Pulmonary blood mass dynamics on 4DCT during tidal breathing

Myziuk, Nicholas; Guerrero, Thomas; Sakthivel, G.; Solis, David; Nair, Girish B.; Guerra, Rudy; Castillo, Edward M.

doi:10.1088/1361-6560/aaff7b

Cited by 7 publications

(10 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Medical Physics, 0 (0), xxxx assumption is known to be invalid due to variations in the spatial distribution of blood mass that occur within the lungs during breathing. 19 In this study, we introduce the novel CT-Perfusion numerical method for quantifying these blood mass variations as a surrogate for pulmonary perfusion. The CT-P formulation builds upon the MCVC formulation and recovers magnitude mass change at the voxel resolution using HU-defined material density estimates and the DIR spatial mapping between the inhale and exhale geometries.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying pulmonary perfusion from noncontrast computed tomography

et al. 2021

Self Cite

View full text Add to dashboard Cite

Purpose: 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 τ. 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 τ. 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

show abstract

Section: Discussionmentioning

confidence: 99%

“…Moreover, previous studies have shown that there is an observable difference in lung tissue mass between inhale and exhale segmented lung volumes based on Eq. (1), 14,19 indicating that pulmonary blood mass dynamics can be quantified from noncontrast inhale/exhale CT image pairs.…”

Section: Introductionmentioning

confidence: 99%

Quantifying pulmonary perfusion from noncontrast computed tomography

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Accounting for motion and reconstruction artifacts is currently not possible by any numerical methodology and is typically accounted for by manually reviewing acquired 4DCTs 33 . Even though geometric artifacts will intuitively have less influence on MCVC, due to its strict reliance on CT intensities, the effects of perfusion are potentially significant 14,34 and could also contribute to the discrepancies between IJF and MCVC.…”

Section: Discussionmentioning

confidence: 99%

“…Several physiological factors contribute to lung volume change within the respiratory cycle, but like all Jacobian ventilation methods, MCVC describes the effect of all physiological factors combined. Recent work has demonstrated that there are two factors contributing to the apparent density variations between inhale and exhale CT images: 1) changes in air content and 2) changes in mass, which presumably describe variations in the spatial distribution of pulmonary blood mass induced by respiratory motion 34 . While the effect of blood mass variation was shown to be measurable, its effect on density variation was also shown to be substantially less than that of air content changes 34 .…”

Section: Discussionmentioning

confidence: 99%

“…Recent work has demonstrated that there are two factors contributing to the apparent density variations between inhale and exhale CT images: 1) changes in air content and 2) changes in mass, which presumably describe variations in the spatial distribution of pulmonary blood mass induced by respiratory motion 34 . While the effect of blood mass variation was shown to be measurable, its effect on density variation was also shown to be substantially less than that of air content changes 34 . Determining the degree to which blood mass variations effect the estimated Jacobian values is not known.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

2019

Self Cite

View full text Add to dashboard Cite

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...

show abstract

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

Castillo

Vinogradskiy

et al. 2020

Medical Physics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Pulmonary blood mass dynamics on 4DCT during tidal breathing

Cited by 7 publications

References 32 publications

Quantifying pulmonary perfusion from noncontrast computed tomography

Quantifying pulmonary perfusion from noncontrast computed tomography

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

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

Contact Info

Product

Resources

About