Evidence map›Paper›PMID 29569728›Full record

ArticleMedical physics2018

Automatic intraoperative stitching of nonoverlapping cone-beam CT acquisitions.

Javad Fotouhi, Bernhard Fuerst, Mathias Unberath, Stefan Reichenstein, Sing Chun Lee, Alex A Johnson, Greg M Osgood, Mehran Armand, Nassir Navab

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.6field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Source-detector trajectory optimization for FOV extension in dental CBCT imaging.Computational and structural biotechnology journal · 2024
    Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Javad FotouhiComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
Bernhard FuerstComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
Mathias UnberathComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
Stefan ReichensteinComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
Sing Chun LeeComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
Alex A JohnsonDepartment of Orthopaedic Surgery, Johns Hopkins Hospital, Baltimore, MD, USA.
Greg M OsgoodDepartment of Orthopaedic Surgery, Johns Hopkins Hospital, Baltimore, MD, USA.
Mehran ArmandDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Nassir NavabComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
Johns Hopkins University · USJohns Hopkins Hospital · USJohns Hopkins University Applied Physics Laboratory · US

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
Training In Orthopaedic Team ScienceT32AR067708 · NIAMS · JOHNS HOPKINS UNIVERSITY · PI JONES, LYNNE C., SHAFIQ, BABAR · 2015 to 2024
$2.0M
Robot-Assisted Femoroplasty with Intraoperative Biomechanical FeedbackR01EB023939 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI ARMAND, MEHRAN · 2017 to 2020
$1.6M
NIAMS NIH HHS T32 AR067708NIBIB NIH HHS R01 EB016703NIBIB NIH HHS R01 EB023939
6 · The paper itself

Abstract

purposeCone-beam computed tomography (CBCT) is one of the primary imaging modalities in radiation therapy, dentistry, and orthopedic interventions. While CBCT provides crucial intraoperative information, it is bounded by a limited imaging volume, resulting in reduced effectiveness. This paper introduces an approach allowing real-time intraoperative stitching of overlapping and nonoverlapping CBCT volumes to enable 3D measurements on large anatomical structures.

methodsA CBCT-capable mobile C-arm is augmented with a red-green-blue-depth (RGBD) camera. An offline cocalibration of the two imaging modalities results in coregistered video, infrared, and x-ray views of the surgical scene. Then, automatic stitching of multiple small, nonoverlapping CBCT volumes is possible by recovering the relative motion of the C-arm with respect to the patient based on the camera observations. We propose three methods to recover the relative pose: RGB-based tracking of visual markers that are placed near the surgical site, RGBD-based simultaneous localization and mapping (SLAM) of the surgical scene which incorporates both color and depth information for pose estimation, and surface tracking of the patient using only depth data provided by the RGBD sensor.

resultsOn an animal cadaver, we show stitching errors as low as 0.33, 0.91, and 1.72 mm when the visual marker, RGBD SLAM, and surface data are used for tracking, respectively.

conclusionsThe proposed method overcomes one of the major limitations of CBCT C-arm systems by integrating vision-based tracking and expanding the imaging volume without any intraoperative use of calibration grids or external tracking systems. We believe this solution to be most appropriate for 3D intraoperative verification of several orthopedic procedures.

Indexed as

AnimalsCalibrationCone-Beam Computed TomographyFemurFiducial MarkersHumansImaging, Three-DimensionalInfrared RaysIntraoperative PeriodMinimally Invasive Surgical ProceduresOrthopedic ProceduresPattern Recognition, AutomatedPhantoms, ImagingSwineTime FactorsVideo RecordingC-armcone-beam CTRGBDstitchingtracking

Identifiers

PMID29569728
PMCPMC5997569
OpenAlexW2808348199

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

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.