View in EDS PDF Full Text

A signal detection model for quantifying overregularization in nonlinear image reconstruction.

Saved in:
Bibliographic Details
Title: A signal detection model for quantifying overregularization in nonlinear image reconstruction.
Authors: Sidky, Emil Y.1 (AUTHOR) sidky@uchicago.edu, Phillips, John Paul1 (AUTHOR), Zhou, Weimin2 (AUTHOR), Ongie, Greg3 (AUTHOR), Cruz‐Bastida, Juan P.1 (AUTHOR), Reiser, Ingrid S.1 (AUTHOR), Anastasio, Mark A.2 (AUTHOR), Pan, Xiaochuan1 (AUTHOR)
Source: Medical Physics. Oct2021, Vol. 48 Issue 10, p6312-6323. 12p.
Subjects: Image reconstruction algorithms, Image reconstruction, Signal detection, Standard deviations
Abstract: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for nonlinear image reconstruction. The vast majority of metrics employed for evaluating nonlinear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to overregularization in the sense that they can favor removal of subtle details in the image. To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details. The metric is demonstrated in the context of a breast CT simulation, where different equal‐dose configurations are considered. The configurations differ in the number of projections acquired. Image reconstruction is performed with a nonlinear algorithm based on total variation constrained least‐squares (TV‐LSQ). The resulting images are studied as a function of three parameters: number of views acquired, total variation constraint value, and number of iterations. The images are evaluated visually, with image RMSE, and with the proposed signal‐detection‐based metric. The latter uses a small signal, and computes detectability in the sinogram and in the reconstructed image. Loss of signal detectability through the image reconstruction process is taken as a quantitative measure of loss of fine details in the image. Loss of signal detectability is seen to correlate well with the blocky or patchy appearance due to overregularization with TV‐LSQ, and this trend runs counter to the image RMSE metric, which tends to favor the over‐regularized images. The proposed signal detection‐based metric provides an image quality assessment that is complimentary to that of image RMSE. Using the two metrics in concert may yield a useful prescription for determining CT algorithm and configuration parameters when nonlinear image reconstruction is used. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 153385043
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A signal detection model for quantifying overregularization in nonlinear image reconstruction.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Sidky%2C+Emil+Y%2E%22">Sidky, Emil Y.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sidky@uchicago.edu</i><br /><searchLink fieldCode="AR" term="%22Phillips%2C+John+Paul%22">Phillips, John Paul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Weimin%22">Zhou, Weimin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ongie%2C+Greg%22">Ongie, Greg</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cruz‐Bastida%2C+Juan+P%2E%22">Cruz‐Bastida, Juan P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reiser%2C+Ingrid+S%2E%22">Reiser, Ingrid S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Anastasio%2C+Mark+A%2E%22">Anastasio, Mark A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Xiaochuan%22">Pan, Xiaochuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Oct2021, Vol. 48 Issue 10, p6312-6323. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Image+reconstruction+algorithms%22">Image reconstruction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for nonlinear image reconstruction. The vast majority of metrics employed for evaluating nonlinear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to overregularization in the sense that they can favor removal of subtle details in the image. To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details. The metric is demonstrated in the context of a breast CT simulation, where different equal‐dose configurations are considered. The configurations differ in the number of projections acquired. Image reconstruction is performed with a nonlinear algorithm based on total variation constrained least‐squares (TV‐LSQ). The resulting images are studied as a function of three parameters: number of views acquired, total variation constraint value, and number of iterations. The images are evaluated visually, with image RMSE, and with the proposed signal‐detection‐based metric. The latter uses a small signal, and computes detectability in the sinogram and in the reconstructed image. Loss of signal detectability through the image reconstruction process is taken as a quantitative measure of loss of fine details in the image. Loss of signal detectability is seen to correlate well with the blocky or patchy appearance due to overregularization with TV‐LSQ, and this trend runs counter to the image RMSE metric, which tends to favor the over‐regularized images. The proposed signal detection‐based metric provides an image quality assessment that is complimentary to that of image RMSE. Using the two metrics in concert may yield a useful prescription for determining CT algorithm and configuration parameters when nonlinear image reconstruction is used. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=153385043
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/mp.14703
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 6312
    Subjects:
      – SubjectFull: Image reconstruction algorithms
        Type: general
      – SubjectFull: Image reconstruction
        Type: general
      – SubjectFull: Signal detection
        Type: general
      – SubjectFull: Standard deviations
        Type: general
    Titles:
      – TitleFull: A signal detection model for quantifying overregularization in nonlinear image reconstruction.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sidky, Emil Y.
      – PersonEntity:
          Name:
            NameFull: Phillips, John Paul
      – PersonEntity:
          Name:
            NameFull: Zhou, Weimin
      – PersonEntity:
          Name:
            NameFull: Ongie, Greg
      – PersonEntity:
          Name:
            NameFull: Cruz‐Bastida, Juan P.
      – PersonEntity:
          Name:
            NameFull: Reiser, Ingrid S.
      – PersonEntity:
          Name:
            NameFull: Anastasio, Mark A.
      – PersonEntity:
          Name:
            NameFull: Pan, Xiaochuan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2021
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 00942405
          Numbering:
            – Type: volume
              Value: 48
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Medical Physics
              Type: main
ResultId 1