Location registration and recognition (LRR) for serial analysis of nodules in lung CT scans

Abstract 

In the clinical workflow for lung cancer management, the comparison of nodules between CT scans from subsequent visits by a patient is necessary for timely classification of pulmonary nodules into benign and malignant and for analyzing nodule growth and response to therapy. The algorithm described in this paper takes (a) two temporally-separated CT scans, and , and (b) a series of nodule locations in , and for each location it produces an affine transformation that maps the locations and their immediate neighborhoods from to . It does this without deformable registration and without initialization by global affine registration. Requiring the nodule locations to be specified in only one volume provides the clinician more flexibility in investigating the condition of the lung. The algorithm uses a combination of feature extraction, indexing, refinement, and decision processes. Together, these processes essentially “recognize” the neighborhoods. We show on lung CT scans that our technique works at near interactive speed and that the median alignment error of 134 nodules is 1.70mm compared to the error 2.14mm of the Diffeomorphic Demons algorithm, and to the error 3.57mm of the global nodule registration with local refinement. We demonstrate on the alignment of 250 nodules, that the algorithm is robust to changes caused by cancer progression and differences in breathing states, scanning procedures, and patient positioning. Our algorithm may be used both for diagnosis and treatment monitoring of lung cancer. Because of the generic design of the algorithm, it might also be used in other applications that require fast and accurate mapping of regions.

Keywords: Pulmonary nodule, Location registration, Location recognition, Alignment verification, Keypoint matching