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Impact of Matching Point Selections on Image Registration Accuracy between Optical Scan and Computed Tomography.

ABSTRACT: The point-based surface registration method involves the manual selection process of paired matching points on the data of computed tomography and optical scan. The purpose of this study was to investigate the impact of selection error and distribution of fiducial points on the accuracy of image matching between 3-dimensional (3D) images in dental planning software programs. Computed tomography and optical scan images of a partial edentulous dental arch were obtained. Image registration of the optical scan image to computed tomography was performed using the point-based surface registration method in planning software programs under different conditions of 3 fiducial points: point selection error (0, 1, or 2?mm), point distribution (unilateral, bilateral), and planning software (Implant Studio, Blue Bio Plan) (n = 5 per condition, N = 60). The accuracy of image registration at each condition was evaluated by measuring linear discrepancies between matched images at X, Y, and Z axes. Kruskal-Wallis test, Mann-Whitney U test with Bonferroni correction, and 3-way analysis of variance were used to statistically analyse the measurement data (? = 0.05). No statistically significant difference was exhibited between the 0 and 1?mm point mismatch conditions in either unilateral or bilateral point distributions. The discrepancy values in the 2?mm mismatch condition were significantly different from the other mismatch conditions, especially in the unilateral point distribution (P < 0.05). Strong interactions among point selection error, distribution, and software programs on the image registration were found (P < 0.001). Minor matching point selection error did not influence the accuracy of point-based automatic image registration in the software programs. When the fiducial points are distributed unilaterally with large point selection error, the image matching accuracy could be decreased.

SUBMITTER: Mai HY

PROVIDER: S-EPMC7426779 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Impact of Matching Point Selections on Image Registration Accuracy between Optical Scan and Computed Tomography.

Mai Hai Yen HY Lee Du-Hyeong DH

BioMed research international 20200804

The point-based surface registration method involves the manual selection process of paired matching points on the data of computed tomography and optical scan. The purpose of this study was to investigate the impact of selection error and distribution of fiducial points on the accuracy of image matching between 3-dimensional (3D) images in dental planning software programs. Computed tomography and optical scan images of a partial edentulous dental arch were obtained. Image registration of the o ...[more]

PMID: 32802841

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Project description:In current computed tomography (CT) examinations, the associated x-ray radiation dose is of a significant concern to patients and operators. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) or kVp parameter (or delivering less x-ray energy to the body) as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and the noise would propagate into the CT image if no adequate noise control is applied during image reconstruction. Since a normal-dose high diagnostic CT image scanned previously may be available in some clinical applications, such as CT perfusion imaging and CT angiography (CTA), this paper presents an innovative way to utilize the normal-dose scan as a priori information to induce signal restoration of the current low-dose CT image series.Unlike conventional local operations on neighboring image voxels, nonlocal means (NLM) algorithm utilizes the redundancy of information across the whole image. This paper adapts the NLM to utilize the redundancy of information in the previous normal-dose scan and further exploits ways to optimize the nonlocal weights for low-dose image restoration in the NLM framework. The resulting algorithm is called the previous normal-dose scan induced nonlocal means (ndiNLM). Because of the optimized nature of nonlocal weights calculation, the ndiNLM algorithm does not depend heavily on image registration between the current low-dose and the previous normal-dose CT scans. Furthermore, the smoothing parameter involved in the ndiNLM algorithm can be adaptively estimated based on the image noise relationship between the current low-dose and the previous normal-dose scanning protocols.Qualitative and quantitative evaluations were carried out on a physical phantom as well as clinical abdominal and brain perfusion CT scans in terms of accuracy and resolution properties. The gain by the use of the previous normal-dose scan via the presented ndiNLM algorithm is noticeable as compared to a similar approach without using the previous normal-dose scan.For low-dose CT image restoration, the presented ndiNLM method is robust in preserving the spatial resolution and identifying the low-contrast structure. The authors can draw the conclusion that the presented ndiNLM algorithm may be useful for some clinical applications such as in perfusion imaging, radiotherapy, tumor surveillance, etc.

Project description:For accurate analysis of bone formation and resorption during fracture healing, correct registration of follow-up onto baseline image is required. A per-fragment approach could improve alignment compared to standard registration based on the whole fractured region. In this exploratory study, we tested the effect of fragment size and displacement on a per-fragment registration, and compared the results of this per-fragment registration to the results of the standard registration in two stable fractures and one unstable fracture. To test the effect of fragment size and displacement, high-resolution peripheral quantitative computed tomography (HR-pQCT) scans of three unfractured radii were divided into subvolumes. Different displacements in x-, y, or z-direction or rotations around each axis were applied, and each subvolume was registered onto the initial volume to realign it. Next, registration of follow-up onto baseline scan was performed in two stable and one unstable fracture. After coarsely aligning the follow-up onto the baseline scan, a more accurate registration was performed of the whole fracture, i.e. the standard registration, and of each fracture fragment separately, i.e. per-fragment registration. Alignment was checked using overlay images showing baseline, follow-up and overlap between these scans, and by comparing correlation coefficients between the standard and per-fragment registration. Generally, subvolumes as small as 300 mm3 that were displaced up to 0.82 mm in x- or y-, or up to 1.64 mm in z-direction could be realigned correctly. For the fragments of all fractures, correlation coefficients were higher after per-fragment registration compared to standard registration. Most improvement was found in the unstable fracture and one fragment of the unstable fracture did not align correctly. This exploratory study showed that image registration of individual subvolumes, such as fracture fragments, is feasible in both stable and unstable fractures, and leads to better alignment of these fragments compared to an approach that is based on registration using the whole fractured region. This result is promising for additional analysis of bone formation and resorption in HR-pQCT studies on fracture healing.

Project description:OBJECTIVE:To compare the accuracy of different methods for image registration in image-guided adaptive brachytherapy (IGABT) for cervical cancer. METHODS:The last treatment planning CT images (CT1) and the first treatment planning CT images (CT2) were acquired from 15 patients with cervical cancer and registered with different match image qualities (retained/removed catheter source in images) and different match regions [target only (S Group)/ interested organ structure (M Group)/body (L Group)] in Velocity3.2 software. The dice similarity coefficient (DSC) between the clinical target volumes (CTV) of the CT1 and CT2 images (CTVCT1 and CTVCT2, respectively) and between the organs-at-risk (OAR) of the two imaging datasets (OARCT1 and OARCT2, respectively) were used to evaluate the image registration accuracy. RESULTS:The auto-segmentation volume of the catheter source using Velocity software based on the CT threshold was the closest to the actual volume within the CT value range of 1700-1800 HU. In the retained group, the DSC for the OARs of was better than or equal to that of the removed group, and the DSC value of the rectum was significantly improved (P < 0.05). For comparison of different match regions, the high-risk target volume (HRCTV) and the low-risk target volume (IRCTV) had the best precision for registration of the target area, which was significantly greater than that of M group and L group (P < 0.05). The M group had better registration accuracy of the target area and the best accuracy for the OARs. The DSC values of the bladder and rectum were significantly better than those of the other two groups (P < 0.05). CONCLUSIONS:The CT value range of 1700-1800 HU is optimal for automatic image segmentation using Velocity software. Automatic segmentation and shielding the volume of the catheter source can improve the image quality. We recommend the use of interested organ structures regions for image registration in image-guided adaptive brachytherapy for cervical cancer.

Dataset Information

Impact of Matching Point Selections on Image Registration Accuracy between Optical Scan and Computed Tomography.

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Impact of Matching Point Selections on Image Registration Accuracy between Optical Scan and Computed Tomography.

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