Loading…

An L 1 -plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset

Magnetic Particle Imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-line...

Full description

Saved in:
Bibliographic Details
Published in:Physics in medicine & biology 2025-01
Main Authors: Gapyak, Vladyslav, Rentschler, Corinna Erika, März, Thomas, Weinmann, Andreas
Format: Article
Language:English
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title Physics in medicine & biology
container_volume
creator Gapyak, Vladyslav
Rentschler, Corinna Erika
März, Thomas
Weinmann, Andreas
description Magnetic Particle Imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a Plug-and-Play approach based on a generic zero-shot denoiser with an $\ell^1$-prior. Approach: We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, ART, DIP and the previous PP-MPI, which is a Plug-and-Play method with denoiser trained on MPI-friendly data. Main results: We derive a Plug-and-Play reconstruction method based on a generic zero-shot denoiser. Addressing (hyper)parameter selection, we perform an extended parameter search on a hybrid validation dataset we produced and apply the derived parameters for reconstruction on the 3D Open MPI Dataset. We offer a quantitative and qualitative evaluation of the zero-shot Plug-and-Play approach on the 3D Open MPI dataset with the validated parameters. Moreover, we show the quality of the approach with different levels of preprocessing of the data. Significance: The proposed method employs a zero-shot denoiser which has not been trained for the MPI reconstruction task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.
doi_str_mv 10.1088/1361-6560/ada5a1
format article
fullrecord <record><control><sourceid>pubmed_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1088_1361_6560_ada5a1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>39752878</sourcerecordid><originalsourceid>FETCH-LOGICAL-c648-6de1cae555c4097bfdf5a2b8c1e180524e4358703f8bc96a19e9c5090c4f55283</originalsourceid><addsrcrecordid>eNo9kE1Lw0AQhhdRbK3ePcn8gdjdbDbZHEv9KlT00HuYbCZNJM2G3USpv97UamFghoHnhfdh7Fbwe8G1ngsZiyBWMZ9jgQrFGZueXudsyrkUQSqUmrAr7z84F0KH0SWbyDRRoU70lDWLFtYgIOiaYRtgW4wH7gG7zlk0FZTWwev7CgZft1tA-CZnwVe2h4JaW3ty8FX3FdAnNgP2tW1hnL4ikA9gO2p_6QJ79NRfs4sSG083f3vGNk-Pm-VLsH57Xi0X68DEkQ7igoRBUkqZiKdJXhalwjDXRpDQXIURRVLphMtS5yaNUaSUGsVTbqJSjbXkjPFjrHHWe0dl1rl6h26fCZ4dvGUHSdlBUnb0NiJ3R6Qb8h0VJ-BflPwBaytoIg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>An L 1 -plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset</title><source>Institute of Physics</source><creator>Gapyak, Vladyslav ; Rentschler, Corinna Erika ; März, Thomas ; Weinmann, Andreas</creator><creatorcontrib>Gapyak, Vladyslav ; Rentschler, Corinna Erika ; März, Thomas ; Weinmann, Andreas</creatorcontrib><description>Magnetic Particle Imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a Plug-and-Play approach based on a generic zero-shot denoiser with an $\ell^1$-prior. Approach: We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, ART, DIP and the previous PP-MPI, which is a Plug-and-Play method with denoiser trained on MPI-friendly data. Main results: We derive a Plug-and-Play reconstruction method based on a generic zero-shot denoiser. Addressing (hyper)parameter selection, we perform an extended parameter search on a hybrid validation dataset we produced and apply the derived parameters for reconstruction on the 3D Open MPI Dataset. We offer a quantitative and qualitative evaluation of the zero-shot Plug-and-Play approach on the 3D Open MPI dataset with the validated parameters. Moreover, we show the quality of the approach with different levels of preprocessing of the data. Significance: The proposed method employs a zero-shot denoiser which has not been trained for the MPI reconstruction task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.</description><identifier>ISSN: 0031-9155</identifier><identifier>EISSN: 1361-6560</identifier><identifier>DOI: 10.1088/1361-6560/ada5a1</identifier><identifier>PMID: 39752878</identifier><language>eng</language><publisher>England</publisher><ispartof>Physics in medicine &amp; biology, 2025-01</ispartof><rights>2025 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0009-0006-8822-9089</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39752878$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gapyak, Vladyslav</creatorcontrib><creatorcontrib>Rentschler, Corinna Erika</creatorcontrib><creatorcontrib>März, Thomas</creatorcontrib><creatorcontrib>Weinmann, Andreas</creatorcontrib><title>An L 1 -plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset</title><title>Physics in medicine &amp; biology</title><addtitle>Phys Med Biol</addtitle><description>Magnetic Particle Imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a Plug-and-Play approach based on a generic zero-shot denoiser with an $\ell^1$-prior. Approach: We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, ART, DIP and the previous PP-MPI, which is a Plug-and-Play method with denoiser trained on MPI-friendly data. Main results: We derive a Plug-and-Play reconstruction method based on a generic zero-shot denoiser. Addressing (hyper)parameter selection, we perform an extended parameter search on a hybrid validation dataset we produced and apply the derived parameters for reconstruction on the 3D Open MPI Dataset. We offer a quantitative and qualitative evaluation of the zero-shot Plug-and-Play approach on the 3D Open MPI dataset with the validated parameters. Moreover, we show the quality of the approach with different levels of preprocessing of the data. Significance: The proposed method employs a zero-shot denoiser which has not been trained for the MPI reconstruction task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.</description><issn>0031-9155</issn><issn>1361-6560</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><recordid>eNo9kE1Lw0AQhhdRbK3ePcn8gdjdbDbZHEv9KlT00HuYbCZNJM2G3USpv97UamFghoHnhfdh7Fbwe8G1ngsZiyBWMZ9jgQrFGZueXudsyrkUQSqUmrAr7z84F0KH0SWbyDRRoU70lDWLFtYgIOiaYRtgW4wH7gG7zlk0FZTWwev7CgZft1tA-CZnwVe2h4JaW3ty8FX3FdAnNgP2tW1hnL4ikA9gO2p_6QJ79NRfs4sSG083f3vGNk-Pm-VLsH57Xi0X68DEkQ7igoRBUkqZiKdJXhalwjDXRpDQXIURRVLphMtS5yaNUaSUGsVTbqJSjbXkjPFjrHHWe0dl1rl6h26fCZ4dvGUHSdlBUnb0NiJ3R6Qb8h0VJ-BflPwBaytoIg</recordid><startdate>20250103</startdate><enddate>20250103</enddate><creator>Gapyak, Vladyslav</creator><creator>Rentschler, Corinna Erika</creator><creator>März, Thomas</creator><creator>Weinmann, Andreas</creator><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0009-0006-8822-9089</orcidid></search><sort><creationdate>20250103</creationdate><title>An L 1 -plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset</title><author>Gapyak, Vladyslav ; Rentschler, Corinna Erika ; März, Thomas ; Weinmann, Andreas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c648-6de1cae555c4097bfdf5a2b8c1e180524e4358703f8bc96a19e9c5090c4f55283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2025</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gapyak, Vladyslav</creatorcontrib><creatorcontrib>Rentschler, Corinna Erika</creatorcontrib><creatorcontrib>März, Thomas</creatorcontrib><creatorcontrib>Weinmann, Andreas</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Physics in medicine &amp; biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gapyak, Vladyslav</au><au>Rentschler, Corinna Erika</au><au>März, Thomas</au><au>Weinmann, Andreas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An L 1 -plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset</atitle><jtitle>Physics in medicine &amp; biology</jtitle><addtitle>Phys Med Biol</addtitle><date>2025-01-03</date><risdate>2025</risdate><issn>0031-9155</issn><eissn>1361-6560</eissn><abstract>Magnetic Particle Imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a Plug-and-Play approach based on a generic zero-shot denoiser with an $\ell^1$-prior. Approach: We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, ART, DIP and the previous PP-MPI, which is a Plug-and-Play method with denoiser trained on MPI-friendly data. Main results: We derive a Plug-and-Play reconstruction method based on a generic zero-shot denoiser. Addressing (hyper)parameter selection, we perform an extended parameter search on a hybrid validation dataset we produced and apply the derived parameters for reconstruction on the 3D Open MPI Dataset. We offer a quantitative and qualitative evaluation of the zero-shot Plug-and-Play approach on the 3D Open MPI dataset with the validated parameters. Moreover, we show the quality of the approach with different levels of preprocessing of the data. Significance: The proposed method employs a zero-shot denoiser which has not been trained for the MPI reconstruction task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.</abstract><cop>England</cop><pmid>39752878</pmid><doi>10.1088/1361-6560/ada5a1</doi><orcidid>https://orcid.org/0009-0006-8822-9089</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0031-9155
ispartof Physics in medicine & biology, 2025-01
issn 0031-9155
1361-6560
language eng
recordid cdi_crossref_primary_10_1088_1361_6560_ada5a1
source Institute of Physics
title An L 1 -plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T15%3A14%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pubmed_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20L%201%20-plug-and-play%20approach%20for%20MPI%20using%20a%20zero%20shot%20denoiser%20with%20evaluation%20on%20the%203D%20open%20MPI%20dataset&rft.jtitle=Physics%20in%20medicine%20&%20biology&rft.au=Gapyak,%20Vladyslav&rft.date=2025-01-03&rft.issn=0031-9155&rft.eissn=1361-6560&rft_id=info:doi/10.1088/1361-6560/ada5a1&rft_dat=%3Cpubmed_cross%3E39752878%3C/pubmed_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c648-6de1cae555c4097bfdf5a2b8c1e180524e4358703f8bc96a19e9c5090c4f55283%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/39752878&rfr_iscdi=true