel conspicuity, and lesion detectability compared to a standard CAIPIRINHA-VIBE in renal imaging at 3 Tesla.
Methods: In this prospective study, 50 patients with 23 solid and 45 cystic renal lesions underwent MRI with clinical MR sequences, including standard CAIPIRINHA-VIBE and DL-CAIPIRINHA-VIBE sequences in the nephrographic phase at 3 Tesla.
Ultrasound Med Biol
January 2025
Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province, China.
Objectives: Three-dimensional (3D) ultrasound imaging can overcome the limitations of conventional two-dimensional (2D) ultrasound imaging in structural observation and measurement. However, conducting volumetric ultrasound imaging for large-sized organs still faces difficulties including long acquisition time, inevitable patient movement, and 3D feature recognition. In this study, we proposed a real-time volumetric free-hand ultrasound imaging system optimized for the above issues and applied it to the clinical diagnosis of scoliosis.
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January 2025
Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 29 Avenue du Marechal de Lattre de Tassigny, 54000, Nancy, France.
Background: We evaluated the accuracy of magnetic resonance imaging (MRI) computed tomography (CT)-like sequences compared to normal-resolution CT (NR-CT) and super-high-resolution CT (SHR-CT) for planning of cochlear implantation.
Methods: Six cadaveric temporal bone specimens were used. 3-T MRI scans were performed using radial volumetric interpolated breath-hold (STARVIBE), pointwise-encoding time reduction with radial acquisition (PETRA), and ultrashort time of echo (UTE) sequences.
Sci Rep
January 2025
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.
Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images.
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December 2024
Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, 27599, USA.
A long-standing goal of neuroimaging is the non-invasive volumetric assessment of whole brain function and structure at high spatial and temporal resolutions. Functional ultrasound (fUS) and ultrasound localization microscopy (ULM) are rapidly emerging techniques that promise to bring advanced brain imaging and therapy to the clinic with the safety and low-cost advantages associated with ultrasound. fUS has been used to study cerebral hemodynamics at high temporal resolutions while ULM has been used to study cerebral microvascular structure at high spatial resolutions.
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