Evidence map›Paper›PMID 40445552›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

End-to-end 2D/3D registration from pre-operative MRI to intra-operative fluoroscopy for orthopedic procedures.

Ping-Cheng Ku, Mingxu Liu, Robert Grupp, Andrew Harris, Julius K Oni, Simon C Mears, Alejandro Martin-Gomez, Mehran Armand

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Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ping-Cheng KuDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. pku1@jh.edu.ORCID http://orcid.org/0000-0002-1285-6143
Mingxu LiuDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
Robert GruppDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
Andrew HarrisDepartment of Orthopaedic Surgery, Johns Hopkins Medicine, Baltimore, MD, 21224, USA.
Julius K OniDepartment of Orthopaedic Surgery, Johns Hopkins Medicine, Baltimore, MD, 21224, USA.
Simon C MearsDepartment of Orthopaedic Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
Alejandro Martin-GomezDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
Mehran ArmandDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.

Funding

Image-Guided Workstation and Tools for the Treatment of Bone DefectsR01EB016703 · NIBIB · UNIVERSITY OF ARKANSAS AT FAYETTEVILLE · PI ARMAND, MEHRAN · 2013 to 2022
$3.4M
Robotic System for Spinal Decompression and Interbody FusionR01AR080315 · NIAMS · UNIVERSITY OF ARKANSAS AT FAYETTEVILLE · PI ARMAND, MEHRAN, JAIN, AMIT · 2022 to 2025
$1.7M
NIAMS NIH HHS R01AR080315NIBIB NIH HHS R01EB016703
6 · The paper itself

Abstract

purposeSoft tissue pathologies and bone defects are not easily visible in intra-operative fluoroscopic images; therefore, we develop an end-to-end MRI-to-fluoroscopic image registration framework, aiming to enhance intra-operative visualization for surgeons during orthopedic procedures.

methodsThe proposed framework utilizes deep learning to segment MRI scans and generate synthetic CT (sCT) volumes. These sCT volumes are then used to produce digitally reconstructed radiographs (DRRs), enabling 2D/3D registration with intra-operative fluoroscopic images. The framework's performance was validated through simulation and cadaver studies for core decompression (CD) surgery, focusing on the registration accuracy of femur and pelvic regions.

resultsThe framework achieved a mean translational registration accuracy of 2.4 ± 1.0 mm and rotational accuracy of 1.6 ±

conclusionThe MRI-to-fluoroscopic registration framework offers a novel approach to image guidance in orthopedic surgeries, exclusively using MRI without the need for CT scans. This approach enhances the visualization of soft tissues and bone defects, reduces radiation exposure, and provides a safer, more effective alternative for intra-operative surgical guidance.

Indexed as

Imaging, Three-DimensionalMagnetic Resonance ImagingOrthopedic ProceduresSurgery, Computer-AssistedCadaverFemurFluoroscopyHumansTomography, X-Ray Computed2D/3D registrationCore decompressionDeep learningMRI segmentationMRI to X-ray registration

Identifiers

PMID40445552

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.