Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT

2013 ◽  
Vol 40 (10) ◽  
pp. 101913 ◽  
Author(s):  
Marcus Brehm ◽  
Pascal Paysan ◽  
Markus Oelhafen ◽  
Marc Kachelrieß
Keyword(s):  
Motion Estimation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Patient Specific ◽  
Artifact Model

SU-FF-J-05: Motion Estimation Using Cone-Beam CT Projection Images

2009 ◽  
Vol 36 (6Part5) ◽  
pp. 2475-2476
Author(s):  
N Becker ◽  
I Kay
Keyword(s):  
Motion Estimation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Images

TH-D-BRC-05: Respiratory Motion Correction of Cone-Beam CT in Abdomen Using a Patient-Specific Motion Model

2009 ◽  
Vol 36 (6Part28) ◽  
pp. 2812-2812
Author(s):  
Q Zhang ◽  
YC Hu ◽  
S Kriminski ◽  
K Goodman ◽  
KE Rosenzweig ◽  
...  
Keyword(s):  
Motion Correction ◽  
Respiratory Motion ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Patient Specific ◽  
Motion Model ◽  
Respiratory Motion Correction

scholarly journals Magnitude of observer error using cone beam CT for prostate interfraction motion estimation: effect of reducing scan length or increasing exposure

2015 ◽  
Vol 88 (1054) ◽  
pp. 20150208 ◽  
Author(s):  
Helen A McNair ◽  
Emma J Harris ◽  
Vibeke N Hansen ◽  
Karen Thomas ◽  
Christopher South ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Observer Error ◽  
Interfraction Motion ◽  
Scan Length

scholarly journals Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study

2021 ◽  
Author(s):  
Salam Dhou ◽  
Mohanad Alkhodari ◽  
Dan Ionascu ◽  
Christopher Williams ◽  
John H. Lewis
Keyword(s):  
Vector Fields ◽  
Displacement Vector ◽  
Cone Beam Ct ◽  
Ground Truth ◽  
Absolute Error ◽  
Tumor Location ◽  
Cone Beam ◽  
Patient Specific ◽  
3D Image ◽  
Motion Models

A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from 4-dimensional cone-beam CT (4D-CBCT) images is developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is done in two steps: 1) deriving motion models and 2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior-inferior (SI) direction and the 95th percentile in two patient datasets were (2.29 mm and 5.79 mm) for patient 1 and (1.89 mm and 4.82 mm) for patient 2. This study has demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.

TU-D-I-611-04: Intensity-Modulated Cone-Beam CT for Patient-Specific Distribution of SNR

2005 ◽  
Vol 32 (6Part16) ◽  
pp. 2092-2092 ◽  
Author(s):  
S Graham ◽  
J Siewerdsen ◽  
H Keller ◽  
D Moseley ◽  
D Jaffray
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Patient Specific ◽  
Intensity Modulated ◽  
Specific Distribution

scholarly journals A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance

2020 ◽  
Vol 15 (11) ◽  
pp. 1787-1796
Author(s):  
Mareike Thies ◽  
Jan-Nico Zäch ◽  
Cong Gao ◽  
Russell Taylor ◽  
Nassir Navab ◽  
...  
Keyword(s):  
Neural Network ◽  
Cone Beam Ct ◽  
Simulated Data ◽  
Tomographic Imaging ◽  
Cone Beam ◽  
Screw Placement ◽  
Patient Specific ◽  
Metal Artifacts ◽  
High Quality ◽  
Reconstruction Quality

Abstract Purpose During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality. Methods We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i.e., verification of screw placement. Adjustments are performed on-the-fly using a convolutional neural network that regresses a quality index over all possible next views given the current X-ray image. Adjusting the CBCT trajectory to acquire the recommended views results in non-circular source orbits that avoid poor images, and thus, data inconsistencies. Results We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory. Using both realistically simulated data as well as real CBCT acquisitions of a semianthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts. Conclusion The proposed method is a step toward online patient-specific C-arm CBCT source trajectories that enable high-quality tomographic imaging in the operating room. Since the optimization objective is implicitly encoded in a neural network trained on large amounts of well-annotated projection images, the proposed approach overcomes the need for 3D information at run-time.

scholarly journals Correction of motion artifacts in cone-beam CT using a patient-specific respiratory motion model

2010 ◽  
Vol 37 (6Part1) ◽  
pp. 2901-2909 ◽  
Author(s):  
Qinghui Zhang ◽  
Yu-Chi Hu ◽  
Fenghong Liu ◽  
Karyn Goodman ◽  
Kenneth E. Rosenzweig ◽  
...  
Keyword(s):  
Respiratory Motion ◽  
Cone Beam Ct ◽  
Motion Artifacts ◽  
Cone Beam ◽  
Patient Specific ◽  
Motion Model
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