Evidence map›Paper›PMID 33973113›Full record

ArticleInternational journal of computer assisted radiology and surgery2021

A level-wise spine registration framework to account for large pose changes.

Yunliang Cai, Shaoju Wu, Xiaoyao Fan, Jonathan Olson, Linton Evans, Scott Lollis, Sohail K Mirza, Keith D Paulsen, Songbai Ji

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 38% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Registration of Longitudinal Spine CTs for Monitoring Lesion Growth.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  4. Article
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 1 country.

Yunliang CaiWorcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
Shaoju WuWorcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
Xiaoyao FanDartmouth College Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
Jonathan OlsonDartmouth College Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
Linton EvansDartmouth College Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
Scott LollisUniversity of Vermont Medical Center, Burlington, VT, 05401, USA.
Sohail K MirzaDartmouth College Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
Keith D PaulsenDartmouth College Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
Songbai JiWorcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA. sji@wpi.edu.ORCID http://orcid.org/0000-0002-2886-5781
Dartmouth College · USWorcester Polytechnic Institute · USUniversity of Vermont Medical Center · US

Funding

Image based registration and intraoperative updating for guiding spine surgeryR01EB025747 · NIBIB · DARTMOUTH COLLEGE · PI MIRZA, SOHAIL K, PAULSEN, KEITH D. · 2017 to 2020
$1.7M
NIBIB NIH HHS R01 EB025747
6 · The paper itself

Abstract

purposesAccurate and efficient spine registration is crucial to success of spine image guidance. However, changes in spine pose cause intervertebral motion that can lead to significant registration errors. In this study, we develop a geometrical rectification technique via nonlinear principal component analysis (NLPCA) to achieve level-wise vertebral registration that is robust to large changes in spine pose.

methodsWe used explanted porcine spines and live pigs to develop and test our technique. Each sample was scanned with preoperative CT (pCT) in an initial pose and rescanned with intraoperative stereovision (iSV) in a different surgical posture. Patient registration rectified arbitrary spinal postures in pCT and iSV into a common, neutral pose through a parameterized moving-frame approach. Topologically encoded depth projection 2D images were then generated to establish invertible point-to-pixel correspondences. Level-wise point correspondences between pCT and iSV vertebral surfaces were generated via 2D image registration. Finally, closed-form vertebral level-wise rigid registration was obtained by directly mapping 3D surface point pairs. Implanted mini-screws were used as fiducial markers to measure registration accuracy.

resultsIn seven explanted porcine spines and two live animal surgeries (maximum in-spine pose change of 87.5 mm and 32.7 degrees averaged from all spines), average target registration errors (TRE) of 1.70  ±  0.15 mm and 1.85 ± 0.16 mm were achieved, respectively. The automated spine rectification took 3-5 min, followed by an additional 30 secs for depth image projection and level-wise registration.

conclusionsAccuracy and efficiency of the proposed level-wise spine registration support its application in human open spine surgeries. The registration framework, itself, may also be applicable to other intraoperative imaging modalities such as ultrasound and MRI, which may expand utility of the approach in spine registration in general.

Indexed as

AnimalsDisease Models, AnimalFiducial MarkersHumansImaging, Three-DimensionalMagnetic Resonance ImagingSpinal DiseasesSpineSurgery, Computer-AssistedSwineUltrasonographyDeep learning networksImage registrationNLPCAPatient registrationSpine imagingStereovision

Identifiers

PMID33973113
PMCPMC8358825
OpenAlexW3160405827

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.