Abstract
Objectives
To evaluate the improvement in extremity cone-beam computed tomography (CBCT) image quality in datasets with motion artifact using a motion compensation method based on maximizing image sharpness.
Methods
Following IRB approval, retrospective analysis of 308 CBCT scans of lower extremities was performed by a fellowship-trained musculoskeletal radiologist to identify images with moderate to severe motion artifact. Twenty-four scans of 22 patients (18 male, four female; mean, 32 years old, range, 21–74 years old) were chosen for inclusion. Sharp (bone) and smooth (soft tissue) reconstructions were processed using the motion compensation algorithm. Two experts rated visualization of trabecular bone, cortical bone, joint spaces, and tendon on a nine-level Likert scale with and without motion compensation (a total of 96 datasets). Visual grading characteristics (VGC) was used to quantitatively determine the difference in image quality following motion compensation. Intra-class correlation coefficient (ICC) was obtained to assess inter-observer agreement.
Results
Motion-compensated images exhibited appreciable reduction in artifacts. The observer study demonstrated the associated improvement in diagnostic quality. The fraction of cases receiving scores better than “Fair” increased from less than 10% without compensation to 40–70% following compensation, depending on the task. The area under the VGC curve was 0.75 (tendon) to 0.85 (cortical bone), confirming preference for motion compensated images. ICC values showed excellent agreement between readers before (ICC range, 0.8–0.91) and after motion compensation (ICC range, 0.92–0.97).
Conclusions
The motion compensation algorithm significantly improved the visualization of bone and soft tissue structures in extremity CBCT for cases exhibiting patient motion.
Similar content being viewed by others
Abbreviations
- CBCT:
-
Cone-beam CT
- MDCT:
-
Multi-detector CT
- HIPPA:
-
Health Insurance Portability and Accountability Act
- VIG:
-
Variance of image gradient
- ROI:
-
Region of interest
- ICS:
-
Image criteria scores
- ICC:
-
Intraclass correlation
- VGC:
-
Visual grading characteristics
References
Pugmire BS, Shailam R, Sagar P, Liu B, Li X, Palmer WE, et al. Initial clinical experience with extremity cone-beam CT of the foot and ankle in pediatric patients. AJR Am J Roentgenol. 2016;206(2):431–5.
Shakoor D, Osgood GM, Brehler M, Zbijewski W, de Cesar Netto C, Shafiq B, et al. Cone-beam CT measurements of distal tibio-fibular syndesmosis in asymptomatic uninjured ankles: does weight-bearing matter? Skeletal Radiol. 2019;48:583.
Lintz F, de Cesar Netto C, Barg A, Burssens A, Richter M; Weight Bearing CT International Study Group. Weight-bearing cone beam CT scans in the foot and ankle. EFORT Open Rev. 2018;3(5):278–86.
Osgood GM, Thawait GK, Hafezi-Nejad N, Shakoor D, Shaner A, Yorkston J, et al. Image quality of cone beam computed tomography for evaluation of extremity fractures in the presence of metal hardware: visual grading characteristics analysis. Br J Radiol. 2017;90(1073):20160539.
Demehri S, Muhit A, Zbijewski W, Stayman JW, Yorkston J, Packard N, et al. Assessment of image quality in soft tissue and bone visualization tasks for a dedicated extremity cone-beam CT system. Eur Radiol. 2015;25(6):1742–51.
Carrino JA, Al Muhit A, Zbijewski W, Thawait GK, Stayman JW, Packard N, et al. Dedicated cone-beam CT system for extremity imaging. Radiology. 2014;270(3):816–24.
de Cesar Netto C, Schon LC, Thawait GK, da Fonseca LF, Chinanuvathana A, Zbijewski WB, et al. Flexible adult acquired flatfoot deformity: comparison between weight-bearing and non-weight-bearing measurements using cone-beam computed tomography. J Bone Joint Surg Am. 2017;99(18):e98.
Thawait GK, Demehri S, AlMuhit A, Zbijweski W, Yorkston J, Del Grande F, et al. Extremity cone-beam CT for evaluation of medial tibiofemoral osteoarthritis: initial experience in imaging of the weight-bearing and non-weight-bearing knee. Eur J Radiol. 2015;84(12):2564–70.
Sisniega A, Zbijewski W, Badal A, Kyprianou IS, Stayman JW, Vaquero JJ, et al. Monte Carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions. Med Phys. 2013;40(5):051915.
Sisniega A, Zbijewski W, Xu J, Dang H, Stayman JW, Yorkston J, et al. High-fidelity artifact correction for cone-beam CT imaging of the brain. Phys Med Biol. 2015;60(4):1415–39.
Zbijewski W, Sisniega A, Stayman JW, Muhit A, Thawait G, Packard N, et al. High-performance soft-tissue imaging in extremity cone-beam CT. Proc SPIE Int Soc Opt Eng. 2014;9033:903329.
Siewerdsen JH. Cone-beam CT with a flat-panel detector: from image science to image-guided surgery. Nucl Inst Methods Phys Res A. 2011;648(S1):S241–s250.
Lang H, Neubauer J, Fritz B, Spira EM, Strube J, Langer M, et al. A retrospective, semi-quantitative image quality analysis of cone beam computed tomography (CBCT) and MSCT in the diagnosis of distal radius fractures. Eur Radiol. 2016;26(12):4551–61.
Sisniega A, Stayman JW, Yorkston J, Siewerdsen JH, Zbijewski W. Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion. Phys Med Biol. 2017;62(9):3712–34.
Choi JH, Fahrig R, Keil A, Besier TF, Pal S, McWalter EJ, et al. Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. Part I. numerical model-based optimization. Med Phys. 2013;40(9):091905.
Choi JH, Maier A, Keil A, Pal S, McWalter EJ, Beaupre GS, et al. Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II Experiment. Med Phys. 2014;41(6):061902.
Gang GJ, Zbijewski W, Mahesh M, Thawait G, Packard N, Yorkston J, et al. Image quality and dose for a multisource cone-beam CT extremity scanner. Med Phys. 2018;45(1):144–55.
Hansen N, Muller SD, Koumoutsakos P. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput. 2003;11(1):1–18.
Sisniega A, Zbijewski W, Wu P, Stayman JW, Aygun N, Stevens R, et al. Multi-motion compensation for high-quality cone-beam CT of the head. In: The Fifth International Conference on Image Formation in X-Ray Computed Tomography, Salt Lake City, UT. 2018.
Fleiss JL. Statistical methods for rates and proportions. New York: Wiley; 1981.
Bath M, Mansson LG. Visual grading characteristics (VGC) analysis: a non-parametric rank-invariant statistical method for image quality evaluation. Br J Radiol. 2007;80(951):169–76.
Berger M, Müller K, Aichert A, Unberath M, Thies J, Choi JH, et al. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys. 2016;43(3):1235–48.
Ouadah S, Jacobson M, Stayman J, Ehtiati T, Weiss C, Siewerdsen J. Correction of patient motion in cone-beam CT using 3D–2D registration. Phys Med Biol. 2017;62(23):8813.
Demehri S, Hafezi-Nejad N, Morelli JN, Thakur U, Lifchez SD, Means KR, et al. Scapholunate kinematics of asymptomatic wrists in comparison with symptomatic contralateral wrists using four-dimensional CT examinations: initial clinical experience. Skelet Radiol. 2016;45(4):437–46.
Demehri S, Wadhwa V, Thawait GK, Fattahi N, Means KR, Carrino JA, et al. Dynamic evaluation of pisotriquetral instability using 4-dimensional computed tomography. J Comput Assist Tomogr. 2014;38(4):507–12.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work has originated in Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
Rights and permissions
About this article
Cite this article
Sisniega, A., Thawait, G.K., Shakoor, D. et al. Motion compensation in extremity cone-beam computed tomography. Skeletal Radiol 48, 1999–2007 (2019). https://doi.org/10.1007/s00256-019-03241-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00256-019-03241-w