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Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy
Author(s) -
Zhang Hao,
Ma Jianhua,
Wang Jing,
Moore William,
Liang Zhengrong
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12378
Subject(s) - image quality , iterative reconstruction , regularization (linguistics) , medicine , artificial intelligence , radiology , tomography , computer science , nuclear medicine , computer vision , pattern recognition (psychology) , image (mathematics)
Purpose Repeated computed tomography ( CT ) scans are prescribed for some clinical applications such as lung nodule surveillance. Several studies have demonstrated that incorporating a high‐quality prior image into the reconstruction of subsequent low‐dose CT ( LDCT ) acquisitions can either improve image quality or reduce data fidelity requirements. Our proposed previous normal‐dose image induced nonlocal means (ndi NLM ) regularization method for LDCT is an example of such a method. However, one major concern with prior image based methods is that they might produce false information when the prior image and the current LDCT image show different structures (for example, if a lung nodule emerges, grows, shrinks, or disappears over time). This study aims to assess the performance of the ndi NLM regularization method in situations with change in anatomy. Method We incorporated the ndi NLM regularization into the statistical image reconstruction ( SIR ) framework for reconstruction of subsequent LDCT images. Because of its patch‐based search mechanism, a rough registration between the prior image and the current LDCT image is adequate for the SIR ‐ndi NLM method. We assessed the performance of the SIR ‐ndi NLM method in lung nodule surveillance for two different scenarios: (a) the nodule was not found in a baseline exam but appears in a follow‐up LDCT scan; (b) the nodule was present in a baseline exam but disappears in a follow‐up LDCT scan. We further investigated the effect of nodule size on the performance of the SIR ‐ndi NLM method. Results We found that a relatively large search‐window (e.g., 33 × 33) should be used for the SIR ‐ndi NLM method to account for misalignment between the prior image and the current LDCT image, and to ensure that enough similar patches can be found in the prior image. With proper selection of other parameters, experimental results with two patient datasets demonstrated that the SIR ‐ndi NLM method did not miss true nodules nor introduce false nodules in the lung nodule surveillance scenarios described above. We also found that the SIR ‐ndi NLM reconstruction shows improved image quality when the prior image is similar to the current LDCT image in anatomy. These gains in image quality might appear small upon visual inspection, but they can be detected using quantitative measures. Finally, the SIR ‐ndi NLM method also performed well in ultra‐low‐dose conditions and with different nodule sizes. Conclusions This study assessed the performance of the SIR ‐ndi NLM method in situations in which the prior image and the current LDCT image show substantial anatomical differences, specifically, changes in lung nodules. The experimental results demonstrate that the SIR ‐ndi NLM method does not introduce false lung nodules nor miss true nodules, which relieves the concern that this method might produce false information. However, there is insufficient evidence that these findings will hold true for all kinds of anatomical changes.