Robust CT ventilation from the integral formulation of the Jacobian

Castillo, Edward M.; Castillo, Richard; Vinogradskiy, Yevgeniy; Dougherty, M. K.; Solis, David; Myziuk, Nicholas; Thompson, Andrew; Guerra, Rudy; Nair, Girish B.; Guerrero, Thomas

doi:10.1002/mp.13453

Cited by 29 publications

(51 citation statements)

References 39 publications

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“…In another work, Kipritidis et al 20 propose to calculate ventilation by a direct scaling of the HU values, bypassing the need for a DIR solution. Castillo et al 22 have proposed an alternative to the Jacobian-based approach using a mass conserving point cloud density function. Neural networks have also been proposed as a means of generating ventilation images.…”

Section: Calculation Methodologies Of Ct-based Ventilation Imagingmentioning

confidence: 99%

“…Studies are underway to address the challenges of calculating ventilation information from 4D CBCT images. 22,52 In terms of treatment planning, researchers are employing knowledge-based treatment planning approaches to potentially improve the quality of functional avoidance radiotherapy plans. 67 CT-ventilation based functional avoidance is also being explored retrospectively for use in thoracic proton radiotherapy.…”

Section: Future Work and Summarymentioning

confidence: 99%

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CT-based ventilation imaging in radiation oncology

Vinogradskiy

2019

BJR|Open

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A form of lung function imaging is emerging that uses phase-resolved four-dimensional CT (4DCT or breath-hold CT) images along with image processing techniques to generate lung function maps that provide a surrogate of lung ventilation. CT-based ventilation (referred to as CT-ventilation) research has gained momentum in Radiation Oncology because many lung cancer patients undergo four-dimensional CT simulation as part of the standard treatment planning process. Therefore, generating CT-ventilation images provides functional information without burdening the patient with an extra imaging procedure. CT-ventilation has progressed from an image processing calculation methodology, to validation efforts, to retrospective demonstration of clinical utility in Radiation Oncology. In particular, CT-ventilation has been proposed for two main clinical applications: functional avoidance radiation therapy and thoracic dose–response assessment. The idea of functional avoidance radiation therapy is to preferentially spare functional portions of the lung (as measured by CT-ventilation) during radiation therapy with the hypothesis that reducing dose to functional portions of the lung will lead to reduced rates of radiation-related thoracic toxicity. The idea of imaging-based dose–response assessment is to evaluate pre- to post-treatment CT-ventilation-based imaging changes. The hypothesis is that early, imaging-change-based response can be an early predictor of subsequent thoracic toxicity. Based on the retrospective evidence, the clinical applications of CT-ventilation have progressed from the retrospective setting to on-going prospective clinical trials. This review will cover basic CT-ventilation calculation methodologies, validation efforts, presentation of clinical applications, summarize on-going clinical trials, review potential uncertainties and shortcomings of CT-ventilation, and discuss future directions of CT-ventilation research.

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Section: Calculation Methodologies Of Ct-based Ventilation Imagingmentioning

confidence: 99%

Section: Future Work and Summarymentioning

confidence: 99%

CT-based ventilation imaging in radiation oncology

Vinogradskiy

2019

BJR|Open

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“…This property was derived and demonstrated numerically in our previous studies. 10,11 Considering the correlations reported in Table I, robust ventilation algorithms provide a numerical framework that (a) is reproducible with respect to image processing pipeline and (b) generates results consistent with SPECT ventilation. However, the framework does not guard against 4DCT acquisition artifacts, which can lead to substantial differences between DIR solutions generated from competing methods.…”

Section: Resultsmentioning

confidence: 98%

“…As previously described in detail, the uncertainties in the Eqs. () and () measurements can be characterized using the standard errors of the corresponding sample means 10,11 . For IJF, it was shown that with 95% probability

\frac{(||, vol ()(, ϕ ()(, Ω)) ‐ H)}{H} \leq τ, \forall H \geq H^{*},

where the minimum hit count,

H^{*}

, depends on

M

and

τ

is a specified tolerance.…”

Section: Methodsmentioning

confidence: 99%

“…8,9 We recently described two new CT-ventilation algorithms designed to address reproducibility issues previously described in the literature [7][8][9] : the intensity-based mass conserving volume change (MCVC) method 10 and the transformation-based Integrated Jacobian Formulation (IJF) method. 11 As opposed to traditional approaches, MCVC and IJF estimate the Jacobian factor of the DIR transformation from a series of spatially corresponding lung subregions. The numerical uncertainty in the subregional volume change measurements is modeled with Gaussian statistics, which allows for their uncertainty to be characterized and, consequently, controlled through the definition of an uncertainty tolerance parameter.…”

Section: Introductionmentioning

confidence: 99%

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Technical Note: On the spatial correlation between robust CT‐ventilation methods and SPECT ventilation

Castillo

Vinogradskiy

et al. 2020

Medical Physics

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The computed tomography (CT)-derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion-induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT-ventilation images and single photon emission CT-ventilation (SPECT-V). Methods: Previously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, τ, was conducted using the 4DCT and SPECT-V images acquired for 15 non-small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT-ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range 0:01, 0:25 ½. Voxel-wise Spearman correlation between the SPECT-V and the resulting CT-ventilation images was computed. Results: The median correlations between MCVC and SPECT-V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT-V ranged between 0.79 and 0.82. For the optimal IJF tolerance τ ¼ 0:07, the IJF and SPECT-V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance τ ¼ 0:03, the MCVC and SPECT-V correlations across all 15 test cases ranged between −0.06 and 0.84. Conclusion: The reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT-V, with the transformation-based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for τ ∈ ½0:03, 0:15, indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.

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Robust HU‐based CT ventilation from an integrated mass conservation formulation

2019

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Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high-intensity vessel segmentation, and a post-processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel. Purpose: We introduce the novel mass-conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post-processing Gaussian smoothing. Methods: Subregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional CTs (4DCTs) available on www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation-based ventilation method. Results: The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11). Conclusion: While HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vascu...

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Robust CT ventilation from the integral formulation of the Jacobian

Cited by 29 publications

References 39 publications

CT-based ventilation imaging in radiation oncology

CT-based ventilation imaging in radiation oncology

Technical Note: On the spatial correlation between robust CT‐ventilation methods and SPECT ventilation

Robust HU‐based CT ventilation from an integrated mass conservation formulation

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