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Denoised and texture enhanced MVCT to improve soft tissue conspicuity
Author(s) -
Sheng Ke,
Gou Shuiping,
Wu Jiaolong,
Qi Sharon X.
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4894714
Subject(s) - tomotherapy , imaging phantom , artificial intelligence , computer vision , noise reduction , contrast (vision) , computer science , medicine , nuclear medicine , radiology , radiation therapy
Purpose: MVCT images have been used in TomoTherapy treatment to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation, and adaptive radiation therapy is limited due to insignificant photoelectric interaction components and the presence of noise resulting from low detector quantum efficiency of megavoltage x‐rays. Algebraic reconstruction with sparsity regularizers as well as local denoising methods has not significantly improved the soft tissue conspicuity. The authors aim to utilize a nonlocal means de noising method and t exture e nhancement to recover the soft tissue information in MV CT (DeTECT).Methods: A block matching 3D (BM3D) algorithm was adapted to reduce the noise while keeping the texture information of the MVCT images. Following imaging denoising, a saliency map was created to further enhance visual conspicuity of low contrast structures. In this study, BM3D and saliency maps were applied to MVCT images of a CT imaging quality phantom, a head and neck, and four prostate patients. Following these steps, the contrast‐to‐noise ratios (CNRs) were quantified.Results: By applying BM3D denoising and saliency map, postprocessed MVCT images show remarkable improvements in imaging contrast without compromising resolution. For the head and neck patient, the difficult‐to‐see lymph nodes and vein in the carotid space in the original MVCT image became conspicuous in DeTECT. For the prostate patients, the ambiguous boundary between the bladder and the prostate in the original MVCT was clarified. The CNRs of phantom low contrast inserts were improved from 1.48 and 3.8 to 13.67 and 16.17, respectively. The CNRs of two regions‐of‐interest were improved from 1.5 and 3.17 to 3.14 and 15.76, respectively, for the head and neck patient. DeTECT also increased the CNR of prostate from 0.13 to 1.46 for the four prostate patients. The results are substantially better than a local denoising method using anisotropic diffusion.Conclusions: The authors showed that it is feasible to extract more soft tissue contrast information from the noisy MVCT images using a nonlocal means 3D block matching method in combination with saliency maps, revealing information that was originally unperceivable to human observers.