Evidence map›Paper›PMID 27160300›Full record

ArticleMagma (New York, N.Y.)2016

A novel alternative to classify tissues from T 1 and T 2 relaxation times for prostate MRI.

Jorge Zavala Bojorquez, Stéphanie Bricq, François Brunotte, Paul M Walker, Alain Lalande

Abstract read
PubMed Publisher
In one paragraph

Article in Magma (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020
    Article
  9. Article
  10. Article
  11. 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

5 authors.

Jorge Zavala BojorquezLE2I, UMR 6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 21000, Dijon, France. Jorge-Arturo.Zavala-Bojorquez@u-bourgogne.fr.
Stéphanie BricqLE2I, UMR 6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 21000, Dijon, France.
François BrunotteLE2I, UMR 6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 21000, Dijon, France.
Paul M WalkerLE2I, UMR 6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 21000, Dijon, France.
Alain LalandeLE2I, UMR 6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 21000, Dijon, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo segment and classify the different attenuation regions from MRI at the pelvis level using the T 1 and T 2 relaxation times and anatomical knowledge as a first step towards the creation of PET/MR attenuation maps. MATERIALS AND

methodsRelaxation times were calculated by fitting the pixel-wise intensities of acquired T 1- and T 2-weighted images from eight men with inversion-recovery and multi-echo multi-slice spin-echo sequences. A decision binary tree based on relaxation times was implemented to segment and classify fat, muscle, prostate, and air (within the body). Connected component analysis and an anatomical knowledge-based procedure were implemented to localize the background and bone.

resultsRelaxation times at 3 T are reported for fat (T 1 = 385 ms, T 2 = 121 ms), muscle (T 1 = 1295 ms, T 2 = 40 ms), and prostate (T 1 = 1700 ms, T 2 = 80 ms). The relaxation times allowed the segmentation-classification of fat, prostate, muscle, and air, and combined with anatomical knowledge, they allowed classification of bone. The good segmentation-classification of prostate [mean Dice similarity score (mDSC) = 0.70] suggests a viable implementation in oncology and that of fat (mDSC = 0.99), muscle (mDSC = 0.99), and bone (mDSCs = 0.78) advocates for its implementation in PET/MR attenuation correction.

conclusionOur method allows the segmentation and classification of the attenuation-relevant structures required for the generation of the attenuation map of PET/MR systems in prostate imaging: air, background, bone, fat, muscle, and prostate.

Indexed as

Magnetic Resonance ImagingAlgorithmsArtifactsDecision TreesHumansImage Processing, Computer-AssistedMaleMusclesPositron-Emission TomographyProstateProstatic NeoplasmsTime FactorsAttenuation correctionClassificationMRIProstateT 1 and T 2 relaxation times

Identifiers

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.