Quantitative analysis tools and digital phantoms for deformable image registration quality assurance

Haksoo Kim, Samuel B. Park, James I. Monroe, Bryan J. Traughber, Yiran Zheng, Simon S. Lo, Min Yao, David Mansur, Rodney Ellis, Mitchell Machtay, Jason W. Sohn

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R’. The data set, R’, T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck.

Original languageEnglish (US)
Pages (from-to)428-439
Number of pages12
JournalTechnology in Cancer Research and Treatment
Volume14
Issue number4
DOIs
StatePublished - Jan 1 2015

Fingerprint

Neck
Head
Liver Neoplasms
Color
Lung
Datasets

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

Cite this

Kim, Haksoo ; Park, Samuel B. ; Monroe, James I. ; Traughber, Bryan J. ; Zheng, Yiran ; Lo, Simon S. ; Yao, Min ; Mansur, David ; Ellis, Rodney ; Machtay, Mitchell ; Sohn, Jason W. / Quantitative analysis tools and digital phantoms for deformable image registration quality assurance. In: Technology in Cancer Research and Treatment. 2015 ; Vol. 14, No. 4. pp. 428-439.
@article{9a24408a9bb349e4a13efb7a6d1add71,
title = "Quantitative analysis tools and digital phantoms for deformable image registration quality assurance",
abstract = "This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R’. The data set, R’, T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck.",
author = "Haksoo Kim and Park, {Samuel B.} and Monroe, {James I.} and Traughber, {Bryan J.} and Yiran Zheng and Lo, {Simon S.} and Min Yao and David Mansur and Rodney Ellis and Mitchell Machtay and Sohn, {Jason W.}",
year = "2015",
month = "1",
day = "1",
doi = "10.1177/1533034614553891",
language = "English (US)",
volume = "14",
pages = "428--439",
journal = "Technology in Cancer Research and Treatment",
issn = "1533-0346",
publisher = "Adenine Press",
number = "4",

}

Kim, H, Park, SB, Monroe, JI, Traughber, BJ, Zheng, Y, Lo, SS, Yao, M, Mansur, D, Ellis, R, Machtay, M & Sohn, JW 2015, 'Quantitative analysis tools and digital phantoms for deformable image registration quality assurance', Technology in Cancer Research and Treatment, vol. 14, no. 4, pp. 428-439. https://doi.org/10.1177/1533034614553891

Quantitative analysis tools and digital phantoms for deformable image registration quality assurance. / Kim, Haksoo; Park, Samuel B.; Monroe, James I.; Traughber, Bryan J.; Zheng, Yiran; Lo, Simon S.; Yao, Min; Mansur, David; Ellis, Rodney; Machtay, Mitchell; Sohn, Jason W.

In: Technology in Cancer Research and Treatment, Vol. 14, No. 4, 01.01.2015, p. 428-439.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Quantitative analysis tools and digital phantoms for deformable image registration quality assurance

AU - Kim, Haksoo

AU - Park, Samuel B.

AU - Monroe, James I.

AU - Traughber, Bryan J.

AU - Zheng, Yiran

AU - Lo, Simon S.

AU - Yao, Min

AU - Mansur, David

AU - Ellis, Rodney

AU - Machtay, Mitchell

AU - Sohn, Jason W.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R’. The data set, R’, T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck.

AB - This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R’. The data set, R’, T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck.

UR - http://www.scopus.com/inward/record.url?scp=84960373751&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84960373751&partnerID=8YFLogxK

U2 - 10.1177/1533034614553891

DO - 10.1177/1533034614553891

M3 - Article

C2 - 25336380

AN - SCOPUS:84960373751

VL - 14

SP - 428

EP - 439

JO - Technology in Cancer Research and Treatment

JF - Technology in Cancer Research and Treatment

SN - 1533-0346

IS - 4

ER -