Regularization design for high-quality cone-beam CT of intracranial hemorrhage using statistical reconstruction

2016 ◽  
Author(s):  
H. Dang ◽  
J. W. Stayman ◽  
J. Xu ◽  
A. Sisniega ◽  
W. Zbijewski ◽  
...  
Keyword(s):  
Intracranial Hemorrhage ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
High Quality ◽  
Statistical Reconstruction

Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion

2015 ◽  
Vol 40 ◽  
pp. 30-38 ◽  
Author(s):  
Yongbin Zhang ◽  
Jinzhong Yang ◽  
Lifei Zhang ◽  
Laurence E. Court ◽  
Song Gao ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Respiratory Motion ◽  
Cone Beam Ct ◽  
Ct Images ◽  
Cone Beam ◽  
High Quality ◽  
Digital Reconstruction

scholarly journals Nonlinear statistical reconstruction for flat-panel cone-beam CT with blur and correlated noise models

2016 ◽  
Author(s):  
Steven Tilley ◽  
Jeffrey H. Siewerdsen ◽  
Wojciech Zbijewski ◽  
J. Webster Stayman
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Correlated Noise ◽  
Flat Panel ◽  
Statistical Reconstruction ◽  
Noise Models

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.

Design and characterization of a dedicated cone-beam CT scanner for detection of acute intracranial hemorrhage

2016 ◽  
Author(s):  
J. Xu ◽  
A. Sisniega ◽  
W. Zbijewski ◽  
H. Dang ◽  
J. W. Stayman ◽  
...  
Keyword(s):  
Intracranial Hemorrhage ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Scanner

High quality 4D cone-beam CT reconstruction using motion-compensated total variation regularization

2017 ◽  
Vol 62 (8) ◽  
pp. 3313-3329 ◽  
Author(s):  
Hua Zhang ◽  
Jianhua Ma ◽  
Zhaoying Bian ◽  
Dong Zeng ◽  
Qianjin Feng ◽  
...  
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Total Variation Regularization ◽  
Ct Reconstruction ◽  
High Quality

scholarly journals Technical assessment of a prototype cone-beam CT system for imaging of acute intracranial hemorrhage

2016 ◽  
Vol 43 (10) ◽  
pp. 5745-5757 ◽  
Author(s):  
Jennifer Xu ◽  
Alejandro Sisniega ◽  
Wojciech Zbijewski ◽  
Hao Dang ◽  
J. Webster Stayman ◽  
...  
Keyword(s):  
Intracranial Hemorrhage ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct System ◽  
Technical Assessment

scholarly journals Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method

2015 ◽  
Vol 42 (5) ◽  
pp. 2699-2708 ◽  
Author(s):  
Adam S. Wang ◽  
J. Webster Stayman ◽  
Yoshito Otake ◽  
Sebastian Vogt ◽  
Gerhard Kleinszig ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Statistical Reconstruction

Task-based statistical image reconstruction for high-quality cone-beam CT

2017 ◽  
Vol 62 (22) ◽  
pp. 8693-8719 ◽  
Author(s):  
Hao Dang ◽  
J Webster Stayman ◽  
Jennifer Xu ◽  
Wojciech Zbijewski ◽  
Alejandro Sisniega ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
High Quality ◽  
Statistical Image Reconstruction
Sign in / Sign up

Export Citation Format

Share Document