Effective noise-suppressed and artifact-reduced reconstruction of SPECT data using a preconditioned alternating projection algorithm.

PURPOSE: The authors have recently developed a preconditioned alternating projection algorithm (PAPA) with total variation (TV) regularizer for solving the penalized-likelihood optimization model for single-photon emission computed tomography (SPECT) reconstruction. This algorithm belongs to a novel class of fixed-point proximity methods. The goal of this work is to investigate how PAPA performs while dealing with realistic noisy SPECT data, to compare its performance with more conventional methods, and to address issues with TV artifacts by proposing a novel form of the algorithm invoking high-order TV regularization, denoted as HOTV-PAPA, which has been explored and studied extensively in the present work. METHODS: Using Monte Carlo methods, the authors simulate noisy SPECT data from two water cylinders; one contains lumpy "warm" background and "hot" lesions of various sizes with Gaussian activity distribution, and the other is a reference cylinder without hot lesions. The authors study the performance of HOTV-PAPA and compare it with PAPA using first-order TV regularization (TV-PAPA), the Panin-Zeng-Gullberg one-step-late method with TV regularization (TV-OSL), and an expectation-maximization algorithm with Gaussian postfilter (GPF-EM). The authors select penalty-weights (hyperparameters) by qualitatively balancing the trade-off between resolution and image noise separately for TV-PAPA and TV-OSL. However, the authors arrived at the same penalty-weight value for both of them. The authors set the first penalty-weight in HOTV-PAPA equal to the optimal penalty-weight found for TV-PAPA. The second penalty-weight needed for HOTV-PAPA is tuned by balancing resolution and the severity of staircase artifacts. The authors adjust the Gaussian postfilter to approximately match the local point spread function of GPF-EM and HOTV-PAPA. The authors examine hot lesion detectability, study local spatial resolution, analyze background noise properties, estimate mean square errors (MSEs), and report the convergence speed and computation time. RESULTS: HOTV-PAPA yields the best signal-to-noise ratio, followed by TV-PAPA and TV-OSL/GPF-EM. The local spatial resolution of HOTV-PAPA is somewhat worse than that of TV-PAPA and TV-OSL. Images reconstructed using HOTV-PAPA have the lowest local noise power spectrum (LNPS) amplitudes, followed by TV-PAPA, TV-OSL, and GPF-EM. The LNPS peak of GPF-EM is shifted toward higher spatial frequencies than those for the three other methods. The PAPA-type methods exhibit much lower ensemble noise, ensemble voxel variance, and image roughness. HOTV-PAPA performs best in these categories. Whereas images reconstructed using both TV-PAPA and TV-OSL are degraded by severe staircase artifacts; HOTV-PAPA substantially reduces such artifacts. It also converges faster than the other three methods and exhibits the lowest overall reconstruction error level, as measured by MSE. CONCLUSIONS: For high-noise simulated SPECT data, HOTV-PAPA outperforms TV-PAPA, GPF-EM, and TV-OSL in terms of hot lesion detectability, noise suppression, MSE, and computational efficiency. Unlike TV-PAPA and TV-OSL, HOTV-PAPA does not create sizable staircase artifacts. Moreover, HOTV-PAPA effectively suppresses noise, with only limited loss of local spatial resolution. Of the four methods, HOTV-PAPA shows the best lesion detectability, thanks to its superior noise suppression. HOTV-PAPA shows promise for clinically useful reconstructions of low-dose SPECT data.

Development of ultrafast laser-based x-ray in-vivo phase-contrast micro-CT beamline for biomedical applications at Advanced Laser Light Source (ALLS).

We are developing and exploring the imaging performance of, an in vivo, in-line holography, x-ray phase-contrast, micro-CT system with an ultrafast laser-based x-ray (ULX) source. By testing and refining our system, and by performing computer simulations, we plan to improve system performance in terms of contrast resolution and multi-energy imaging to a level beyond what can be obtained using a conventional microfocal x-ray tube. Initial CT projection sets at single energy (Mo K(alpha) and K(beta) lines) were acquired in the Fresnel regime and reconstructed for phantoms and a euthanized mouse. We also performed computer simulations of phase-contrast micro-CT scans for low-contrast, soft-tissue, tumor imaging. We determined that, in order to perform a phase-contrast, complete micro-CT scan using ULX, the following conditions must be met: (i) the x-ray source needs to be stable during the scan; (ii) the laser focal spot size needs to be less than 10 mum for source-to-object distance greater than 30 cm; (iii) the laser light intensity on the target needs to be in the range of 5 x 10(17) to 5 x 10(19) W/cm(2); (iv) the ablation protection system needs to allow uninterrupted scans; (v) the laser light focusing on the target needs to remain accurate during the entire scan; (vi) a fresh surface of the target must be exposed to consecutive laser shots during the entire scan; (vii) the effective detector element size must be less than 12 mum. Based on the results obtained in this research project, we anticipate that the new 10 Hz, 200 TW laser with 50 W average power that is being commissioned at ALLS will allow us practical implementation of in vivo x-ray phase-contrast micro-CT.

Soft tissue small avascular tumor imaging with x-ray phase-contrast micro-CT in-line holography.

To assess the feasibility of small soft tissue avascular tumor micro-CT imaging with x-ray phase-contrast in-line holography, we have studied micro-CT imaging with in-line geometry of small spheroidal avascular tumor models with quiescent cell core (< 250 mum) and various distributions of the proliferating cell density (PCD) forming the outer shell. We have simulated imaging with an ultrafast laser-based x-ray source with a Mo target. We observe phase-contrast enhancement of the tumor boundaries in the reconstructed transaxial images, resulting in improved detection of small soft tissue tumors, providing that the PCD density gradient is sufficiently large.