Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

Abstract

Blood-brain-barrier permeability (BBBP) measurements extracted from the perfusion computed tomography (PCT) using the Patlak model can be a valuable indicator to predict hemorrhagic transformation in patients with acute stroke. Unfortunately, the standard Patlak model based PCT requires excessive radiation exposure, which raised attention on radiation safety. Minimizing radiation dose is of high value in clinical practice but can degrade the image quality due to the introduced severe noise. The purpose of this work is to construct high quality BBBP maps from low-dose PCT data by using the brain structural similarity between different individuals and the relations between the high- and low-dose maps. The proposed sparse high-dose induced (shd-Patlak) model performs by building a high-dose induced prior for the Patlak model with a set of location adaptive dictionaries, followed by an optimized estimation of BBBP map with the prior regularized Patlak model. Evaluation with the simulated low-dose clinical brain PCT datasets clearly demonstrate that the shd-Patlak model can achieve more significant gains than the standard Patlak model with improved visual quality, higher fidelity to the gold standard and more accurate details for clinical analysis.

Computational Health Informatics in the Big Data Age

“…Sparse representation has been effective in medical image denoising and fusion using group-wise sparsity ] and image reconstruction [Xu et al 2012;Gholipour et al 2010]. Recently, coupled with dictionary learning, Fang et al [2013] restored the hemodynamic maps in the low-dose computed tomography perfusion by learning a compact dictionary from the high-dose data, with improved accuracy and clinical value using tissue-specific dictionaries ] and applying to various types of medical images [Fang et al 2014]. The sparsity property in the transformed domain has also been important in restoring the medical information by combining with the physiological models ].…”

Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images

“…This important property has been widely used in the communities of medical imaging, computer vision, multimedia and signal processing. It has been successfully applied to practical applications of shape modelling [25], image segmentation [26][27][28], image reconstruction [29,30], motion analysis [31], bias correction [32,33], image registration [34], image retrieval [35,36] and deconvolution [37,38] in the fields of medial imaging and medical image analysis. In addition, it has also been used in a large variety of applications in the field of computer vision, including face recognition [39], image restoration [40], image denoising, deblurring, superresolution and object recognition [41][42][43][44][45][46][47][48].…”

Classification of multiple sclerosis lesions using adaptive dictionary learning

“…brain network model [11] and predicting cognitive data from medical images [12]. In addition, the dictionary learning framework has been used in deformable segmentation [13], image fusion [14], super-resolution analysis [15], denoising [16,17], deconvolution of low-dose computed tomography perfusion [18,19] and low-dose blood-brain barrier permeability quantification [20]. In each of these applications, the dictionaries are learned from the underlying data so that they are better suited for representation of the signal of interest.…”