Evidence map›Paper›PMID 27681278›Full record

ArticleRadiology2017

Diffusion Tractography of the Entire Left Ventricle by Using Free-breathing Accelerated Simultaneous Multisection Imaging.

Choukri Mekkaoui, Timothy G Reese, Marcel P Jackowski, Stephen F Cauley, Kawin Setsompop, Himanshu Bhat, David E Sosnovik

Registry-linked trialOpen access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02973633 (Diffusion MRI in Heart Failure), which is not on this map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
4.8field-weighted citation impact, top 5% 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.

NCT02973633 early_phase1recruitingnot on this map

Diffusion MRI in Heart Failure

TypeinterventionalSponsorMassachusetts General HospitalRan2017 to 2028Enrolled160ConditionsHeart Failure, Myocardial Infarction, Left Ventricular HypertrophyArmsDiffusion Tensor MRI (DTI), Gadolinium DOTA Meglumine
3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Accelerating cardiac diffusion tensor imaging with deep learning-based tensor de-noising and breath hold reduction. A step towards improved efficiency and clinical feasibility.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025
    Article
  6. Article
  7. An Overview of Deep Learning Methods for Left Ventricle Segmentation.Computational intelligence and neuroscience · 2023
    Review
  8. Article
  9. The relationship between myocardial microstructure and strain in chronic infarction using cardiovascular magnetic resonance diffusion tensor imaging and feature tracking.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. Myofiber strain in healthy humans using DENSE and cDTI.Magnetic resonance in medicine · 2021
    Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Imaging the Microstructure of the Human Fetal Heart.Circulation. Cardiovascular imaging · 2018
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Choukri MekkaouiFrom the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (C.M., T.G.R., S.F.C., K.S., D.E.S.), and Cardiovascular Research Center, Cardiology Division (D.E.S.), Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129; Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil (M.P.J.); and Siemens Healthcare, Charlestown, Mass (H.B.).
Timothy G ReeseFrom the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (C.M., T.G.R., S.F.C., K.S., D.E.S.), and Cardiovascular Research Center, Cardiology Division (D.E.S.), Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129; Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil (M.P.J.); and Siemens Healthcare, Charlestown, Mass (H.B.).
Marcel P JackowskiFrom the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (C.M., T.G.R., S.F.C., K.S., D.E.S.), and Cardiovascular Research Center, Cardiology Division (D.E.S.), Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129; Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil (M.P.J.); and Siemens Healthcare, Charlestown, Mass (H.B.).
Stephen F CauleyFrom the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (C.M., T.G.R., S.F.C., K.S., D.E.S.), and Cardiovascular Research Center, Cardiology Division (D.E.S.), Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129; Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil (M.P.J.); and Siemens Healthcare, Charlestown, Mass (H.B.).
Kawin SetsompopFrom the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (C.M., T.G.R., S.F.C., K.S., D.E.S.), and Cardiovascular Research Center, Cardiology Division (D.E.S.), Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129; Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil (M.P.J.); and Siemens Healthcare, Charlestown, Mass (H.B.).
Himanshu BhatFrom the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (C.M., T.G.R., S.F.C., K.S., D.E.S.), and Cardiovascular Research Center, Cardiology Division (D.E.S.), Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129; Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil (M.P.J.); and Siemens Healthcare, Charlestown, Mass (H.B.).
David E SosnovikFrom the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (C.M., T.G.R., S.F.C., K.S., D.E.S.), and Cardiovascular Research Center, Cardiology Division (D.E.S.), Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129; Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil (M.P.J.); and Siemens Healthcare, Charlestown, Mass (H.B.).
Harvard University · US

Funding

ULTRAHIGH FIELD TECHNOLOGY FOR FUNCTIONAL IMAGINGP41RR014075 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI BOAS, DAVID A · 1999 to 2011
$16.4M
Cardiac MRI-Tractography In Vivo: Integrated Imaging of Structure and FunctionR01HL112831 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI SOSNOVIK, DAVID E · 2013 to 2016
$3.0M
Molecular and Microstructural Imaging of Cardiomyocyte Apoptosis and AutophagyR01HL093038 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI SOSNOVIK, DAVID E · 2008 to 2012
$2.2M
Free-Breathing Diffusion Tensor MRI in Patients Following Myocardial InfarctionR01HL131635 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI MEKKAOUI, CHOUKRI · 2016 to 2020
$2.1M
Tracking Cardiac Remodeling in Humans Using In Vivo Diffusion Tensor MRIR56HL125590 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI MEKKAOUI, CHOUKRI · 2015 to 2015
$435k
NCRR NIH HHS P41 RR014075NHLBI NIH HHS R01 HL093038NHLBI NIH HHS R01 HL112831NHLBI NIH HHS R01 HL131635NHLBI NIH HHS R56 HL125590
6 · The paper itself

Abstract

Purpose To develop a clinically feasible whole-heart free-breathing diffusion-tensor (DT) magnetic resonance (MR) imaging approach with an imaging time of approximately 15 minutes to enable three-dimensional (3D) tractography. Materials and Methods The study was compliant with HIPAA and the institutional review board and required written consent from the participants. DT imaging was performed in seven healthy volunteers and three patients with pulmonary hypertension by using a stimulated echo sequence. Twelve contiguous short-axis sections and six four-chamber sections that covered the entire left ventricle were acquired by using simultaneous multisection (SMS) excitation with a blipped-controlled aliasing in parallel imaging readout. Rate 2 and rate 3 SMS excitation was defined as two and three times accelerated in the section axis, respectively. Breath-hold and free-breathing images with and without SMS acceleration were acquired. Diffusion-encoding directions were acquired sequentially, spatiotemporally registered, and retrospectively selected by using an entropy-based approach. Myofiber helix angle, mean diffusivity, fractional anisotropy, and 3D tractograms were analyzed by using paired t tests and analysis of variance. Results No significant differences (P > .63) were seen between breath-hold rate 3 SMS and free-breathing rate 2 SMS excitation in transmural myofiber helix angle, mean diffusivity (mean ± standard deviation, [0.89 ± 0.09] × 10

Indexed as

RespirationDiffusion Tensor ImagingFeasibility StudiesHeart VentriclesHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance Imaging

Identifiers

PMID27681278
PMCPMC5318239
OpenAlexW2525420465

What OpenQuestion holds

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Registered trials

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.