Aims and objectives
To perform lung image registration for the reduction of misregistration artifacts on three-dimensional (3D) temporal subtraction of chest computed tomography (CT) images to enhance temporal changes in lung lesions and to evaluate these changes after deformable image registration using diffeomorphic transformation models.
Methods and materials
Our institutional review board approved this study and waived the informed consent requirement.
Chest CT images of 10 patients were obtained by a radiologist after a search of the radiology database.
Each patient underwent CT imaging at baseline and during follow-up (1–12 months apart).
Each scan was obtained at full inspiration using a 16-slice MDCT scanner.
The images were acquired using the following parameters: volume size,
512 × 512 × slices; slice thickness,
5 mm; X- and Y-direction pixel spacing,
The average registration runtime was 2 h in MI-CC-SyN and 40 min in MI-demons-SyN implementations in 10 cases.
Quantitative evaluation: Landmark-based accuracy evaluation
The landmark feature points (128–292 points) were detected in the left and right lung in 10 cases.
The average distance and standard deviation (SD) of landmark feature points of the previous image and the corresponding current image (n = 10) as a reference standard for the accurate evaluation of diffeomorphic transformation was −0.42 mm in LR (left-right) (range,
−19.91 to 36.18 mm),...
To enhance the ability to detect interval changes in subtle lung lesions by using a subtraction image,
we used MI lung mask affine mapping combined with CC lung tissue greedy SyN mapping to accurately register temporal serial chest CT images.
The obtained subtraction images clearly showed significantly decreased misregistration artifacts and enhanced lesions and nodules in the lung volume.
the lesions maintained original morphology after registration.
These results indicate that MI lung mask affine mapping combined with CC lung tissue diffeomorphic mapping (greedy SyN)...
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