Correlation of C-arm CT acquired parenchymal blood volume (PBV) with 99mTc-macroaggregated albumin (MAA) SPECT/CT for radioembolization work-up

Accuracy and reproducibility of a cone beam CT-based virtual parenchymal perfusion algorithm in the prediction of SPECT/CT anatomical and volumetric results during the planification of radioembolization for HCC

OBJECTIVES

To evaluate anatomical and volumetric predictability of a cone beam computed tomography (CBCT)-based virtual parenchymal perfusion (VPP) software for the single-photon-emission computed tomography (SPECT)/CT imaging results during the work-up for transarterial radioembolization (TARE) procedure in patients with hepatocellular carcinoma (HCC).

METHODS

VPP was evaluated retrospectively on CBCT data of patients treated by TARE for HCC. ^99mTc macroaggregated albumin particles (^99mTc-MAA) uptake territories on work-up SPECT/CT was used as ground truth for the evaluation. Semi-quantitative evaluation consisted of the ranking of visual consistency of the parenchymal enhancement and portal vein tumoral involvement on VPP and ^99mTc-MAA SPECT/CT, using a three-rank scale and two-rank scale, respectively. Inter-reader agreement was evaluated using a kappa coefficient. Quantitative evaluation included absolute volume error calculation and Pearson correlation between volumes enhanced territories on VPP and ^99mTc-MAA SPECT/CT.

RESULTS

Fifty-two CBCTs were performed in 33 included patients. Semi-quantitative evaluation showed a good concordance between actual ^99mTc-MAA uptake and the virtual enhanced territories in 73% and 75% of cases; a mild concordance in 12% and 10% and a poor concordance in 15%, for the two readers. Kappa coefficient was 0.86. Portal vein involvement evaluation showed a good concordance in 58.3% and 66.7% for the two readers, respectively, with a kappa coefficient of 0.82. Quantitative evaluation showed a volume error of 0.46 ± 0.78 mL [0.01-3.55], and Pearson R² factor at 0.75 with a p value < 0.01.

CONCLUSION

CBCT-based VPP software is accurate and reliable to predict ^99mTc-MAA SPECT/CT anatomical and volumetric results in HCC patients during TARE.

KEY POINTS

• Virtual parenchymal perfusion (VPP) software is accurate and reliable in the prediction of ^99mTc-MAA SPECT volumetric and targeting results in HCC patients during transarterial radioembolization (TARE). • VPP software may be used per-operatively to optimize the microcatheter position for ⁹⁰Y infusion allowing precise tumor targeting while preserving non-tumoral parenchyma. • Post-operatively, VPP software may allow an accurate estimation of the perfused volume by each arterial branch and, thus, a precise ⁹⁰Y dosimetry for TARE procedures.

AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography

(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Body-Mass-Index”, and “Mode” to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.