Ray-driven Method Research Articles

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means of overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors' goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.

투과 컴퓨터 단층촬영을 위한 모델 기반 반복연산 재구성에서 투사선 구동 시스템 모델의 성능 비교

The key to model-based iterative reconstruction (MBIR) algorithms for transmission computed tomography lies in the ability to accurately model the data formation process from the emitted photons produced in the transmission source to the measured photons at the detector. Therefore, accurately modeling the system matrix that accounts for the data formation process is a prerequisite for MBIR-based algorithms. In this work we compared quantitative performance of the three representative ray-driven methods for calculating the system matrix; the ray-tracing method (RTM), the distance-driven method (DDM), and the strip-area based method (SAM). We implemented the ordered-subsets separable surrogates (OS-SPS) algorithm using the three different models and performed simulation studies using a digital phantom. Our experimental results show that, in spite of the more advanced features in the SAM and DDM, the traditional RTM implemented in the OS-SPS algorithm with an edge-preserving regularizer out-performs the SAM and DDM in restoring complex edges in the underlying object. The performance of the RTM in smooth regions was also comparable to that of the SAM or DDM.

Fast Implementation of Iterative Reconstruction with Exact Ray-Driven Projector on GPUs

Iterative methods are popular choices in image reconstruction fields due to their capability of recovering object information from incomplete acquisition data. However, the computation process involves frequent uses of forward and backward projections that are computationally expensive. Past research has proved that a forward projector that can produce high quality images is crucial to achieve a good convergence rate. In this paper a high performance iterative reconstruction framework is introduced, where two most popular iterative algorithms: Simultaneous Algebraic Reconstruction Technique (SART) and Ordered-subsets Expectation Maximization (OSEM) are supported. The framework utilizes Siddon's ray-driven method to generate forward projected images. Benefited from functionalities offered by current generation of graphics processing units (GPUs), it achieves better performance when compared to previous GPU implementations that use grid-interpolated methods, on top of the significant speedups over CPU-based solutions.

Speed up of an analytical algorithm for nonuniform attenuation correction by using PC video/graphics card architecture

A major task in quantitative SPECT (single photon emission computed tomography) reconstruction is compensation for object-specific attenuation, which is usually nonuniform. Mathematically this task is expressed as the inversion of the attenuated Radon transform. Novikov had derived an explicit inversion formula for the attenuated Radon transform for parallel-beam collimation geometry. In our previous work, we extended his work to variable focusing fan-beam (VFF) collimators. A ray-driven analytical inversion formula for VFF reconstruction with nonuniform attenuation was derived. The drawback of ray-driven methods is that they are time consuming. In this work, we proposed a fast implementation method, which includes algorithm optimization and acceleration by the texture-mapping architecture of PC graphics/video card. We further investigated the noise properties and associated artifacts of the analytical inversion formula. The artifacts were remarkably reduced when more projections were sampled to mitigate the problem of wide bandwidth of the discrete Hilbert transform. The reconstruction from noisy data demonstrated the accuracy and robustness of the presented ray-driven analytical inversion formula with dramatic speed acceleration by the PC graphics/video card.