Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior

Abstract

Cerebral perfusion X-ray computed tomography (PCT) imaging, which detects and characterizes the ischemic penumbra, and assesses blood-brain barrier permeability with acute stroke or chronic cerebrovascular diseases, has been developed extensively over the past decades. However, due to its sequential scan protocol, the associated radiation dose has raised significant concerns to patients. Therefore, in this study we developed an iterative image reconstruction algorithm based on the maximum a posterior (MAP) principle to yield a clinically acceptable cerebral PCT image with lower milliampere seconds (mAs). To preserve the edges of the reconstructed image, an edge-preserving prior was designed using a normal-dose pre-contrast unenhanced scan. For simplicity, the present algorithm was termed as “MAP-ndiNLM”. Evaluations with the digital phantom and the simulated low-dose clinical brain PCT datasets clearly demonstrate that the MAP-ndiNLM method can achieve more significant gains than the existing FBP and MAP-Huber algorithms with better image noise reduction, low-contrast object detection and resolution preservation. More importantly, the MAP-ndiNLM method can yield more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps than the MAP-Huber method.

Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior

“…Mathematically, the PWLS criterion for CT image reconstruction can be rewritten as follows [10,13,40]:…”

“…To solve this problem, in this paper, similar to our previous works [13,43], an alternating minimization scheme was used to optimize (9) wherein the weights w i,j ð Þ in equation (5) can be automatically updated according to the similarity between the patch-windows in the current estimation m n (n is the iterative index) and the normaldose prior image m nd during each iteration. In summary, the present PWLS-ndiTV approach for CT image reconstruction has three main steps as follows:…”

“…To reduce the radiation dose of CT scans, many investigations have been performed including the hardware-based scanning protocols [3,4,5] and software-based image reconstruction techniques [6,7,8]. It is known that lowering the milliampere-seconds (mAs) [9,10,11,12,13] or reducing the number of projections per rotation around the body [14,15,16,17,18,19,20] is an important means for reducing radiation dose. However, the associative image quality would be unavoidably deteriorated due to the noisy or sparse-view measurements if no adequate noise control is applied in image reconstruction.…”

“…In these conditions, the previous normal-dose scanned data can be referred to as ''normal-dose prior image'' aiming to facilitate subsequent CT image reconstructions with sparse-view projection measurements [13,15,25,26,27]. For example, Chen et al proposed a prior image constrained compressed sensing (PICCS) approach for sparse-view CT image reconstruction by incorporating a prior image [15].…”

“…In this paper, based on the recent studies about sparse-view and low-dose CT image reconstructions [11,12,13,28,34,35,36,37,38,39], we propose a normal-dose image induced total variation prior (ndiTV) under the penalized weighted least-square (PWLS) criteria [10], which is referred to ''PWLS-ndiTV'' for simplicity, aiming to relieve the requirement of misalignment reduction operation of the PICCS algorithm. The novelty of the present PWLS-ndiTV approach is twofold.…”