Evidence map›Paper›PMID 36961014›Full record

ArticleTomography (Ann Arbor, Mich.)2023

Characterization of Effects of Compressed Sensing on High Spectral and Spatial Resolution (HiSS) MRI with Comparison to SENSE.

Milica Medved, Marco Vicari, Gregory S Karczmar

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Article in Tomography (Ann Arbor, Mich.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Milica MedvedDepartment of Radiology, University of Chicago, Chicago, IL 60637, USA.ORCID 0000-0003-2945-5511
Marco VicariFraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany.ORCID 0000-0003-4840-9967
Gregory S KarczmarDepartment of Radiology, University of Chicago, Chicago, IL 60637, USA.

Funding

Assessment of Breast Cancer Risk with High Spectral and Spatial Resolution MRIR01CA167785 · NCI · UNIVERSITY OF CHICAGO · PI KARCZMAR, GREGORY S., NEWSTEAD, GILLIAN · 2013 to 2017
$2.4M
NCI NIH HHS R01 CA167785
6 · The paper itself

Abstract

High Spectral and Spatial resolution (HiSS) MRI shows high diagnostic performance in the breast. Acceleration methods based on k-space undersampling could allow stronger T2*-based image contrast and/or higher spectral resolution, potentially increasing diagnostic performance. An agar/oil phantom was prepared with water-fat boundaries perpendicular to the readout and phase encoding directions in a breast coil. HiSS MRI was acquired at 3T, at sensitivity encoding (SENSE) acceleration factors R of up to 10, and the R = 1 dataset was used to simulate corresponding compressed sensing (CS) accelerations. Image quality was evaluated by quantifying noise and artifact levels. Effective spatial resolution was determined via modulation transfer function analysis. Dispersion vs. absorption (DISPA) analysis and full width at half maximum (FWHM) quantified spectral lineshape changes. Noise levels remained constant with R for CS but amplified with SENSE. SENSE preserved the spatial resolution of HiSS MRI, while CS reduced it in the phase encoding direction. SENSE showed no effect on FWHM or DISPA markers, while CS increased FWHM. Thus, CS might perform better in noise-limited or geometrically constrained applications, but in geometric configurations specific to breast MRI, spectral analysis might be compromised, decreasing the diagnostic performance of HiSS MRI.

Indexed as

ArtifactsMagnetic Resonance ImagingPhantoms, Imagingcompressed sensing (CS)dispersion vs. absorption (DISPA) analysishigh spectral and spatial resolution (HiSS) MRIimage qualitymodulation transfer functionspectral lineshape

Identifiers

PMID36961014
PMCPMC10037569

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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.