Evidence map›Paper›PMID 39894238›Full record

ArticleNeuroImage2025

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

Paul J Weiser, Georg Langs, Wolfgang Bogner, Stanislav Motyka, Bernhard Strasser, Polina Golland, Nalini Singh, Jorg Dietrich, Erik Uhlmann, Tracy Batchelor and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

14 authors.

Paul J WeiserAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computational Imaging Research Lab - Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria. Electronic address: pweiser1@mgh.harvard.edu.
Georg LangsComputational Imaging Research Lab - Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Wolfgang BognerHigh Field MR Center - Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Stanislav MotykaComputational Imaging Research Lab - Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Bernhard StrasserHigh Field MR Center - Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Polina GollandComputer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.
Nalini SinghComputer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.
Jorg DietrichPappas Center for Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Erik UhlmannDepartment of Neurology, Beth-Israel Deaconess Medical Center, Boston, MA, USA.
Tracy BatchelorDepartment of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
Daniel CahillDepartment of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.
Malte HoffmannAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Antoine KlauserAdvanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Geneva, Switzerland.
Ovidiu C AndronesiAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: oandronesi@mgh.harvard.edu.

Funding

Targeting the Vasular SystemP50CA165962 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI WENDY Beth LONDON · 2013 to 2026
$33.5M
Development of whole-brain in vivo 2HG imaging for precision medicine in mutant IDH gliomaR01CA211080 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Ovidiu C Andronesi · 2017 to 2026
$5.4M
Development of next generation 2HG and metabolic MR imaging for precision oncology of mutant IDH and wildtype glioma patientsR01CA255479 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI ANDRONESI, OVIDIU C · 2021 to 2025
$3.0M
Advancing fMRI Acquisition through Dissemination of EPTI- An Efficient Distortion-Free Multi-Contrast Imaging TechnologyU24NS129893 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Fuyixue Wang · 2023 to 2026
$2.6M
NCI NIH HHS P50 CA165962NCI NIH HHS R01 CA211080NCI NIH HHS R01 CA255479NINDS NIH HHS U24 NS129893
6 · The paper itself

Abstract

introductionAltered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps.

methodsFast high-resolution whole-brain metabolic imaging was performed at 3.4 mm

resultsDeep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial-spectral quality and metabolite quantification with 12%-45% (P<0.05) higher signal-to-noise and 8%-50% (P<0.05) smaller Cramer-Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Deep-ER generalizes reliably to unseen data.

conclusionDeep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine.

Indexed as

BrainBrain NeoplasmsDeep LearningGliomaImage Processing, Computer-AssistedMagnetic Resonance ImagingNeuroimagingAdultFemaleHumansMagnetic Resonance SpectroscopyMaleMiddle AgedPhantoms, ImagingYoung AdultBrainCompressed sensingDeep learningGliomaImage reconstructionMetabolismMR spectroscopic imagingNon-cartesianUltra high field

Identifiers

PMID39894238
PMCPMC11952141

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.