Tensor total-variation regularized deconvolution kegularlzea ueconvolution for efficient low-dose CT perfusion

Variations of quantitative perfusion measurement on dynamic contrast enhanced CT for colorectal cancer: implication of standardized image protocol

Tumor angiogenesis is considered an important prognostic factor. With an increasing emphasis on imaging evaluation of the tumor microenvironment, dynamic contrast enhanced-computed tomography (DCE-CT) has evolved as an important functional technique in this setting. Yet many questions remain as to how and when these functional measurements should be performed for each agent and tumor type, and what quantitative models should be used in the fitting process. In this study, we evaluated the variations of perfusion measurement on DCE-CT for rectal cancer patients from (1) different tracer kinetic models, (2) different scan acquisition lengths, and (3) different scan intervals. A total of seven commonly used models were studied: the adiabatic approximation to the tissue homogeneity (AATH) model, adiabatic approximation to the homogeneity tissue with fixed transit time (AATHFT) model, the Tofts model (TM), the extended Tofts model (ETM), Patlak model, Logan model, and the model-free deconvolution method. Akaike's information criterion was used to identify the best fitting model. The interchangeability of different models was further evaluated using Bland-Altman analysis. All models gave comparable blood volume (BV) measurements except the Patlak method. While for the volume transfer constant (K_trans) estimation, AATHFT, AATH, and ETM generated reasonable agreement among each other but not for the other models. Regarding the blood flow (BF) measurement, no two models were interchangeable. In addition, the perfusion parameters were compared with four acquisition times (45, 65, 85, and 105 s) and four temporal intervals (1, 2, 3, and 4 s). No significant difference was observed in the volume transfer constant (K_trans), BV, and BF measurements when comparing data acquired over 65 s with data acquired over 105 s using any of the DCE models in this study. Yet increasing the temporal interval led to a significant overestimation of BF in the deconvolution method. In conclusion, the perfusion measurement is indeed model dependent and the image acquisition/processing technique is dependent. The radiation dose of DCE-CT was an average of 1.5-2 times an abdomen/pelvic CT, which is not insubstantial. To take the DCE-CT forward as a biomarker in oncology, prospective studies should be carefully designed with the optimal image acquisition and analysis technique.

TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-term health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the TTV framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization, and an efficient algorithm is proposed to reduce the time complexity with speedy similarity computation. Evaluations on clinical data of patients subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

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.