ABSTRACT
Lung nodule tracking assessment relies on cross-sectional measurements of the largest lesion profile depicted in initial and follow-up computed tomography (CT) images. However, apparent changes in nodule size assessed via simple image-based measurements may also be compromised by the effect of the background lung tissue deformation on the GGN between the initial and follow-up images, leading to erroneous conclusions about nodule changes due to disease. To compensate for the lung deformation and enable consistent nodule tracking, here we propose a feature-based affine registration method and study its performance vis-a-vis several other registration methods. We implement and test each registration method using both a lung- and a lesion-centered region of interest on ten patient CT datasets featuring twelve nodules, including both benign and malignant GGO lesions containing pure GGNs, part-solid, or solid nodules. We evaluate each registration method according to the target registration error (TRE) computed across 30 - 50 homologous fiducial landmarks surrounding the lesions and selected by expert radiologists in both the initial and follow-up patient CT images. Our results show that the proposed feature-based affine lesion-centered registration yielded a 1.1 ± 1.2 mm TRE, while a Symmetric Normalization deformable registration yielded a 1.2 ± 1.2 mm TRE, and a least-square fit registration of the 30-50 validation fiducial landmark set yielded a 1.5 ± 1.2 mm TRE. Although the deformable registration yielded a slightly higher registration accuracy than the feature-based affine registration, it is significantly more computationally efficient, eliminates the need for ambiguous segmentation of GGNs featuring ill-defined borders, and reduces the susceptibility of artificial deformations introduced by the deformable registration, which may lead to increased similarity between the registered initial and follow-up images, over-compensating for the background lung tissue deformation, and, in turn, compromising the true disease-induced nodule change assessment. We also assessed the registration qualitatively, by visual inspection of the subtraction images, and conducted a pilot pre-clinical study that showed the proposed feature-based lesion-centered affine registration effectively compensates for the background lung tissue deformation between the initial and follow-up images and also serves as a reliable baseline registration method prior to assessing lung nodule changes due to disease.
ABSTRACT
Accurate measurement of knee alignment, quantified by the hip-knee-ankle (HKA) angle (varus-valgus), serves as an essential biomarker in the diagnosis of various orthopaedic conditions and selection of appropriate therapies. Such angular deformities are assessed from standing X-ray panoramas. However, the limited field-of-view of traditional X-ray imaging systems necessitates the acquisition of several sector images to capture an individual's standing posture, and their subsequent 'stitching' to reconstruct a panoramic image. Such panoramas are typically constructed manually by an X-ray imaging technician, often using various external markers attached to the individual's clothing and visible in two adjacent sector images. To eliminate human error, user-induced variability, improve consistency and reproducibility, and reduce the time associated with the traditional manual 'stitching' protocol, here we propose an automatic panorama construction method that only relies on anatomical features reliably detected in the images, eliminating the need for any external markers or manual input from the technician. The method first performs a rough segmentation of the femur and the tibia, then the sector images are registered by evaluating a distance metric between the corresponding bones along their medial edge. The identified translations are then used to generate the standing panorama image. The method was evaluated on 95 patient image datasets from a database of X-ray images acquired across 10 clinical sites as part of the screening process for a multi-site clinical trial. The panorama reconstruction parameters yielded by the proposed method were compared to those used for the manual panorama construction, which served as gold-standard. The horizontal translation differences were 0:43 ± 1:95 mm 0:26 ± 1:43 mm for the femur and tibia respectively, while the vertical translation differences were 3:76 ± 22:35 mm and 1:85 ± 6:79 mm for the femur and tibia, respectively. Our results showed no statistically significant differences between the HKA angles measured using the automated vs. the manually generated panoramas, and also led to similar decisions with regards to the patient inclusion/exclusion in the clinical trial. Thus, the proposed method was shown to provide comparable performance to manual panorama construction, with increased efficiency, consistency and robustness.
