Quantifying pulmonary perfusion from noncontrast computed tomography

Simple item page
dc.citation.firstpage1804en_US
dc.citation.issueNumber4en_US
dc.citation.journalTitleMedical Physicsen_US
dc.citation.lastpage1814en_US
dc.citation.volumeNumber48en_US
dc.contributor.authorCastillo, Edwarden_US
dc.contributor.authorNair, Girishen_US
dc.contributor.authorTurner‐Lawrence, Danielleen_US
dc.contributor.authorMyziuk, Nicholasen_US
dc.contributor.authorEmerson, Scotten_US
dc.contributor.authorAl‐Katib, Sayfen_US
dc.contributor.authorWestergaard, Sarahen_US
dc.contributor.authorCastillo, Richarden_US
dc.contributor.authorVinogradskiy, Yevgeniyen_US
dc.contributor.authorQuinn, Thomasen_US
dc.contributor.authorGuerrero, Thomasen_US
dc.contributor.authorStevens, Craigen_US
dc.date.accessioned2021-06-07T20:22:43Zen_US
dc.date.available2021-06-07T20:22:43Zen_US
dc.date.issued2021en_US
dc.description.abstractPurpose: Computed tomography (CT)-derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT-Perfusion (CT-P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion. Methods: CT-Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT-P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT-P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT-ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter. Spatial Spearman correlation between single photon emission CT perfusion (SPECT-P) and the proposed CT-P method was assessed in two patient cohorts via a parameter sweep of . The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT-P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT-P and 4DCT images acquired prior to radiotherapy. For each test case, CT-P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT-P and the resulting CT-P images were computed. Results: The median correlations between CT-P and SPECT-P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT-P and SPECT-P correlations across all 30 test cases ranged between 0.02 and 0.82. A one-sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two-sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One-sample sign test was statistically significant with 96.5 % confidence interval: 0.20–0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one-sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45–0.71, P < 0.00001. Conclusion: CT-Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT-P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT-P imaging.en_US
dc.identifier.citationCastillo, Edward, Nair, Girish, Turner‐Lawrence, Danielle, et al.. "Quantifying pulmonary perfusion from noncontrast computed tomography." <i>Medical Physics,</i> 48, no. 4 (2021) Wiley: 1804-1814. https://doi.org/10.1002/mp.14792.en_US
dc.identifier.digitalmp-14792en_US
dc.identifier.doihttps://doi.org/10.1002/mp.14792en_US
dc.identifier.urihttps://hdl.handle.net/1911/110693en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleQuantifying pulmonary perfusion from noncontrast computed tomographyen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
mp-14792.pdf
Size:
1.13 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
CMOR Publications