Accurate Transaxial Region-of-Interest Reconstruction in Helical CT?

Abstract

In conventional helical computed tomography (CT), the field-of-view is a cylinder centered on the axis of the helix. Here, we consider the situation where all measurement lines are blocked except those intersecting a small cylindrical region-of-interest (ROI) not necessarily centered on the axis of the system. We address the question of image reconstruction inside the ROI. The patient boundary is assumed known, and we avoid the “interior problem” by assuming that the ROI includes part of the patient boundary. By applying analytic image reconstruction theory, we show that the entire cylindrical ROI can be reconstructed provided the pitch of the helix does not violate the well-known Tam–Danielsson detector condition. Using an iterative algorithm, we performed ROI reconstruction from simulated phantom data and from real patient data, and compared the results with full-field reconstructions. Visually, the ROI reconstructed images perfectly matched the full-field reconstructions. However, there were small quantitative discrepancies near the interior boundaries of the ROIs, which we attribute to the known reduced stability at one side of the inverse truncated Hilbert transform. In conclusion, we have demonstrated mathematically that accurate transverse ROI reconstruction is possible for helical CT, although care must be taken near the interior boundary to achieve quantitative accuracy.