New approaches to region of interest computed tomography

Abstract

In classical x-ray CT, the diameter of the field of measurement (FOM) must not fall below the transversal diameter of the patient or specimen. Thereby, the ratio of the diameter of FOM and the number of transversal detector elements typically defines the spatial resolution. The authors aim at improving the spatial resolution within a region of interest (ROI) by a factor of 10-100 while maintaining artifact-free CT image reconstruction inside and outside the ROI. Two novel methods are proposed for artifact-free reconstruction of the truncated ROI scan (data weighting method and data filtering method) and compared with the gold standard (data completion method) for this problem. First, an overview scan with low spatial resolution and a large FOM that exceeds the object transversally is performed. Second, a high-resolution scan is performed, where the scanner's magnification is changed such that the FOM matches the ROI at the cost of laterally truncated projection data. The gold standard is forward projecting the low-resolution scan on the rays missing in the high-resolution scan. The authors propose the data filtering method, which uses the low-resolution reconstruction and calculates a high frequency correction term from the high-resolution scan, and the data weighting method, which reconstructs the truncated high-resolution data and calculates a detruncation image from the low-resolution data. The methods are compared using a simulation of the Forbild head phantom and a measurement of a spinal disk implant. The results of the data weighting method and the data completion method show the same image quality. The data filtering method yields slightly inferior image quality that may still be sufficient for many applications. Both new methods considerably outperform the data completion method regarding the computational load. The new ROI reconstruction methods are superior to the gold standard regarding the computational load. Comparing the image quality with the gold standard, the data filtering method is slightly inferior and the data weighting method yields equal quality.