A Sisniega1, J W Stayman1, Q Cao1, J Yorkston2, J H Siewerdsen3, W Zbijewski1. 1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA. 2. Carestream Health, Rochester, NY USA. 3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA; Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD USA.
Abstract
PURPOSE: Cone-beam CT (CBCT) of the extremities provides high spatial resolution, but its quantitative accuracy may be challenged by involuntary sub-mm patient motion that cannot be eliminated with simple means of external immobilization. We investigate a two-step iterative motion compensation based on a multi-component metric of image sharpness. METHODS: Motion is considered with respect to locally rigid motion within a particular region of interest, and the method supports application to multiple locally rigid regions. Motion is estimated by maximizing a cost function with three components: a gradient metric encouraging image sharpness, an entropy term that favors high contrast and penalizes streaks, and a penalty term encouraging smooth motion. Motion compensation involved initial coarse estimation of gross motion followed by estimation of fine-scale displacements using high resolution reconstructions. The method was evaluated in simulations with synthetic motion (1-4 mm) applied to a wrist volume obtained on a CMOS-based CBCT testbench. Structural similarity index (SSIM) quantified the agreement between motion-compensated and static data. The algorithm was also tested on a motion contaminated patient scan from dedicated extremities CBCT. RESULTS: Excellent correction was achieved for the investigated range of displacements, indicated by good visual agreement with the static data. 10-15% improvement in SSIM was attained for 2-4 mm motions. The compensation was robust against increasing motion (4% decrease in SSIM across the investigated range, compared to 14% with no compensation). Consistent performance was achieved across a range of noise levels. Significant mitigation of artifacts was shown in patient data. CONCLUSION: The results indicate feasibility of image-based motion correction in extremities CBCT without the need for a priori motion models, external trackers, or fiducials.
PURPOSE: Cone-beam CT (CBCT) of the extremities provides high spatial resolution, but its quantitative accuracy may be challenged by involuntary sub-mm patient motion that cannot be eliminated with simple means of external immobilization. We investigate a two-step iterative motion compensation based on a multi-component metric of image sharpness. METHODS: Motion is considered with respect to locally rigid motion within a particular region of interest, and the method supports application to multiple locally rigid regions. Motion is estimated by maximizing a cost function with three components: a gradient metric encouraging image sharpness, an entropy term that favors high contrast and penalizes streaks, and a penalty term encouraging smooth motion. Motion compensation involved initial coarse estimation of gross motion followed by estimation of fine-scale displacements using high resolution reconstructions. The method was evaluated in simulations with synthetic motion (1-4 mm) applied to a wrist volume obtained on a CMOS-based CBCT testbench. Structural similarity index (SSIM) quantified the agreement between motion-compensated and static data. The algorithm was also tested on a motion contaminated patient scan from dedicated extremities CBCT. RESULTS: Excellent correction was achieved for the investigated range of displacements, indicated by good visual agreement with the static data. 10-15% improvement in SSIM was attained for 2-4 mm motions. The compensation was robust against increasing motion (4% decrease in SSIM across the investigated range, compared to 14% with no compensation). Consistent performance was achieved across a range of noise levels. Significant mitigation of artifacts was shown in patient data. CONCLUSION: The results indicate feasibility of image-based motion correction in extremities CBCT without the need for a priori motion models, external trackers, or fiducials.
Authors: Jang-Hwan Choi; Andreas Maier; Andreas Keil; Saikat Pal; Emily J McWalter; Gary S Beaupré; Garry E Gold; Rebecca Fahrig Journal: Med Phys Date: 2014-06 Impact factor: 4.071
Authors: W Zbijewski; P De Jean; P Prakash; Y Ding; J W Stayman; N Packard; R Senn; D Yang; J Yorkston; A Machado; J A Carrino; J H Siewerdsen Journal: Med Phys Date: 2011-08 Impact factor: 4.071
Authors: Qian Cao; Alejandro Sisniega; Michael Brehler; J Webster Stayman; John Yorkston; Jeffrey H Siewerdsen; Wojciech Zbijewski Journal: Med Phys Date: 2017-11-27 Impact factor: 4.071
Authors: Adam Wang; Ian Cunningham; Mats Danielsson; Rebecca Fahrig; Thomas Flohr; Christoph Hoeschen; Frederic Noo; John M Sabol; Jeffrey H Siewerdsen; Anders Tingberg; John Yorkston; Wei Zhao; Ehsan Samei Journal: J Med Imaging (Bellingham) Date: 2022-03-16