Yan Xia
Region-of-Interest Imaging with C-arm Computed Tomography
Abstract
C-arm based flat-detector computed tomography (FDCT) is a promising approach for neurovascular diagnosis and intervention since it facilitates proper analysis of surgical implants and intra-procedural guidance. In the majority of endovascular treatments, intra-procedural updates of the imaged object often are restricted to a small diagnostic region of interest (ROI). Such targeted ROI is often the region of intervention that contains device/vessel specific information such as stent expansion or arterial wall apposition. Following the principle of as low as reasonably achievable (ALARA), it is highly desirable to reduce unnecessary peripheral doses outside an ROI by using physical X-ray collimation, leading to substantial reduction of patient dose. However, such a technique gives rise to severely truncated projections from which conventional reconstruction algorithms generally yield images with strong truncation artifacts.
The primary research goal of this thesis, therefore, lies on the algorithmic development of various truncation artifact reduction techniques that are dedicated for different imaging scenarios. First, a new data completion method is proposed that utilizes sinogram consistency conditions to estimate the missing sinogram. Although it is only extended to a 2D fan-beam geometry, preliminary results suggest the method is promising regarding truncation artifact reduction and attenuation coefficient recovery. Thereafter, three algorithms are presented, which either follow the analytic filtered backprojection (FBP) frame or are by construction in an iterative manner. They are capable of generating a 3D image from transaxially truncated data and thus appear to be closer to clinical applications. The first approach is the refinement of an existing truncation robust algorithm – ATRACT, which is implicitly effective with respect to severely truncated data. In this thesis, ATRACT is modified to more practically-useful reconstruction methods by expressing its expensive non-local filter as an efficient 1D/2D analytic convolution. The second approach is targeted to particular imaging applications that require an ROI with high image quality for diagnosis, and also a surrounding region with the relatively low resolution for orientation. To accomplish this task, an interleaved acquisition strategy that acquires both a sparse set of global non-truncated data and a dense set of truncated data is presented, along with three associated algorithms. The third approach is an attempt to exploit low-dose patient-specific prior knowledge for the extrapolation of truncated projections. The comparative evaluation clearly depicts the algorithmic performance of all investigated 3D methods under a uniform evaluation framework. In general, ATRACT appears to be more robust than the explicit water cylinder extrapolation in severe truncation case. Contrary to the heuristic methods, the techniques that come with either a sparse set of global data or prior knowledge achieve the ROI reconstructions in a more accurate and robust manner. The decision on which method should be selected relies on multiple factors, but the presented results could be used as the first indicator for the ease of such selection.