Geometrical force constraint method for vessel and x-ray angiogram simulation

A novel method of micro-tomography geometric angle calibration with random phantom

The objective of this study is to develop and test the feasibility of applying a machine learning method for geometry calibration of angles in micro-tomography systems. Increasing importance of micro-tomography systems are manifested with escalating applications in various scenarios including but not limited to oral and maxillofacial surgery, vascular and intervention radiology, among other specific applications for purposes of diagnosis and treatments planning. There is possibility, however, actual pathology is confused by artifact of tissue structures after volume reconstruction as a result of CT construction errors. A Kernel Ridge Regression algorithm for micro-tomography geometry estimation and its corresponding phantom is developed and tested in this study. Several projection images of a rotating Random Phantom of some steel ball bearings in an unknown geometry with gantry angle information were utilized to calibrate both in-plane and out-plane rotation of the detector. The described method can also be expanded to calibrate other parameters of CT construction effortlessly. Using computer simulation, the study results validated that geometry parameters of micro-tomography system were accurately calibrated.