Iterative reconstruction for CT perfusion with a prior‐image induced hybrid nonlocal means regularization: Phantom studies. Issue 4 (14th March 2016)

• Record Type: Journal Article
• Authors: Li, Bin; Lyu, Qingwen; Ma, Jianhua; Wang, Jing
• Is Part Of: Medical physics. Volume 43:Issue 4(2016)
• Page Start: 1688
• Page End: 1699
• Publication Date: 2016-03-14
• Subjects: computerised tomography -- Gaussian processes -- image reconstruction -- iterative methods -- least mean squares methods -- medical image processing -- phantoms; Computed tomography -- Reconstruction; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; CT perfusion -- low‐dose -- hybrid nonlocal means regularization -- statistical image reconstruction; Computed tomography -- Image reconstruction -- Medical image reconstruction -- Medical image noise -- Brain -- X‐ray detectors -- Image sensors -- Haemodynamics; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4943380
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: In computed tomography perfusion (CTP) imaging, an initial phase CT acquired with a high‐dose protocol can be used to improve the image quality of later phase CT acquired with a low‐dose protocol. For dynamic regions, signals in the later low‐dose CT may not be completely recovered if the initial CT heavily regularizes the iterative reconstruction process. The authors propose a hybrid nonlocal means (hNLM) regularization model for iterative reconstruction of low‐dose CTP to overcome the limitation of the conventional prior‐image induced penalty. Methods: The hybrid penalty was constructed by combining the NLM of the initial phase high‐dose CT in the stationary region and later phase low‐dose CT in the dynamic region. The stationary and dynamic regions were determined by the similarity between the initial high‐dose scan and later low‐dose scan. The similarity was defined as a Gaussian kernel‐based distance between the patch‐window of the same pixel in the two scans, and its measurement was then used to weigh the influence of the initial high‐dose CT. For regions with high similarity (e.g., stationary region), initial high‐dose CT played a dominant role for regularizing the solution. For regions with low similarity (e.g., dynamic region), the regularization relied on a low‐dose scan itself. This new hNLM penalty was incorporated into the penalized weighted least‐squares (PWLS) for CTP reconstruction. Digital and physical phantom studies were performed to evaluate the PWLS‐hNLM algorithm. Results: Both phantom studies showed that the PWLS‐hNLM algorithm is superior to the conventional prior‐image induced penalty term without considering the signal changes within the dynamic region. In the dynamic region of the Catphan phantom, the reconstruction error measured by root mean square error was reduced by 42.9% in PWLS‐hNLM reconstructed image. Conclusions: The PWLS‐hNLM algorithm can effectively use the initial high‐dose CT to reconstruct low‐dose CTP in the stationary region while reducing its influence in the dynamic region.