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Evaluating the accuracy of 4D‐ CT ventilation imaging: First comparison with Technegas SPECT ventilation
Author(s) -
HegiJohnson Fiona,
Keall Paul,
Barber Jeff,
Bui Chuong,
Kipritidis John
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.12317
Subject(s) - nuclear medicine , lung cancer , single photon emission computed tomography , medicine , image registration , ventilation (architecture) , radiation therapy , radiation treatment planning , computed tomography , radiology , physics , computer science , pathology , artificial intelligence , image (mathematics) , thermodynamics
Purpose Computed tomography ventilation imaging (CTVI) is a highly accessible functional lung imaging modality that can unlock the potential for functional avoidance in lung cancer radiation therapy. Previous attempts to validate CTVI against clinical ventilation single‐photon emission computed tomography (V‐SPECT) have been hindered by radioaerosol clumping artifacts. This work builds on those studies by performing the first comparison of CTVI with 99m Tc‐carbon (‘Technegas’), a clinical V‐SPECT modality featuring smaller radioaerosol particles with less clumping. Methods Eleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four‐dimensional CT (4DCT) scans paired with Technegas V/Q‐SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing‐induced lung density changes (CTVI DIR‐HU ), or breathing‐induced lung volume changes (CTVI DIR‐Jac ) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air‐tissue densities (CTVI HU ) and did not involve DIR. Corresponding CTVI and V‐SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer‐selected anatomic landmarks. Results Interestingly, the overall best performing method (CTVI HU ) did not involve DIR. For nondefect regions, the CTVI HU , CTVI DIR‐HU , and CTVI DIR‐Jac methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r ‐values were generally weak: 0.26 for CTVI HU , 0.18 for CTVI DIR‐HU , and −0.02 for CTVI DIR‐Jac . The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q‐SPECT scans achieved good correlations with V‐SPECT (mean r > 0.6), suggesting that the image quality of Technegas V‐SPECT was not a limiting factor in this study. Conclusions We performed a validation of CTVI using clinically available 4DCT and Technegas V/Q‐SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT.