We present the development of a two-component magnetic resonance (MR) fiducial system, that is, a fiducial marker device combined with an auto-segmentation algorithm, designed to be paired with existing ultrasound probe tracking and image fusion technology to automatically fuse MR and ultrasound (US) images. The fiducial device consisted of four ~6.4 mL cylindrical wells filled with 1 g/L copper sulfate solution. The algorithm was designed to automatically segment the device in clinical abdominal MR images. The algorithm's detection rate and repeatability were investigated through a phantom study and in human volunteers. The detection rate was 100% in all phantom and human images. The center-of-mass of the fiducial device was robustly identified with maximum variations of 2.9 mm in position and 0.9° in angular orientation. In volunteer images, average differences between algorithm-measured inter-marker spacings and actual separation distances were 0.53 ± 0.36 mm. "Proof-of-concept" automatic MR-US fusions were conducted with sets of images from both a phantom and volunteer using a commercial prototype system, which was built based on the above findings. Image fusion accuracy was measured to be within 5 mm for breath-hold scanning. These results demonstrate the capability of this approach to automatically fuse US and MR images acquired across a wide range of clinical abdominal pulse sequences.
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http://dx.doi.org/10.1002/acm2.12352 | DOI Listing |
Int J Radiat Oncol Biol Phys
January 2025
Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA.
Objectives: Radiotherapy manages pancreatic cancer in various settings; however, the proximity of gastrointestinal (GI) luminal organs-at-risk (OAR) poses challenges to conventional radiotherapy. Proton beam therapy (PBT) may reduce toxicities compared to photon therapy. This consensus statement summarizes PBT's safe and optimal delivery for pancreatic tumors.
View Article and Find Full Text PDFEur J Radiol
December 2024
Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany Berlin Institute of Health, Berlin, Germany. Electronic address:
Background: The Prostate Imaging-Reporting and Data System (PI-RADS) calls for reporting the prostate index lesion and the location within the transition (TZ) or peripheral zone (PZ) and location on a corresponding sector map. The aim of this study was to train a deep learning DL-based algorithm for automatic prostate sector mapping and to validate its' performance.
Methods: An automatic 24-sector grid-map (ASG) of the prostate was developed, based on an automatic zone-specific deep learning segmentation of the prostate.
Adv Sci (Weinh)
December 2024
Department of Bioengineering, Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
High-resolution optical microscopy, particularly super-resolution localization microscopy, requires precise real-time drift correction to maintain constant focus at nanoscale precision during the prolonged data acquisition. Existing methods, such as fiducial marker tracking, reflection monitoring, and bright-field image correlation, each provide certain advantages but are limited in their broad applicability. In this work, a versatile and robust drift correction technique is presented for single-molecule localization-based super-resolution microscopy.
View Article and Find Full Text PDFCommun Eng
December 2024
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China.
Continuous monitoring of nocturnal blood pressure is crucial for hypertension management and cardiovascular risk assessment. However, current clinical methods are invasive and discomforting, posing challenges. These traditional techniques often disrupt sleep, impacting patient compliance and measurement accuracy.
View Article and Find Full Text PDFFront Neurorobot
December 2024
Faculty of Computer Science and AI, Air University, Islamabad, Pakistan.
Introduction: Recognizing human actions is crucial for allowing machines to understand and recognize human behavior, with applications spanning video based surveillance systems, human-robot collaboration, sports analysis systems, and entertainment. The immense diversity in human movement and appearance poses a significant challenge in this field, especially when dealing with drone-recorded (RGB) videos. Factors such as dynamic backgrounds, motion blur, occlusions, varying video capture angles, and exposure issues greatly complicate recognition tasks.
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