A three-dimensional analytical technique for the representation of the cranial surface based on a spherical harmonic expansion is presented. The analytical surface representation is used in conjunction with a sample iterative ray-intersection algorithm to compute the dose calculation depth. The technique is compared to other conventional techniques and it is determined that the spherical harmonic approach of approximating the cranial surface decreases the depth calculation time by approximately a factor of 60.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1118/1.596629 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Focal volume reduction in transcranial focused ultrasound using spherical wave expansions.
Ultrasonics
January 2025
School of Biological Science and Medical Engineering, Beihang University, Beijing, China. Electronic address:
Transcranial focused ultrasound (tFUS) has been gaining increased attention as a non-invasive modality for treating brain diseases. However, accurately focusing on brain structures remains a challenge as the ultrasound is severely distorted by the presence of the skull. In this article, we propose a promising distortion correction method based on spherical wave expansions.
View Article and Find Full Text PDFPerformance Analysis of Cardioid and Omnidirectional Microphones in Spherical Sector Arrays for Coherent Source Localization.
Sensors (Basel)
November 2024
Department of Computer and Electrical Engineering, Mid Sweden University, 851 70 Sundsvall, Sweden.
Traditional spherical sector microphone arrays using omnidirectional microphones face limitations in modal strength and spatial resolution, especially within spherical sector configurations. This study aims to enhance array performance by developing a spherical sector array employing first-order cardioid microphones. A model based on spherical sector harmonic (SSH) functions is introduced to extend the benefits of spherical harmonics to sector arrays.
View Article and Find Full Text PDFLeveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data.
J Mach Learn Biomed Imaging
May 2024
Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Article Synopsis
- Convolutional neural networks (CNNs) enhance parameter sharing and translational equivariance using convolutional kernels, and adjusting these to be SO(3)-steerable improves their efficacy.
- These rotationally-equivariant convolutional layers offer benefits like increased robustness to unseen poses, reduced network size, and better sample efficiency compared to standard convolutional layers.
- The authors introduce a new family of segmentation networks utilizing equivariant voxel convolutions based on spherical harmonics, achieving improved performance in MRI brain tumor segmentation without needing rotation-based data augmentation.
Satellite monitoring of long period ocean-induced magnetic field variations.
Philos Trans A Math Phys Eng Sci
December 2024
Department of Space Research and Technology, Technical University of Denmark, Lyngby, Denmark.
Satellite magnetic field observations have the potential to provide valuable information on dynamics, heat content and salinity throughout the ocean. Here, we present the expected spatio-temporal characteristics of the ocean-induced magnetic field (OIMF) at satellite altitude on periods of months to decades. We compare these to the characteristics of other sources of Earth's magnetic field, and discuss whether it is feasible for the OIMF to be retrieved and routinely monitored from space.
View Article and Find Full Text PDFRealizing the Calculation of a Fully Normalized Associated Legendre Function Based on an FPGA.
Sensors (Basel)
November 2024
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
A large number of fully normalized associated Legendre function (fnALF) calculations are required to compute Earth's gravity field elements using ultra high-order gravity field coefficient models. In the surveying and mapping industry, researchers typically rely on CPU-based systems for these calculations, which leads to limitations in execution speed and power efficiency. Although modern CPUs improve instruction execution efficiency through instruction-level parallelism, the constraints of a shared memory architecture impose further limitations on the execution speed and power efficiency.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!