Quantifying pulmonary perfusion from noncontrast computed tomography

Abstract

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 $\tau$ . 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 $\tau$ . 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.

Keywords: 4DCT; SPECT perfusion; computed tomography; deformable image registration; perfusion; ventilation.

Fig. 1

The maximum, 75th percentile, median, 25th percentile, and minimum CT‐P and SPECT‐P Spearman correlations, r_s across all 30 test cases (PE and Lung Cancer cohorts combined) as a function of the uncertainty parameter $\tau$ are plotted. The median correlation values remain relatively constant across the parameter sweep.

Fig. 2

The median CT‐P and SPECT‐P Spearman correlations, r_s across the 15 patients in the PE cohort (PE, blue) and Lung Cancer cohort (LC, green) as a function of the uncertainty parameter $\tau$ are plotted. The median across all 30 test cases (PE and Lung Cancer cohorts combined) is also provided (All, red). The median correlation curve for each cohort shows that for smaller $\tau$ (high certainty, low resolution) correlation with SPECT‐P is lower, indicating insufficient resolution in the subregional measurements to accurately describe spatial variations in pulmonary function. A similar, though not as pronounced, drop in correlation is also seen for $\tau >0.095$ (low certainty, higher resolution), indicating the effects of uncertainty and more erroneous subregional mass change measurements.

Fig. 3

Axial (Left), sagittal (middle), and coronal (right) slices from the CT‐Perfusion (CTP) (top row) and SPECT Perfusion (bottom row) images for test case 5 in the PE cohort (Table I). The Spearman correlation between the spatially aligned CTP and SPECT‐Perfusion values is 0.70. For visualization, the intensity values within each image were converted to percentile values (color scale). Visually, there is good correlation between CT‐P and SPECT‐P.

Fig. 4

Axial (Left), sagittal (middle), and coronal (right) slices from the CT‐Perfusion (CTP) (top row) and SPECT Perfusion (bottom row) images for test case 11 in the Lung Cancer cohort (Table I). The Spearman correlation between the spatially aligned CTP and SPECT‐Perfusion values is 0.70. For visualization, the intensity values within each image were converted to percentile values (color scale). Both CTP and SPECT‐P show decreased perfusion in the patient’s left lower lobe.

Fig. 5

Corresponding coronal slices from the 4DCT maximum inhale phase (left), CT‐Perfusion image superimposed on the maximum exhale phase (middle), and the SPECT‐perfusion (right) images for case 1 in the lung cancer cohort from Table I. For visualization, the intensity values within each image were converted to percentile values (color scale). The inhale image possesses a phase‐bin artifact (blue arrow), which erroneously elevates the estimated magnitude mass change.

Fig. 6

Coronal images from the SPECT‐Perfusion (left), CT‐Perfusion (middle) and IJF CT‐ventilation (right) images for PE case 6 (Table I). For visualization, the intensity values within each image were converted to percentile values (color scale). The red arrows indicate a “wedge” perfusion defect in a region of preserved ventilation on the CT‐ventilation image, indicating perfusion/ventilation mismatch that is characteristic of PE.