Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries

Low-Dose Volume-Perfusion CT of the Brain: Effects of Radiation Dose Reduction on Performance of Perfusion CT Algorithms

PURPOSE

We aimed to compare different computed tomography (CT) perfusion post-processing algorithms regarding image quality of perfusion maps from low-dose volume perfusion CT (VPCT) and their diagnostic performance regarding the detection of ischemic brain lesions.

METHODS AND MATERIALS

We included VPCT data of 21 patients with acute stroke (onset < 6h), which were acquired at 80 kV and 180 mAs. Low-dose VPCT datasets with 72 mAs (40 % of original dose) were generated using realistic low-dose simulation. Perfusion maps (cerebral blood volume (CBV); cerebral blood flow (CBF) from original and low-dose datasets were generated using two different commercially available post-processing methods: deconvolution-based method (DC) and maximum slope algorithm (MS). The resulting DC and MS perfusion maps were compared regarding perfusion values, signal-to-noise ratio (SNR) as well as image quality and diagnostic accuracy as rated by two blinded neuroradiologists.

RESULTS

Quantitative perfusion parameters highly correlated for both algorithms and both dose levels (r ≥ 0.613, p < 0.001). Regarding SNR levels and image quality of the CBV maps, no significant differences between DC and MS were found (p ≥ 0.683). Low-dose MS CBF maps yielded significantly higher SNR levels (p < 0.001) and quality scores (p = 0.014) than those of DC. Low-dose CBF and CBV maps from both DC and MS yielded high sensitivity and specificity for the detection of ischemic lesions (sensitivity ≥ 0.82, specificity ≥ 0.90).

CONCLUSION

Our results indicate that both methods produce diagnostically sufficient perfusion maps from simulated low-dose VPCT. However, MS produced CBF maps with significantly higher image quality and SNR than DC, indicating that MS might be more suitable for low-dose VPCT imaging.

Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. "Time is brain" is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

Tissue-specific sparse deconvolution for low-dose CT perfusion

Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we extend this line of work by introducing tissue-specific sparse deconvolution to preserve the subtle perfusion information in the low-contrast tissue classes by learning tissue-specific dictionaries for each tissue class, and restore the low-dose perfusion maps by joining the tissue segments reconstructed from the corresponding dictionaries. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method with the advantage of better differentiation between abnormal and normal tissue in these patients.

Towards robust deconvolution of low-dose perfusion CT: sparse perfusion deconvolution using online dictionary learning

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain.