Detection of time-varying structures by large deformation diffeomorphic metric mapping to aid reading of high-resolution CT images of the lung.
OBJECTIVE
To evaluate the accuracy of advanced non-linear registration of serial lung Computed Tomography (CT) images using Large Deformation Diffeomorphic Metric Mapping (LDDMM).


METHODS
FIFTEEN CASES OF LUNG CANCER WITH SERIAL LUNG CT IMAGES (INTERVAL: 62.2±26.9 days) were used. After affine transformation, three dimensional, non-linear volume registration was conducted using LDDMM with or without cascading elasticity control. Registration accuracy was evaluated by measuring the displacement of landmarks placed on vessel bifurcations for each lung segment. Subtraction images and Jacobian color maps, calculated from the transformation matrix derived from image warping, were generated, which were used to evaluate time-course changes of the tumors.


RESULTS
The average displacement of landmarks was 0.02±0.16 mm and 0.12±0.60 mm for proximal and distal landmarks after LDDMM transformation with cascading elasticity control, which was significantly smaller than 3.11±2.47 mm and 3.99±3.05 mm, respectively, after affine transformation. Emerged or vanished nodules were visualized on subtraction images, and enlarging or shrinking nodules were displayed on Jacobian maps enabled by highly accurate registration of the nodules using LDDMM. However, some residual misalignments were observed, even with non-linear transformation when substantial changes existed between the image pairs.


CONCLUSIONS
LDDMM provides accurate registration of serial lung CT images, and temporal subtraction images with Jacobian maps help radiologists to find changes in pulmonary nodules.
Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Ryo
R
Sakamoto
Department of Radiology, Kennedy Krieger Institute, Baltimore, Maryland, United States of America ; F.M. Kirby Functional Imaging Center, Kennedy Krieger Institute, Baltimore, Maryland, United States of America.
Susumu
S
Mori
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America ; Center of Imaging Science, Johns Hopkins University, Baltimore, Maryland, United States of America.
Michael I
MI
Miller
Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Tomohisa
T
Okada
Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Kaori
K
Togashi
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001331:Automation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D008875:Middle Aged; D013997:Time Factors; D014057:Tomography, X-Ray Computed