Knowledge-based self-calibration method of calibration phantom by and for accurate robot-based CT imaging systems

Duan, Xiaoman; Cai, Jiangze; Ling, Qingqing; Huang, Yanchao; Qi, Hongliang; Chen, Yusi; Zhou, Linghong; Xu, Yuan

doi:10.1016/j.knosys.2021.107343

Cited by 8 publications

(7 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xu et.al [18] set phantom error index integrated with image quality index as object function, and simultaneously improve phantom precision and image quality through optimization. Duan et.al [19] gathered multiple projections through separately moving source position. Coordinates of markers under these projections were then used in an optimization of phantom markers.…”

Section: Introductionmentioning

confidence: 99%

Projection Matrix Based Iterative Reconstruction Algorithm for Robotic CT

Zhou

Wei

2023

IEEE Access

View full text Add to dashboard Cite

Robotic computed tomography (CT) consists of two separate manipulators which carry the source and detector, respectively. Various errors occur in motion due to lack of rigid connection, which may change scanning trajectory of this system. Some methods aimed at recovering geometry relation have occurred. However, these methods are relied on circular scanning trajectory. Projection matrix is the matrix form of geometry definition, which contains geometric parameters in the matrix and transforms geometry calculation into homogeneous matrix calculation. It is suitable for robotic CT because of its irrelevance between angles. Besides, iteration reconstruction algorithm makes no assumptions on scanning trajectories, which is suitable for arbitrary scanning trajectories. Hence, this study combines iteration reconstruction algorithm with the concept of projection matrix to fill the gap. In this study, we established a transformation between projection matrix and ray function, and construct corresponding projection model. Projection matrix based iterative reconstruction algorithm was then developed and validated by simulation and experiment. Uncooperative rotation of manipulators, jitter in motion and non-circular motion trajectories were simulated. FDK, projection matrix-based FDK and projection matrix-based iterative method were compared. Real experiments were also conducted. The results showed projection matrix based iterative reconstruction could lead to better image quality against motion error. In summary, this study provided a high-precision reconstruction approach for robotic CT.

show abstract

Section: Introductionmentioning

confidence: 99%

Projection Matrix Based Iterative Reconstruction Algorithm for Robotic CT

Zhou

Wei

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As a brief review of the literature, there is a wealth of calibration methods for cone-beam computed tomography (CT) and tomosynthesis imaging in diverse applications [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. They can broadly be separated into offline phantom-based calibration methods and online phantom-less methods [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

“…For offline methods, the geometry of the imaging system is determined with dedicated scans of calibration phantoms. Calibration phantoms contain distinct positional markers, such as radio-opaquebeads [ 15 , 18 , 34 , 35 , 41 , 42 , 45 , 49 ] or wires [ 44 ]. Some phantom-based methods do not require knowledge of the positions of the markers in the phantom [ 23 , 25 , 35 , 36 , 45 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

Online Calibration of a Linear Micro Tomosynthesis Scanner

et al. 2022

View full text Add to dashboard Cite

In a linear tomosynthesis scanner designed for imaging histologic samples of several centimeters size at 10 µm resolution, the mechanical instability of the scanning stage (±10 µm) exceeded the resolution of the image system, making it necessary to determine the trajectory of the stage for each scan to avoid blurring and artifacts in the images that would arise from the errors in the geometric information used in 3D reconstruction. We present a method for online calibration by attaching a layer of randomly dispersed micro glass beads or calcium particles to the bottom of the sample stage. The method was based on a parametric representation of the rigid body motion of the sample stage-marker layer assembly. The marker layer was easy to produce and proven effective in the calibration procedure.

show abstract

“…The hybrid CT system has a unique architecture and presents a unique geometric calibration problem, with a unique set of information that can be used for calibration. Previously, a number of calibration methods have been developed for cone-beam tomosynthesis as well as computed tomography to meet the demand of diverse applications [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. They have been generally classified into offline phantom-based calibration methods and online phantom-less methods [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…In offline methods, the geometry of the imaging system is determined with dedicated calibration scans of phantoms. The phantoms contain distinct positional markers, such as radio-opaque beads [ 19 , 22 , 23 , 30 , 31 , 35 , 36 , 41 ] or wires [ 33 ]. Some phantom-based methods do not need to know the precise arrangement of the markers, as long as the same phantom is scanned in multiple orientations and positions [ 9 , 11 , 23 , 24 , 36 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Online Geometric Calibration of a Hybrid CT System for Ultrahigh-Resolution Imaging

Wang

Bennett

et al. 2022

Tomography

View full text Add to dashboard Cite

A hybrid imaging system consisting of a standard computed tomography (CT) scanner and a low-profile photon-counting detector insert in contact with the patient’s body has been used to produce ultrahigh-resolution images in a limited volume in chest scans of patients. The detector insert is placed on the patient bed as needed and not attached. Thus, its position and orientation in the scanner is dependent on the patient’s position and scan settings. To allow accurate image reconstruction, we devised a method of determining the relative geometry of the detector insert and the CT scanner for each scan using fiducial markers. This method uses an iterative registration algorithm to align the markers in the reconstructed volume from the detector insert to that of the concurrent CT scan. After obtaining precise geometric information of the detector insert relative to the CT scanner, the two complementary sets of images are summed together to create a detailed image with reduced artifacts.

show abstract

Knowledge-based self-calibration method of calibration phantom by and for accurate robot-based CT imaging systems

Cited by 8 publications

References 61 publications

Projection Matrix Based Iterative Reconstruction Algorithm for Robotic CT

Projection Matrix Based Iterative Reconstruction Algorithm for Robotic CT

Online Calibration of a Linear Micro Tomosynthesis Scanner

Online Geometric Calibration of a Hybrid CT System for Ultrahigh-Resolution Imaging

Contact Info

Product

Resources

About