GPU accelerated FDTD solver and its application in MRI

Medical image processing on the GPU - past, present and future

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

Inverse field-based approach for simultaneous B₁ mapping at high fields - a phantom based study

Based on computational electromagnetics and multi-level optimization, an inverse approach of attaining accurate mapping of both transmit and receive sensitivity of radiofrequency coils is presented. This paper extends our previous study of inverse methods of receptivity mapping at low fields, to allow accurate mapping of RF magnetic fields (B(1)) for high-field applications. Accurate receive sensitivity mapping is essential to image domain parallel imaging methods, such as sensitivity encoding (SENSE), to reconstruct high quality images. Accurate transmit sensitivity mapping will facilitate RF-shimming and parallel transmission techniques that directly address the RF inhomogeneity issue, arguably the most challenging issue of high-field magnetic resonance imaging (MRI). The inverse field-based approach proposed herein is based on computational electromagnetics and iterative optimization. It fits an experimental image to the numerically calculated signal intensity by iteratively optimizing the coil-subject geometry to better resemble the experiments. Accurate transmit and receive sensitivities are derived as intermediate results of the optimization process. The method is validated by imaging studies using homogeneous saline phantom at 7T. A simulation study at 300MHz demonstrates that the proposed method is able to obtain receptivity mapping with errors an order of magnitude less than that of the conventional method. The more accurate receptivity mapping and simultaneously obtained transmit sensitivity mapping could enable artefact-reduced and intensity-corrected image reconstructions. It is hoped that by providing an approach to the accurate mapping of both transmit and receive sensitivity, the proposed method will facilitate a range of applications in high-field MRI and parallel imaging.