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.

Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called "artifact suppressed dictionary learning (ASDL)." In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries. Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems.

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Minimal path techniques can efficiently extract geometrically curve-like structures by finding the path with minimal accumulated cost between two given endpoints. Though having found wide practical applications (e.g., line identification, crack detection, and vascular centerline extraction), minimal path techniques suffer from some notable problems. The first one is that they require setting two endpoints for each line to be extracted (endpoint problem). The second one is that the connection might fail when the geodesic distance between the two points is much shorter than the desirable minimal path (shortcut problem). In addition, when connecting two distant points, the minimal path connection might become inefficient as the accumulated cost increases over the propagation and results in leakage into some non-feature regions near the starting point (accumulation problem). To address these problems, this paper proposes an approach termed minimal path propagation with backtracking. We found that the information in the process of backtracking from reached points can be well utilized to overcome the above problems and improve the extraction performance. The whole algorithm is robust to parameter setting and allows a coarse setting of the starting point. Extensive experiments with both simulated and realistic data are performed to validate the performance of the proposed method.