| Autor: |
Maitree R; Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States., Perez-Carrillo GJG; Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States.; University of Arizona, Department of Radiology, Tucson, Arizona, United States., Shimony JS; Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States., Gach HM; Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States.; Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States.; Washington University School of Medicine, Department of Biomedical Engineering, St. Louis, Missouri, United States., Chundury A; Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States., Roach M; Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States., Li HH; Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States., Yang D; Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States.; Washington University School of Medicine, Department of Biomedical Engineering, St. Louis, Missouri, United States. |
| Abstrakt: |
Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography. |
