IMAGE RECONSTRUCTION USING MACHINE LEARNING REGULARIZERS
A system and method for reconstructing an image of a target object using an iterative reconstruction technique can include a machine learning model as a regularization filter (100). An image data set for a target object generated using an imaging modality can be received, and an image of the target...
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creator | RAMESH, Nisha ATRIA, Cristian YATSENKO, Dimitri |
description | A system and method for reconstructing an image of a target object using an iterative reconstruction technique can include a machine learning model as a regularization filter (100). An image data set for a target object generated using an imaging modality can be received, and an image of the target object can be reconstructed using an iterative reconstruction technique that includes a machine learning model as a regularization filter (100) used in part to reconstruct the image of the target object. The machine learning model can be trained prior to receiving the image data using learning datasets that have image data associated with the target object, where the learning datasets providing objective data for training the machine learning model, and the machine learning model can be included in the iterative reconstruction technique to introduce the object features into the image of the target object being reconstructed. |
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An image data set for a target object generated using an imaging modality can be received, and an image of the target object can be reconstructed using an iterative reconstruction technique that includes a machine learning model as a regularization filter (100) used in part to reconstruct the image of the target object. The machine learning model can be trained prior to receiving the image data using learning datasets that have image data associated with the target object, where the learning datasets providing objective data for training the machine learning model, and the machine learning model can be included in the iterative reconstruction technique to introduce the object features into the image of the target object being reconstructed.</description><language>eng ; fre ; ger</language><subject>CALCULATING ; COMPUTING ; COUNTING ; DIAGNOSIS ; HUMAN NECESSITIES ; HYGIENE ; IDENTIFICATION ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; MEDICAL OR VETERINARY SCIENCE ; PHYSICS ; SURGERY</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200729&DB=EPODOC&CC=EP&NR=3685350A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200729&DB=EPODOC&CC=EP&NR=3685350A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>RAMESH, Nisha</creatorcontrib><creatorcontrib>ATRIA, Cristian</creatorcontrib><creatorcontrib>YATSENKO, Dimitri</creatorcontrib><title>IMAGE RECONSTRUCTION USING MACHINE LEARNING REGULARIZERS</title><description>A system and method for reconstructing an image of a target object using an iterative reconstruction technique can include a machine learning model as a regularization filter (100). An image data set for a target object generated using an imaging modality can be received, and an image of the target object can be reconstructed using an iterative reconstruction technique that includes a machine learning model as a regularization filter (100) used in part to reconstruct the image of the target object. The machine learning model can be trained prior to receiving the image data using learning datasets that have image data associated with the target object, where the learning datasets providing objective data for training the machine learning model, and the machine learning model can be included in the iterative reconstruction technique to introduce the object features into the image of the target object being reconstructed.</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DIAGNOSIS</subject><subject>HUMAN NECESSITIES</subject><subject>HYGIENE</subject><subject>IDENTIFICATION</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>MEDICAL OR VETERINARY SCIENCE</subject><subject>PHYSICS</subject><subject>SURGERY</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLDw9HV0d1UIcnX29wsOCQp1DvH091MIDfb0c1fwdXT28PRzVfBxdQzyAwkEubqH-jgGeUa5BgXzMLCmJeYUp_JCaW4GBTfXEGcP3dSC_PjU4oLE5NS81JJ41wBjMwtTY1MDR0NjIpQAADwyKHI</recordid><startdate>20200729</startdate><enddate>20200729</enddate><creator>RAMESH, Nisha</creator><creator>ATRIA, Cristian</creator><creator>YATSENKO, Dimitri</creator><scope>EVB</scope></search><sort><creationdate>20200729</creationdate><title>IMAGE RECONSTRUCTION USING MACHINE LEARNING REGULARIZERS</title><author>RAMESH, Nisha ; ATRIA, Cristian ; YATSENKO, Dimitri</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP3685350A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DIAGNOSIS</topic><topic>HUMAN NECESSITIES</topic><topic>HYGIENE</topic><topic>IDENTIFICATION</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>MEDICAL OR VETERINARY SCIENCE</topic><topic>PHYSICS</topic><topic>SURGERY</topic><toplevel>online_resources</toplevel><creatorcontrib>RAMESH, Nisha</creatorcontrib><creatorcontrib>ATRIA, Cristian</creatorcontrib><creatorcontrib>YATSENKO, Dimitri</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>RAMESH, Nisha</au><au>ATRIA, Cristian</au><au>YATSENKO, Dimitri</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>IMAGE RECONSTRUCTION USING MACHINE LEARNING REGULARIZERS</title><date>2020-07-29</date><risdate>2020</risdate><abstract>A system and method for reconstructing an image of a target object using an iterative reconstruction technique can include a machine learning model as a regularization filter (100). An image data set for a target object generated using an imaging modality can be received, and an image of the target object can be reconstructed using an iterative reconstruction technique that includes a machine learning model as a regularization filter (100) used in part to reconstruct the image of the target object. The machine learning model can be trained prior to receiving the image data using learning datasets that have image data associated with the target object, where the learning datasets providing objective data for training the machine learning model, and the machine learning model can be included in the iterative reconstruction technique to introduce the object features into the image of the target object being reconstructed.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTING COUNTING DIAGNOSIS HUMAN NECESSITIES HYGIENE IDENTIFICATION IMAGE DATA PROCESSING OR GENERATION, IN GENERAL MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY |
title | IMAGE RECONSTRUCTION USING MACHINE LEARNING REGULARIZERS |
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