Technical Note: On the spatial correlation between robust CT‐ventilation methods and SPECT ventilation. Issue 11 (17th October 2020)

• Record Type: Journal Article
• Authors: Castillo, Edward; Castillo, Richard; Vinogradskiy, Yevgeniy; Nair, Girish; Grills, Inga; Guerrero, Thomas; Stevens, Craig
• Is Part Of: Medical physics. Volume 47:Issue 11(2020)
• Page Start: 5731
• Page End: 5738
• Publication Date: 2020-10-17
• Subjects: 4DCT -- computed tomography -- deformable image registration -- SPECT -- ventilation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14511
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: 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 . 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 = 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 = 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 ∈ [ 0.03, 0.15 ], indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.