Joseph John Manak

Registration and integration for fluoroscopy device enhancement

We investigated a method, motion compensated integration (MCI), for enhancing stent Contrast-to-Noise Ratio (CNR) such that stent deployment may be more easily assessed. MCI registers fluoroscopic frames on the basis of stent motion and performs pixel-wise integration to reduce noise. Registration is based on marker balls, high contrast interventional devices which guide the clinician in stent placement. It is assumed that stent motion is identical to that of the marker balls. Detecting marker balls and identifying their centroids with a high degree of accuracy is a non-trivial task. To address the required registration accuracy, in this work we examine MCIs visualization benefit as a function of registration error. We employ adaptive forced choice experiments to quantify human discrimination fidelity. Perception results are contrasted with CNR measurements. For each level of registration inaccuracy investigated, MCI conferred a benefit (p < 0.05) on stent deployment assessment suggesting the technique is tolerant of modest registration error. We also consider the blurring effect of cardiac motion during the x-ray pulse and select frames for integration as a function of cardiac phase imaged.

A novel approach to reconstruct cone-beam data acquired on less than a full scan

Reconstruction for an incomplete circular scan typically results in artifacts due not only to data incompleteness, but also to non-optimal handling of data redundancy. We present a novel reconstruction approach aimed at improving the handling of redundant data after first analyzing how such redundancy is handled in a more traditional approach.

Initial experience in image reconstruction from limited-angle C-arm CBCT data

C-arm cone-beam CT (CBCT) often acquires data over a limited angular range. Image reconstruction from limited-angle data (i.e., data less than a short scan) becomes mathematically an ill-posed problem, and the inversion of the X-ray transform by use of conventional analytic formulas can introduce significant artifacts. In this work, using the adaptive steepest descent and projection-onto-convex-sets (ASD-POCS) algorithm, we investigate image reconstructions from limited-angle simulation cone-beam data and real patient data acquired with a C-arm system. The results show that the algorithm can reconstruct potentially useful images from data collected over limited angular range.