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transformation with iterative closest point (icp) matching algorithm  (MathWorks Inc)


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    MathWorks Inc transformation with iterative closest point (icp) matching algorithm
    Flow-diagram for three image localization method. The pipeline uses FreeSurfer to process the raw images and segment the anatomical information from the <t>pre-implant</t> <t>MRI.</t> Next, the electrode artifacts, A CT and A MRI , from the two post-implant images are imported into MATLAB for labeling and further processing. The three-dimensional point sets from the two scans' metal-artifact signals are combined with an iterative-closest point matching algorithm which calculates the ideal transformation T <t>ICP</t> . This transform allows the centroids from the post-implant CT scan to be reconciled with the post-implant MRI space that is optimally aligned to the pre-implant MRI. Note: Dotted arrows are steps that compute a transformation. The dashed line box indicates the only step requiring a simple manual intervention, which is unrelated to the automated localization and rather a clinically dependent step to be used for matching recording channels to corresponding electrode contacts.
    Transformation With Iterative Closest Point (Icp) Matching Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/transformation with iterative closest point (icp) matching algorithm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    transformation with iterative closest point (icp) matching algorithm - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Enhanced co-registration methods to improve intracranial electrode contact localization"

    Article Title: Enhanced co-registration methods to improve intracranial electrode contact localization

    Journal: NeuroImage : Clinical

    doi: 10.1016/j.nicl.2018.07.026

    Flow-diagram for three image localization method. The pipeline uses FreeSurfer to process the raw images and segment the anatomical information from the pre-implant MRI. Next, the electrode artifacts, A CT and A MRI , from the two post-implant images are imported into MATLAB for labeling and further processing. The three-dimensional point sets from the two scans' metal-artifact signals are combined with an iterative-closest point matching algorithm which calculates the ideal transformation T ICP . This transform allows the centroids from the post-implant CT scan to be reconciled with the post-implant MRI space that is optimally aligned to the pre-implant MRI. Note: Dotted arrows are steps that compute a transformation. The dashed line box indicates the only step requiring a simple manual intervention, which is unrelated to the automated localization and rather a clinically dependent step to be used for matching recording channels to corresponding electrode contacts.
    Figure Legend Snippet: Flow-diagram for three image localization method. The pipeline uses FreeSurfer to process the raw images and segment the anatomical information from the pre-implant MRI. Next, the electrode artifacts, A CT and A MRI , from the two post-implant images are imported into MATLAB for labeling and further processing. The three-dimensional point sets from the two scans' metal-artifact signals are combined with an iterative-closest point matching algorithm which calculates the ideal transformation T ICP . This transform allows the centroids from the post-implant CT scan to be reconciled with the post-implant MRI space that is optimally aligned to the pre-implant MRI. Note: Dotted arrows are steps that compute a transformation. The dashed line box indicates the only step requiring a simple manual intervention, which is unrelated to the automated localization and rather a clinically dependent step to be used for matching recording channels to corresponding electrode contacts.

    Techniques Used: Labeling, Transformation Assay, Computed Tomography

    Validation and measuring electrode distances. ( Left ) All the pertinent information for electrode localization. Here, the yellow, blue, and red point clouds represent the metal artifact voxels extracted from the co-registered implant images. The yellow dots are A CT and blue dots are A MRI ; the red dots are A ICP = A CT *T ICP . The grey transparent object is the 3D rendered brain surface computed from the pre-implant MRI. ( Right ) Zoomed in to illustrate the validation metrics. The Euclidian distances, P CT and P MRI , between the electrode centroids, E (CT,MRI) , (yellow and pink circle) and the nearest vertex (black dot) on the smoothed pial surface was measured for every electrode. Additionally, the Euclidian distance, D Method , between electrode centroids, E (CT,MRI) , (yellow and pink circle) was calculated pairwise for each comparison of methods. NOTE: “CT” variables are calculated with both imaging software, FSL4.1 and SPM12, while “MRI” variables represent results of the novel ICP method presented here.
    Figure Legend Snippet: Validation and measuring electrode distances. ( Left ) All the pertinent information for electrode localization. Here, the yellow, blue, and red point clouds represent the metal artifact voxels extracted from the co-registered implant images. The yellow dots are A CT and blue dots are A MRI ; the red dots are A ICP = A CT *T ICP . The grey transparent object is the 3D rendered brain surface computed from the pre-implant MRI. ( Right ) Zoomed in to illustrate the validation metrics. The Euclidian distances, P CT and P MRI , between the electrode centroids, E (CT,MRI) , (yellow and pink circle) and the nearest vertex (black dot) on the smoothed pial surface was measured for every electrode. Additionally, the Euclidian distance, D Method , between electrode centroids, E (CT,MRI) , (yellow and pink circle) was calculated pairwise for each comparison of methods. NOTE: “CT” variables are calculated with both imaging software, FSL4.1 and SPM12, while “MRI” variables represent results of the novel ICP method presented here.

    Techniques Used: Biomarker Discovery, Comparison, Imaging, Software

    Variables and descriptions. The three types of images are processed and transformed to yield several variables and metrics, defined here in brief for easy reference. Please see main text for full descriptions. n = (50–150); the number of implanted electrodes.
    Figure Legend Snippet: Variables and descriptions. The three types of images are processed and transformed to yield several variables and metrics, defined here in brief for easy reference. Please see main text for full descriptions. n = (50–150); the number of implanted electrodes.

    Techniques Used: Transformation Assay, Magnetic Resonance Imaging, Software

    Cross-methods comparison for grid electrodes. The three methods presented here were compared with a recent localization technique that also relies on post-operative MRI information, . The median Euclidian distances between Yang's technique applied to this dataset, and the other methods (X-axis) are shown in the box-whisker plots above for N = 11 patients (blue dot). For ICP, the median distance across all patients and electrodes was 1.90 mm, with an interquartile range of 2.23 mm.
    Figure Legend Snippet: Cross-methods comparison for grid electrodes. The three methods presented here were compared with a recent localization technique that also relies on post-operative MRI information, . The median Euclidian distances between Yang's technique applied to this dataset, and the other methods (X-axis) are shown in the box-whisker plots above for N = 11 patients (blue dot). For ICP, the median distance across all patients and electrodes was 1.90 mm, with an interquartile range of 2.23 mm.

    Techniques Used: Comparison, Whisker Assay

    Localization approach steps and run times in parallel layout. Run times are shown for one typical subject and 1.0 mm 3 resolution MRI scans and 0.5 mm 3 CT scan; the files will occupy roughly 1.0 Gb of hard disk space. Steps are ordered from first (top) to last (bottom).
    Figure Legend Snippet: Localization approach steps and run times in parallel layout. Run times are shown for one typical subject and 1.0 mm 3 resolution MRI scans and 0.5 mm 3 CT scan; the files will occupy roughly 1.0 Gb of hard disk space. Steps are ordered from first (top) to last (bottom).

    Techniques Used: Computed Tomography, Stripping Membranes



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