Evidence map›Paper›PMID 24320463›Full record

ArticleMedical physics2013

Use of MRI to assess the prediction of heart motion with gross body motion in myocardial perfusion imaging by stereotracking of markers on the body surface.

Michael A King, Joyoni Dey, Karen Johnson, Paul Dasari, Joyeeta M Mukherjee, Joseph E McNamara, Arda Konik, Cliff Lindsay, Shaokuan Zheng, Dennis Coughlin

Open access · greenAbstract read
In one paragraph

Article in Medical physics, 2013. 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
1.3field-weighted citation impact, top 19% 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, 6 citations in OpenAlex.

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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

10 authors at 4 institutions in 1 country.

Michael A KingDepartment of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts 01655.
Joyoni Dey
Karen Johnson
Paul Dasari
Joyeeta M Mukherjee
Joseph E McNamara
Arda Konik
Cliff Lindsay
Shaokuan Zheng
Dennis Coughlin
University of Massachusetts Chan Medical School · USUMass Memorial Medical Center · USVA New Jersey Health Care System · USWorcester Polytechnic Institute · US

Funding

PATIENT MOTION DETECTION AND COMPENSATION IN SPECTR01EB001457 · NIBIB · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI KING, MICHAEL A · 2003 to 2013
$5.7M
NIBIB NIH HHS R01 EB001457
6 · The paper itself

Abstract

purposeThe aim of this study is to determine using MRI in volunteers whether the rigid-body-motion (RBM) model can be approximately used to estimate the gross body-motion of the heart from that of external markers on patient's chest. Our target clinical application is to use a visual-tracking-system (VTS) which employs stereoimaging to estimate heart motion during SPECT/CT and PET∕CT myocardial perfusion imaging.

methodsTo investigate body-motion separate from the respiration the authors had the volunteers hold their breath during the acquisition of a sequence of two sets of EKG-triggered MRI sagittal slices. The first set was acquired pre-motion, and the second postmotion. The motion of the heart within each breath-hold set of slices was estimated by registration to the semiautomatic 3D segmentation of the heart region in a baseline set acquired using the Navigator technique. The motion of the heart between the pre- and postmotion sets was then determined as the difference in the individual motions in comparison to the Navigator sets. An analysis of the combined motion of the individual markers on the chest was used to obtain an estimate of the six-degree-of-freedom RBM from the VTS system. The metric for judging agreement between the motion estimated by MRI and the VTS was the average error. This was defined as the average of the magnitudes of the differences in the vector displacements of all voxels in the heart region. Studies with the Data Spectrum Anthropomorphic Phantom and "No-Motion" studies in which the volunteer did not intentionally move were used to establish a baseline for agreement. With volunteer studies a t-test was employed to determine when statistically significant differences in Average Errors occurred compared to the No-motion studies.

resultsFor phantom acquisitions, the Average Error when the motion was just translation was 0.1 mm. With complex motions, which included a combination of rotations and translations, the Average Error increased to 3.6 mm. In the volunteers the Average Error averaged over all No-Motion acquisitions was 1.0 mm. For the case of translational motion, which might be expected to be RBM, the Average Error averaged over all volunteer studies increased to 2.6 mm, which was statistically different from the No-Motion studies. For the case of bends and twists of the torso, which would be expected to challenge the RBM model, the Average Error averaged over all such volunteer studies was 4.9 mm and was again statistically different. Investigations of motion of the arm including just bending at the elbow and leg motion resulted in Average Errors which were not statistically different from the No-Motion studies. However, when shoulder movement was included with arm motion the Average Error was near that of torso bends and twists, and statistically different.

conclusionsUse of the RBM model with VTS predictions of heart motion during reconstruction should decrease the extent of artifacts for the types of patient motion studied. The impact of correction would be less for torso bends and twists, and arm motion which includes the shoulders.

Indexed as

Myocardial Perfusion ImagingAnthropometryArtifactsAutomationCalibrationElectrocardiographyEquipment DesignFemaleHealthy VolunteersHeartHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingMaleMovement

Identifiers

PMID24320463
PMCPMC3815050
OpenAlexW2082885903

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.