A Feature-based Affine Registration Method for Capturing Background Lung Tissue Deformation for Ground Glass Nodule Tracking
| Autor: | Deepak Roy Chittajallu, María Helguera, Nathan D. Cahill, Cristian A. Linte, Yehuda K. Ben-Zikri, David T. Fetzer, Marc Niethammer, Stephen R. Aylward, David A. Shrier |
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| Rok vydání: | 2023 |
| Předmět: |
medicine.diagnostic_test
Computer science Biomedical Engineering Computational Mechanics Computed tomography Nodule (medicine) respiratory system Deformation (meteorology) Tracking (particle physics) Article respiratory tract diseases Computer Science Applications medicine Feature based Radiology Nuclear Medicine and imaging sense organs Affine transformation medicine.symptom Lung tissue Biomedical engineering |
| Zdroj: | Comput Methods Biomech Biomed Eng Imaging Vis |
| ISSN: | 2168-1163 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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