{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:case1346966179"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:case1346966179","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"Optimization of Fast MR Imaging Technologies using the Case-PDM to Quantitatively Assess Image Quality","abstract":"There exist an extraordinary number of ways to create an MR image, and the number seems to grow daily. In almost all cases, images will be viewed by radiologists to make a diagnosis, to stage a disease, to apply a treatment, and/or to assess a treatment. Since images are viewed, one needs to assess visual image quality, preferably in a quantitative way. We developed a perceptual difference model, Case-PDM, to quantitatively assess image quality, in a way well-correlated to clinical needs, and demonstrate how the methods can be used to improve MR imaging techniques. We validated existing Case-PDM with advanced observer experiments. Human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms were compared to Case-PDM and competing methods such as IDM (Sarnoff Corporation) and SSIM (Wang et al.). Global image quality is quantified by comparing fast acquired, reconstructed image to slower, full k-space, high quality reference image. We used advanced human observer experimental methods (DSCQS, FMT, 2AFC) to prove these methods on the contexts of human-model correlation, comparability of model evaluation scores across different image contents, and imperceptible difference threshold discrimination. To date, most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. We used an advanced observer experiment and Artifact-PDM, an extension of Case-PDM, to measure relative disturbance of MR image artifacts to radiologists. We used a Functional Measurement Theory (FMT) pair-comparison experiment to measure the disturbance of each artifact to human observers. Radiologists showed preferences towards particular image artifacts, the relative disturbances of which can be quantitatively measured by both observer study and Artifact-PDM. We also applied our methodology to a novel MRI reconstruction algorithm, such as Compressed Sensing (CS) which allows MRI reconstruction from incoherent/random partial k-space samples, to optimize various parameters and study parametric sensitivity, using a large number in vivo MRI data. We conclude that our PDM can faithfully represent human observer image quality evaluation and can be useful in evaluating reconstruction algorithms, especially in evaluating artifact trade-offs, to improve diagnostic accuracy for clinical protocols.","abstract_html":"There exist an extraordinary number of ways to create an MR image, and the number seems to grow daily. In almost all cases, images will be viewed by radiologists to make a diagnosis, to stage a disease, to apply a treatment, and/or to assess a treatment. Since images are viewed, one needs to assess visual image quality, preferably in a quantitative way. We developed a perceptual difference model, Case-PDM, to quantitatively assess image quality, in a way well-correlated to clinical needs, and demonstrate how the methods can be used to improve MR imaging techniques. We validated existing Case-PDM with advanced observer experiments. Human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms were compared to Case-PDM and competing methods such as IDM (Sarnoff Corporation) and SSIM (Wang et al.). Global image quality is quantified by comparing fast acquired, reconstructed image to slower, full k-space, high quality reference image. We used advanced human observer experimental methods (DSCQS, FMT, 2AFC) to prove these methods on the contexts of human-model correlation, comparability of model evaluation scores across different image contents, and imperceptible difference threshold discrimination. To date, most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. We used an advanced observer experiment and Artifact-PDM, an extension of Case-PDM, to measure relative disturbance of MR image artifacts to radiologists. We used a Functional Measurement Theory (FMT) pair-comparison experiment to measure the disturbance of each artifact to human observers. Radiologists showed preferences towards particular image artifacts, the relative disturbances of which can be quantitatively measured by both observer study and Artifact-PDM. We also applied our methodology to a novel MRI reconstruction algorithm, such as Compressed Sensing (CS) which allows MRI reconstruction from incoherent/random partial k-space samples, to optimize various parameters and study parametric sensitivity, using a large number in vivo MRI data. We conclude that our PDM can faithfully represent human observer image quality evaluation and can be useful in evaluating reconstruction algorithms, especially in evaluating artifact trade-offs, to improve diagnostic accuracy for clinical protocols.","abstract_has_math":false,"creators":["Miao, Jun"],"institution":"Case Western Reserve University School of Graduate Studies","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Biomedical Engineering","degree_department":null,"school":null,"contributors":["Wilson, David"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-03-08","date_published":"2013-03-08","updated_at":"2026-07-24T03:35:52Z","subjects":["Biomedical Engineering","Medical Imaging","Perceptual difference model","Image quality","Image artifact","Magnetic Resonance Imaging","Reconstruction Algorithm","Observer Study"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=case1346966179","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wilson, David"]},{"key":"dc:creator","label":"Author","values":["Miao, Jun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-03-08"]},{"key":"dc:publisher","label":"Institution","values":["Case Western Reserve University School of Graduate Studies / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biomedical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Case Western Reserve University School of Graduate Studies"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biomedical Engineering","Medical Imaging","Perceptual difference model","Image quality","Image artifact","Magnetic Resonance Imaging","Reconstruction Algorithm","Observer Study"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=case1346966179"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["There exist an extraordinary number of ways to create an MR image, and the number seems to grow daily. In almost all cases, images will be viewed by radiologists to make a diagnosis, to stage a disease, to apply a treatment, and/or to assess a treatment. Since images are viewed, one needs to assess visual image quality, preferably in a quantitative way. We developed a perceptual difference model, Case-PDM, to quantitatively assess image quality, in a way well-correlated to clinical needs, and demonstrate how the methods can be used to improve MR imaging techniques. We validated existing Case-PDM with advanced observer experiments. Human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms were compared to Case-PDM and competing methods such as IDM (Sarnoff Corporation) and SSIM (Wang et al.). Global image quality is quantified by comparing fast acquired, reconstructed image to slower, full k-space, high quality reference image. We used advanced human observer experimental methods (DSCQS, FMT, 2AFC) to prove these methods on the contexts of human-model correlation, comparability of model evaluation scores across different image contents, and imperceptible difference threshold discrimination. To date, most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. We used an advanced observer experiment and Artifact-PDM, an extension of Case-PDM, to measure relative disturbance of MR image artifacts to radiologists. We used a Functional Measurement Theory (FMT) pair-comparison experiment to measure the disturbance of each artifact to human observers. Radiologists showed preferences towards particular image artifacts, the relative disturbances of which can be quantitatively measured by both observer study and Artifact-PDM. We also applied our methodology to a novel MRI reconstruction algorithm, such as Compressed Sensing (CS) which allows MRI reconstruction from incoherent/random partial k-space samples, to optimize various parameters and study parametric sensitivity, using a large number in vivo MRI data. We conclude that our PDM can faithfully represent human observer image quality evaluation and can be useful in evaluating reconstruction algorithms, especially in evaluating artifact trade-offs, to improve diagnostic accuracy for clinical protocols."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.182","6.42 MB"]},{"key":"dc:title","label":"Title","values":["Optimization of Fast MR Imaging Technologies using the Case-PDM to Quantitatively Assess Image Quality"]}]}],"canonical_facts":{"dc:contributor":["Wilson, David"],"dc:creator":["Miao, Jun"],"dc:date":["2013-03-08"],"dc:description":["There exist an extraordinary number of ways to create an MR image, and the number seems to grow daily. In almost all cases, images will be viewed by radiologists to make a diagnosis, to stage a disease, to apply a treatment, and/or to assess a treatment. Since images are viewed, one needs to assess visual image quality, preferably in a quantitative way. We developed a perceptual difference model, Case-PDM, to quantitatively assess image quality, in a way well-correlated to clinical needs, and demonstrate how the methods can be used to improve MR imaging techniques. We validated existing Case-PDM with advanced observer experiments. Human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms were compared to Case-PDM and competing methods such as IDM (Sarnoff Corporation) and SSIM (Wang et al.). Global image quality is quantified by comparing fast acquired, reconstructed image to slower, full k-space, high quality reference image. We used advanced human observer experimental methods (DSCQS, FMT, 2AFC) to prove these methods on the contexts of human-model correlation, comparability of model evaluation scores across different image contents, and imperceptible difference threshold discrimination. To date, most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. We used an advanced observer experiment and Artifact-PDM, an extension of Case-PDM, to measure relative disturbance of MR image artifacts to radiologists. We used a Functional Measurement Theory (FMT) pair-comparison experiment to measure the disturbance of each artifact to human observers. Radiologists showed preferences towards particular image artifacts, the relative disturbances of which can be quantitatively measured by both observer study and Artifact-PDM. We also applied our methodology to a novel MRI reconstruction algorithm, such as Compressed Sensing (CS) which allows MRI reconstruction from incoherent/random partial k-space samples, to optimize various parameters and study parametric sensitivity, using a large number in vivo MRI data. We conclude that our PDM can faithfully represent human observer image quality evaluation and can be useful in evaluating reconstruction algorithms, especially in evaluating artifact trade-offs, to improve diagnostic accuracy for clinical protocols."],"dc:format":["application/pdf","p.182","6.42 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=case1346966179"],"dc:language":["English"],"dc:publisher":["Case Western Reserve University School of Graduate Studies / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Biomedical Engineering","Medical Imaging","Perceptual difference model","Image quality","Image artifact","Magnetic Resonance Imaging","Reconstruction Algorithm","Observer Study"],"dc:title":["Optimization of Fast MR Imaging Technologies using the Case-PDM to Quantitatively Assess Image Quality"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Biomedical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Case Western Reserve University School of Graduate Studies"]},"updated_at":"2026-07-24T03:35:52Z"}