Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach

Yim, Younghee; Hong, Helen; Shin, Yeong Gil

doi:10.1118/1.3460316

Cited by 15 publications

(18 citation statements)

References 36 publications

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“…To correct positional differences and local deformation of the lungs, the lungs on WO images were globally aligned to the WI image using affine registration based on narrow-band distance propagation, and affine-transformed lungs were nonlinearly deformed by a demon algorithm using a combined gradient force and active cells (21). Then, each pixel in the WI and WO images was assigned to one of the three attenuation types through Equation 1 (below).…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

et al. 2014

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ObjectiveTo evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD).Materials and MethodsThirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics.ResultsComputer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map.ConclusionOur proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation.

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Section: Methodsmentioning

confidence: 99%

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

et al. 2014

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“…We note specifically here that the 4D-CT registration [20][21][22][23] and the follow-up registration are two different tasks. The chest CT examinations involved in the 4D-CT registration typically have many similar characteristics (e.g., time gap in scanning, dose, image quality, reconstruction resolution, and scanning pose) and their major difference lies in the lung shape as a result of different respiratory states.…”

Section: Discussionmentioning

confidence: 99%

“…This may explain why an affine registration is typically used as an initialization procedure for the nonrigid models in 4D-CT registration. 20 The nonrigid model may work well only if they can robustly avoid being trapped in local minima.…”

Section: Discussionmentioning

confidence: 99%

“…The chest CT examinations involved in the 4D-CT registration typically have many similar characteristics (e.g., time gap in scanning, dose, image quality, reconstruction resolution, and scanning pose) and their major difference lies in the lung shape as a result of different respiratory states. [20][21][22][23] Also, the examinations in 4D-CT registration are typically acquired throughout a respiratory cycle. In contrast, the chest CT examinations involved in the follow-up registration may vary significantly in the above mentioned aspects, and they are typically acquired with a long time gap.…”

Section: Discussionmentioning

confidence: 99%

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Pulmonary nodule registration: Rigid or nonrigid?

Wilson

Tan

et al. 2011

Medical Physics

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Purpose: The primary aim of this study is to investigate the performance difference of rigid and nonrigid registration schemes in matching corresponding pulmonary nodules depicted on sequential chest computed tomography (CT) examinations. Methods: A gradient descent based rigid registration algorithm with scaling was developed and it handled the involved geometric transformations (i.e., translation, rescaling, shearing, and rotation) separately instead of optimizing them in a single pass. Given two lung CT examinations, the scaling and translation parameters were simply estimated from the lung volume dimensions (e.g., size and mass center), while the rotation parameters were optimized progressively using gradient descent. To investigate the performance difference of rigid and nonrigid schemes in pulmonary nodule registration, the well-known nonrigid Demons algorithm was implemented and tested along with the developed schemes against 60 diverse low-dose clinical lung CT examinations with average 2-yr follow-up scans. A verified cancer and its correspondence in the follow-up scan as well as their spatial locations (mass center) were identified in each examination. In addition to the computational efficiency, the accuracy of these registration procedures was assessed by computing the Euclidean distances between the corresponding nodules after the registration. To demonstrate the advantage of the developed algorithm, the authors also implemented a fast iterative closest point (ICP) based rigid algorithm and compared their performance. Results: Our experiments on the collected chest CT examinations showed that the nodule registration errors in 3D Euclidean distance for the developed rigid affine approach, the traditional ICP algorithm, and the refining nonrigid Demons algorithm were 9.6, 9.8, and 10.0 mm, respectively, and the corresponding computational costs in time were 5, 300, and 55 s, respectively. Conclusions: A rigid solution may be preferred in practice for the pulmonary nodule registration in longitudinal studies because of its relatively high efficiency and sufficient accuracy for the clinical need.

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“…Yin et al,31 suggesting a variant of SSD cost function (SSTVD), reported registration accuracy ranging from 0.56 to 1.90 mm. Yim et al,18 using an endinhale end-exhale protocol, reported an accuracy of 2.8 mm. Hong et al9 using end-inspiration protocol for lung nodules F.…”

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confidence: 98%

Selecting registration schemes in case of interstitial lung disease follow‐up in CT

et al. 2015

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Selected registration schemes in case of ILD CT follow-up analysis indicate the significance of adaptive stochastic gradient descent optimizer, as well as the importance of combined rigid and nonrigid schemes providing high accuracy and time efficiency. The selected optimal deformable registration schemes are equivalent in terms of their accuracy and thus compatible in terms of their clinical outcome.

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Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach

Cited by 15 publications

References 36 publications

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

Pulmonary nodule registration: Rigid or nonrigid?

Selecting registration schemes in case of interstitial lung disease follow‐up in CT

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