---
title: "Effects of Retrospective Lipid Suppression on Metabolite Quantification in Preclinical Proton MRSI"
id: "biorxiv-5-impacts-of-retrospective-lipid-suppression-on-metabolite-quantification-in"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-5-impacts-of-retrospective-lipid-suppression-on-metabolite-quantification-in"
content_type: "clinical_feed_article"
specialty: "Radiology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.14.751356v1?rss=1"
published_at: "2026-09-21T11:56:41.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Effects of Retrospective Lipid Suppression on Metabolite Quantification in Preclinical Proton MRSI
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-5-impacts-of-retrospective-lipid-suppression-on-metabolite-quantification-in
- **Specialty:** [Radiology](https://medichelpline.com/clinical-feed/radiology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.14.751356v1?rss=1)
- **Published At:** 2026-09-21T11:56:41.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The study evaluated how **retrospective lipid suppression** affects spectral quality, spatial metabolite maps, and quantification variability in preclinical proton magnetic resonance spectroscopic imaging (**MRSI**) of rat brains at **14.1 T**. - Lipid suppression used an **orthogonal projection method** applied to both fully sampled and **compressed sensing** datasets. - Datasets included conditions with minimal and pronounced **lipid contamination** to test the method across realistic contamination levels. - Spectral fitting was performed using two frequency ranges: a broad range (4.1–0.2 ppm) and a narrow range (4.1–1.8 ppm). - When lipid contamination was minimal, retrospective suppression induced only minor spectral and spatial changes, and metabolite quantification remained consistent across conditions. - In datasets with pronounced lipid contamination, suppression produced notable spectral changes that altered spatial metabolite maps and concentration estimates. - Group-level analysis showed that metabolites with **low concentration estimates** were the most affected by suppression. - Similar patterns of effect were observed in compressed sensing datasets, indicating the interaction between lipid suppression and undersampling. - The results inform optimization of retrospective lipid suppression for reliable **metabolite quantification** in preclinical MRSI. - The authors declared no competing interests and reported funding from the Swiss National Science Foundation. Details about specific acquisition parameters and quantitative effect sizes were not reported in the source abstract.
## Clinical Analysis & Structured Key Points
Impacts of retrospective lipid suppression on metabolite quantification in preclinical proton MR spectroscopic imaging | bioRxiv Skip to main content New Results Impacts of retrospective lipid suppression on metabolite quantification in preclinical proton MR spectroscopic imaging View ORCID Profile Tan Toi Phan , Brayan Alves , View ORCID Profile Bernard Lanz , View ORCID Profile Cristina Cudalbu doi: https://doi.org/10.64898/2026.09.14.751356 Tan Toi Phan CIBM Center for Biomedical Imaging, Lausanne, Switzerland; CIBM Pre-Clinical Imaging Section, EPFL - Ecole polytechnique federale de Lausanne, Switzerland; MRS4Brain Group, School of Basic Sciences, EPFL - Ecole polytechnique federale de Lausanne, Switze Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tan Toi Phan For correspondence: tantoi.phan{at}epfl.ch Brayan Alves CIBM Center for Biomedical Imaging, Lausanne, Switzerland; CIBM Pre-Clinical Imaging Section, EPFL - Ecole polytechnique federale de Lausanne, Switzerland; MRS4Brain Group, School of Basic Sciences, EPFL - Ecole polytechnique federale de Lausanne, Switze Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bernard Lanz CIBM Center for Biomedical Imaging, Lausanne, Switzerland; CIBM Pre-Clinical Imaging Section, EPFL - Ecole polytechnique federale de Lausanne, Switzerland; MRS4Brain Group, School of Basic Sciences, EPFL - Ecole polytechnique federale de Lausanne, Switze Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bernard Lanz Cristina Cudalbu CIBM Center for Biomedical Imaging, Lausanne, Switzerland; CIBM Pre-Clinical Imaging Section, EPFL - Ecole polytechnique federale de Lausanne, Switzerland; MRS4Brain Group, School of Basic Sciences, EPFL - Ecole polytechnique federale de Lausanne, Switze Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cristina Cudalbu Abstract Info/History Metrics Supplementary material Preview PDF Abstract Extracranial lipid contamination remains a challenge in proton magnetic resonance spectroscopic imaging (MRSI), especially in short acquisition delay MRSI, where broad lipid resonances overlap with metabolite and macromolecular signals. Although retrospective lipid suppression techniques are widely used in human MRSI, their effects on metabolite quantification in preclinical MRSI, which is more prone to lipid contamination, have not yet been examined. In this study, we assessed how the retrospective lipid suppression and spectral fitting range influence spectral quality, spatial metabolite mapping, and quantification variability using proton MRSI of rat brains at 14.1 T. Lipid suppression was applied via an orthogonal projection method to both fully sampled and compressed sensing datasets, each comprising data with minimal and pronounced lipid contamination. Spectral fitting was performed with both broad (4.1 - 0.2 ppm) and narrow (4.1 - 1.8 ppm) ranges. When lipid contamination was minimal, suppression caused only slight spectral and spatial changes, with consistent metabolite quantification across conditions. In contrast, datasets with pronounced lipid contamination exhibited notable spectral changes following suppression, consequently affecting spatial metabolite mapping and concentration estimates. Group analysis revealed that metabolites with low concentration estimates were most affected. Similar effects were observed in compressed sensing datasets. Our results provide a better understanding of the impact of retrospective lipid suppression on metabolite quantification in preclinical MRSI, thereby supporting future optimizations for its effective application. Competing Interest Statement The authors have declared no competing interest. Funder Information Declared Swiss National Science Foundation , 201218 , 207935 , 10000465 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted September 21, 2026. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Impacts of retrospective lipid suppression on metabolite quantification in preclinical proton MR spectroscopic imaging Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Impacts of retrospective lipid suppression on metabolite quantification in preclinical proton MR spectroscopic imaging Tan Toi Phan , Brayan Alves , Bernard Lanz , Cristina Cudalbu bioRxiv 2026.09.14.751356; doi: https://doi.org/10.64898/2026.09.14.751356 Share This Article: Copy Citation Tools Impacts of retrospective lipid suppression on metabolite quantification in preclinical proton MR spectroscopic imaging Tan Toi Phan , Brayan Alves , Bernard Lanz , Cristina Cudalbu bioRxiv 2026.09.14.751356; doi: https://doi.org/10.64898/2026.09.14.751356 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8013) Biochemistry (18743) Bioengineering (14890) Bioinformatics (44438) Biophysics (22604) Cancer Biology (19727) Cell Biology (26904) Clinical Trials (138) Developmental Biology (13966) Ecology (21006) Epidemiology (2067) Evolutionary Biology (25457) Genetics (16167) Genomics (23510) Immunology (18712) Microbiology (42520) Molecular Biology (18062) Neuroscience (93467) Paleontology (700) Pathology (2979) Pharmacology and Toxicology (5097) Physiology (8114) Plant Biology (16000) Scientific Communication and Education (2095) Synthetic Biology (4560) Systems Biology (10235) Zoology (2391)
## Related Clinical Research

- [Reliability of ultrasound measurement of medial knee joint space width during valgus stress](https://medichelpline.com/clinical-feed/plos-one-4-relative-and-absolute-reliability-of-medial-knee-joint-space-width-measurement.md)
- [SNR-efficient Tensor-Valued Diffusion Encoding Protocol for High-Resolution Brain Microstructure I](https://medichelpline.com/clinical-feed/medrxiv-12-developing-an-snr-efficient-tensor-valued-diffusion-encoding-protocol-for.md)
- [Rule-learning explainable AI for MRI process optimization: global rule models from scanner logs](https://medichelpline.com/clinical-feed/plos-one-20-process-optimization-with-rule-learning-explainable-ai-and-its-application-to.md)
- [Two-Photon–Driven RESOLFT Microscopy Enables Super-Resolution Imaging Deep in Tissue](https://medichelpline.com/clinical-feed/biorxiv-13-fully-two-photon-driven-resolft-microscopy-for-super-resolution-imaging-inside.md)
- [PILR‑U‑Net: Physics‑Informed Latent‑Regularized 3D MRI Segmentation of Pathological Muscle](https://medichelpline.com/clinical-feed/medrxiv-18-3d-segmentation-of-pathological-muscle-with-a-physics-informed-latent.md)

## Navigation
- [← Back to Radiology Feed](https://medichelpline.com/clinical-feed/radiology.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.