Abstract

A barrier to the adoption of non-Cartesian parallel magnetic resonance imaging for real-time applications has been the times required for the image reconstructions. These times have exceeded the underlying acquisition time thus preventing real-time display of the acquired images. We present a reconstruction algorithm for commodity graphics hardware (GPUs) to enable real time reconstruction of sensitivity encoded radial imaging (radial SENSE). We demonstrate that a radial profile order based on the golden ratio facilitates reconstruction from an arbitrary number of profiles.

 This allows the temporal resolution to be adjusted on the fly. A user adaptable regularization term is also included and, particularly for highly undersampled data, used to interactively improve the reconstruction quality. Each reconstruction is fully self-contained from the profile stream, i.e., the required coil sensitivity profiles, sampling density compensation weights, regularization terms, and noise estimates are computed in real-time from the acquisition data itself. The reconstruction implementation is verified using a steady state free precession (SSFP) pulse sequence and quantitatively evaluated. Three applications are demonstrated; real-time imaging with real-time SENSE 1) or k-t SENSE 2) reconstructions, and 3) offline reconstruction with interactive adjustment of reconstruction settings.

Paper available at IEEE.

Abstract:
In this paper we discuss a framework for modeling the 3D lung dynamics of normal and diseased human subjects and visualizing them using an Augmented Reality (AR) based environment. The framework is based on the results obtained from pulmonary function tests and lung image-data of human subjects obtained from 4D High-Resolution Computed Tomography (HRCT). The components of the framework include a parameterized pressure-volume (PV) relation estimated from normal human subjects, and a physics and physiology-based 3D deformable lung model extracted from the 4D HRCT data of normal and tumor-influenced human subjects. The parameterized PV relation allows modeling different breathing conditions of a human subject. The 3D deformable lung model allows visualizing the 3D shape changes of the lung for the breathing condition simulated by the PV relation. Additionally, the 3D lung model is deformed using a graphics processing unit (GPU) and its vertex shaders, which satisfies the real-time frame-rate requirements of the AR environment.

Paper available at ACM.

Abstract: The medical training systems based on virtual simulation are highly desired since minimally invasive surgical techniques have become popular to patients. The training system helps surgeon trainees to acquire, practice and evaluate their surgical skills, and the key component of such a system is to simulate the dynamic procedure such as 3D biological tissue deformation in surgical operation. In our paper, an improved mass-spring model is proposed to represent the biological tissue surface, during which the virtual spring is introduced and utilized to help compensate the weakness of the conventional mass-spring model. Then Verlet integration is adopted to calculate the position of mass points during the deformation process without explicit computation of the velocity values. Finally the bilinear interpolation method is employed to generate one smooth mesh to render the deformed tissue surface. To speed up the simulation performance for surgical tissue deformation, CUDA based GPU computing is adopted, while related data structures and algorithm are designed and implemented for the parallel computation. Our method has been tested by experiments and it can generate realistic biological tissue deformation images. Compared with CPU, our approach can be performed in real time. Therefore, the proposed method in our paper is an effective and practical way for tissue deformation of medical training system.

Abstract:

A novel approach to simulate soft tissue cutting in a virtual reality endoscopic simulator for surgical training is proposed in this paper. This approach is based on both the improved mass-spring model and the use of computational geometry. A virtual spring is introduced and harnessed to help compensate the shortcoming of the conventional mass-spring model, and a detection algorithm utilizing decomposition of affine coordinates is adopted for the purpose of determining the springs that intersect with the cutting plane. To speed up the simulation performance, algorithms and data structures for the cutting model are designed and implemented based on GPGPU (General-purpose computing on graphics processing units). The performance comparison on the GPU and CPU demonstrates that the proposed method is efficacious and practical.

Abstract

A real-time plasma position control system is mandatory to achieve long duration (up to 250 ms), Alternating Current (AC) discharges on the ISTTOK tokamak. Such a system has been used for some time supported only on magnetic field diagnostic data. However, this system is clearly challenged when the plasma current is low, rendering it inoperative during the plasma current reversal. A tomography diagnostic with 3 pinhole cameras and 8 silicon photodiode channels per camera was installed and customized to supply alternative plasma position to be used for plasma position control. As no filtering is applied most of the radiation detected is in the visible/near-UV range. The data acquisition and control system is based on a 2MSPS, 32 channel acquisition ATCA module and the data processing is performed on a GPU that is connected to another ATCA module via the PCI-Express port for fast data access. Control commands are relayed to the plasma positioning PF power supplies via optical serial ports. In this work, an overview of some of the tomographic reconstruction algorithms most commonly used for tokamak plasmas is done and an assessment is made on the best candidate for the proposed real-time implementation. The tomography acquisition and plasma control systems operating at ISTTOK are also described. This system aims at achieving the following goals: (i) execute a tomographic reconstruction; (ii) determine the average emissivity position from it; (iii) calculate the shift from the axis and (iv) supply the vertical field power supply with the desired current value, all in less than

${100}~mu {rm s}$. The horizontal magnetic field power supply unit, essential for vertical plasma positioning, is foreseen to be integrated in the system and will have no impact in the processing time.

Paper available at IEEE.