A data-driven regularization strategy for statistical CT reconstruction
There is an unmet need for CT image reconstruction algorithms that reliably provide diagnostic image quality at reduced radiation dose. Toward this end, we integrate a state-of-the-art statistical reconstruction algorithm, ordered subsets, and separable quadratic surrogates (OS-SQS) accelerated with Nesterov's method, with our own data-driven regularization strategy using the split Bregman method. The regularization enforces intensity-gradient sparsity by minimizing bilateral total variation through the application of bilateral filtration. Adding to the advantages of statistical reconstruction, our implementation of bilateral filtration dynamically varies the regularization strength based on the noise level algorithmically measured within the data, accommodating variations in patient size and photon flux. We refer to this modified form of OS-SQS as OS-SQS with bilateral filtration (OS-SQS-BF), and we apply it to reconstruct clinical, helical CT data provided to us as part of the Low Dose CT Grand Challenge. Specifically, we evaluate OS-SQS-BF for quarter-dose statistical reconstruction and compare its performance with quarter-dose and full-dose filtered backprojection reconstruction. We present results for both the American College of Radiology (ACR) phantom and an abdominal CT scan. Our algorithm reduces noise by approximately 52% relative to filtered backprojection in the ACR phantom, while maintaining contrast and spatial-resolution performance relative to commercial filtered backprojection reconstruction. The quarter-dose scan for the abdominal data set confirmed the identification of 3 liver lesions when using OS-SQS-BF. The reconstruction time is a limitation that we will address in the future.
