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Motion Correction for High-Resolution Cone-Beam CT: Paper by Sisniega et al.

A paper published by Dr. Alejandro Sisniega (Research Associate, Department of Biomedical Engineering) and colleagues at the I-STAR Lab describes a new method for correcting patient motion in cone-beam CT (CBCT). Because CBCT often involves scan times >10 sec (for example, 20-30 sec common in extremity imaging and up to 60 sec in image-guided procedures), patient motion during the scan can result in significant degradation of image quality.

Even a few mm of motion can confound the visibility of subtle image features. A variety of methods have been reported in recent years to correct motion artifacts. Dr. Sisniega’s approach involves a purely image-based solution that does not require external motion tracking devices or prior images of the patient. Instead, the patient motion trajectory is derived directly from the image data using a 3D “auto-focus” method that optimizes sharpness of the resulting 3D image. Sisniega evaluated a number of possible sharpness metrics – including total variation and entropy – and showed gradient variance to perform best overall.

The method uses one or more volumes of interest (VOIs) within which motion can be assumed to follow a rigid trajectory – for example a bone structure – and can support multiple VOIs to independently solve for patient motion across the entire image, even in the presence of complex deformation. For example, in CBCT of the extremities, the method was shown to perform well in images of the knee using 2 VOIs – one for the distal femur and one for the proximal tibia (and optionally, a third for the patella). The method was rigorously evaluated in phantom studies on a CBCT benchtop, showing the ability to recover spatial resolution both in small motions (~0.5 – 1 mm perturbations) and large motions (>10 mm motion during the scan). The algorithm was then tested in clinical studies on an extremity CBCT system in the Department of Radiology and Johns Hopkins Hospital. Cases exhibiting significant motion artifacts were identified in retrospective review, and the algorithm was shown to reliably eliminate artifacts and recover spatial resolution sufficient for visualizing the joint space, subchondral trabecular bone, and surrounding soft-tissue features, including tendons, ligaments, and cartilage.

The motion correction algorithm is now proving its merit in applications within and beyond musculoskeletal extremity imaging, including CBCT of head trauma and C-arm CBCT, which can also involve long scan times and challenging motion artifacts. In addition to restoring spatial resolution in CBCT of bone morphology, ongoing work shows the algorithm to be important in recovering low-contrast visibility of soft tissues as well. Dr. Sisniega is extending the method to handle complex deformation of soft-tissue structures in the abdomen – tackling one of the major challenges to CBCT image quality in image-guided interventions.

Full details of the algorithm and experimental studies can be found in the paper published in the journal of Physics in Medicine and Biology (2017 May 7;62(9):3712-3734. doi: 10.1088/1361-6560/aa6869).