Sheng Ke, Gou Shuiping, Wu Jiaolong, Qi Sharon X
Department of Radiation Oncology, University of California, Los Angeles, California 90095.
Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Xidian University, Xi'An 710071, China.
Med Phys. 2014 Oct;41(10):101916. doi: 10.1118/1.4894714.
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 denoising method and texture enhancement to recover the soft tissue information in MVCT (DeTECT).
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.
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.
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.
在螺旋断层放疗（TomoTherapy）治疗中，兆伏级锥形束CT（MVCT）图像已被用于根据骨骼解剖结构对患者进行定位，但由于光电相互作用成分不显著以及兆伏级X射线探测器量子效率低导致的噪声存在，其在软组织配准、轮廓勾画和自适应放射治疗方面的效用有限。使用稀疏正则化器的代数重建以及局部去噪方法并未显著提高软组织的清晰度。作者旨在利用非局部均值去噪方法和纹理增强来恢复MVCT中的软组织信息（DeTECT）。
采用三维块匹配（BM3D）算法来降低噪声，同时保留MVCT图像的纹理信息。在图像去噪之后，创建一个显著性图以进一步增强低对比度结构的视觉清晰度。在本研究中，BM3D和显著性图被应用于CT成像质量体模、一名头颈部患者以及四名前列腺癌患者的MVCT图像。经过这些步骤后，对对比度噪声比（CNR）进行量化。
通过应用BM3D去噪和显著性图，后处理的MVCT图像在不影响分辨率的情况下，成像对比度有显著改善。对于头颈部患者，原始MVCT图像中在颈动脉间隙难以看到的淋巴结和静脉在DeTECT中变得清晰可见。对于前列腺癌患者，原始MVCT中膀胱和前列腺之间模糊的边界变得清晰。体模低对比度插入物的CNR分别从1.48和3.8提高到13.67和16.17。对于头颈部患者，两个感兴趣区域的CNR分别从1.5和3.17提高到3.14和15.76。对于四名前列腺癌患者，DeTECT还将前列腺的CNR从0.13提高到1.46。结果明显优于使用各向异性扩散的局部去噪方法。
作者表明，使用非局部均值三维块匹配方法结合显著性图从有噪声的MVCT图像中提取更多软组织对比度信息是可行的，揭示了人类观察者原本无法察觉的信息。
