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Correction of Patient Motion in Cone-beam CT Using 3D-2D Registration

Overview
Journal Phys Med Biol
Publisher IOP Publishing
Date 2017 Oct 11
PMID 28994668
Citations 15
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Abstract

Cone-beam CT (CBCT) is increasingly common in guidance of interventional procedures, but can be subject to artifacts arising from patient motion during fairly long (~5-60 s) scan times. We present a fiducial-free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in the intrinsic and extrinsic parameters of geometric calibration. The 3D-2D registration process registers each projection to a prior 3D image by maximizing gradient orientation using the covariance matrix adaptation-evolution strategy optimizer. The resulting rigid transforms are applied to the system projection matrices, and a 3D image is reconstructed via model-based iterative reconstruction. Phantom experiments were conducted using a Zeego robotic C-arm to image a head phantom undergoing 5-15 cm translations and 5-15° rotations. To further test the algorithm, clinical images were acquired with a CBCT head scanner in which long scan times were susceptible to significant patient motion. CBCT images were reconstructed using a penalized likelihood objective function. For phantom studies the structural similarity (SSIM) between motion-free and motion-corrected images was  >0.995, with significant improvement (p  <  0.001) compared to the SSIM values of uncorrected images. Additionally, motion-corrected images exhibited a point-spread function with full-width at half maximum comparable to that of the motion-free reference image. Qualitative comparison of the motion-corrupted and motion-corrected clinical images demonstrated a significant improvement in image quality after motion correction. This indicates that the 3D-2D registration method could provide a useful approach to motion artifact correction under assumptions of local rigidity, as in the head, pelvis, and extremities. The method is highly parallelizable, and the automatic correction of residual geometric calibration errors provides added benefit that could be valuable in routine use.

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References
1.
Otake Y, Wang A, Stayman J, Uneri A, Kleinszig G, Vogt S . Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation. Phys Med Biol. 2013; 58(23):8535-53. PMC: 4915373. DOI: 10.1088/0031-9155/58/23/8535. View

2.
Penney G, Weese J, Little J, Desmedt P, Hill D, Hawkes D . A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE Trans Med Imaging. 1998; 17(4):586-95. DOI: 10.1109/42.730403. View

3.
Brehm M, Paysan P, Oelhafen M, Kachelriess M . Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT. Med Phys. 2013; 40(10):101913. DOI: 10.1118/1.4820537. View

4.
Wicklein J, Kunze H, Kalender W, Kyriakou Y . Image features for misalignment correction in medical flat-detector CT. Med Phys. 2012; 39(8):4918-31. DOI: 10.1118/1.4736532. View

5.
Uneri A, Otake Y, Wang A, Kleinszig G, Vogt S, Khanna A . 3D-2D registration for surgical guidance: effect of projection view angles on registration accuracy. Phys Med Biol. 2013; 59(2):271-87. PMC: 4927006. DOI: 10.1088/0031-9155/59/2/271. View