MO‐FF‐A4‐05: Implementation and Evaluation of Various DRR Algorithms on GPU

M. Folkerts, X. Jia, X. gu, D. Choi, A. Majumdar, S. Jiang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Purpose: The Digitally Reconstructed Radio‐graph (DRR) is a fundamental computation task in cancer radiotherapy. It is used in 2D–3D patient registration, dose calculation, and iterative 3D and 4D cone‐beam CT reconstruction (CBCT). Most DRR algorithms are designed to run on a single processor (CPU). However, significant speed up factors are realized by running DRR algorithms on recently developed graphical processing units (GPUs). It is the goal of this work to systematically evaluate various DRR algorithms running on GPU with emphases on computational efficiency and reliability. Method and Materials: We have implemented and evaluated three different DRR algorithms on both CPU and GPU. To test the speed of each algorithm, we simulate DRR projections of a XCAT phantom data set at 360 directions in a full rotation about the superior‐inferior axis on an NVIDIA Tesla C1060 GPU card and a 2.27GHz Intel Xeon CPU. Speedup factors were then calculated. To test accuracy, the aforementioned projections were used as input to a Feldkamp‐Davis‐Kress 3D reconstruction, the current standard CBCT reconstruction algorithm. We then calculated the mean squared error (MSE) between the original and reconstructed data sets. In these tests we simulated a detector resolution of 512×384 and CBCT data resolution or 512×512×104. Results: We found a modified version Siddon's algorithm to run the fastest on GPU, with an average projection time of 30 ms. A distance‐driven algorithm had the smallest MSE of 3.11×10−6 cm2. A fixed stride ray‐casting algorithm had a speedup factor of 204. Conclusion: We found substantial speedup factors for DRR algorithms running on GPU. The accuracy of each algorithm has also been objectively tested. Having a clear picture of the behavior of each algorithm will enable researchers to make an informed decision about which algorithm to implement in their own future work.

Original languageEnglish (US)
Pages (from-to)3367
Number of pages1
JournalMedical Physics
Volume37
Issue number6
DOIs
StatePublished - 2010

Fingerprint

Four-Dimensional Computed Tomography
Radiotherapy
Research Personnel
Neoplasms
Datasets
Direction compound

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

MO‐FF‐A4‐05 : Implementation and Evaluation of Various DRR Algorithms on GPU. / Folkerts, M.; Jia, X.; gu, X.; Choi, D.; Majumdar, A.; Jiang, S.

In: Medical Physics, Vol. 37, No. 6, 2010, p. 3367.

Research output: Contribution to journalArticle

Folkerts, M. ; Jia, X. ; gu, X. ; Choi, D. ; Majumdar, A. ; Jiang, S. / MO‐FF‐A4‐05 : Implementation and Evaluation of Various DRR Algorithms on GPU. In: Medical Physics. 2010 ; Vol. 37, No. 6. pp. 3367.
@article{be0d5957a4ef40f09be32885b39d2b5c,
title = "MO‐FF‐A4‐05: Implementation and Evaluation of Various DRR Algorithms on GPU",
abstract = "Purpose: The Digitally Reconstructed Radio‐graph (DRR) is a fundamental computation task in cancer radiotherapy. It is used in 2D–3D patient registration, dose calculation, and iterative 3D and 4D cone‐beam CT reconstruction (CBCT). Most DRR algorithms are designed to run on a single processor (CPU). However, significant speed up factors are realized by running DRR algorithms on recently developed graphical processing units (GPUs). It is the goal of this work to systematically evaluate various DRR algorithms running on GPU with emphases on computational efficiency and reliability. Method and Materials: We have implemented and evaluated three different DRR algorithms on both CPU and GPU. To test the speed of each algorithm, we simulate DRR projections of a XCAT phantom data set at 360 directions in a full rotation about the superior‐inferior axis on an NVIDIA Tesla C1060 GPU card and a 2.27GHz Intel Xeon CPU. Speedup factors were then calculated. To test accuracy, the aforementioned projections were used as input to a Feldkamp‐Davis‐Kress 3D reconstruction, the current standard CBCT reconstruction algorithm. We then calculated the mean squared error (MSE) between the original and reconstructed data sets. In these tests we simulated a detector resolution of 512×384 and CBCT data resolution or 512×512×104. Results: We found a modified version Siddon's algorithm to run the fastest on GPU, with an average projection time of 30 ms. A distance‐driven algorithm had the smallest MSE of 3.11×10−6 cm2. A fixed stride ray‐casting algorithm had a speedup factor of 204. Conclusion: We found substantial speedup factors for DRR algorithms running on GPU. The accuracy of each algorithm has also been objectively tested. Having a clear picture of the behavior of each algorithm will enable researchers to make an informed decision about which algorithm to implement in their own future work.",
author = "M. Folkerts and X. Jia and X. gu and D. Choi and A. Majumdar and S. Jiang",
year = "2010",
doi = "10.1118/1.3469159",
language = "English (US)",
volume = "37",
pages = "3367",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "6",

}

TY - JOUR

T1 - MO‐FF‐A4‐05

T2 - Implementation and Evaluation of Various DRR Algorithms on GPU

AU - Folkerts, M.

AU - Jia, X.

AU - gu, X.

AU - Choi, D.

AU - Majumdar, A.

AU - Jiang, S.

PY - 2010

Y1 - 2010

N2 - Purpose: The Digitally Reconstructed Radio‐graph (DRR) is a fundamental computation task in cancer radiotherapy. It is used in 2D–3D patient registration, dose calculation, and iterative 3D and 4D cone‐beam CT reconstruction (CBCT). Most DRR algorithms are designed to run on a single processor (CPU). However, significant speed up factors are realized by running DRR algorithms on recently developed graphical processing units (GPUs). It is the goal of this work to systematically evaluate various DRR algorithms running on GPU with emphases on computational efficiency and reliability. Method and Materials: We have implemented and evaluated three different DRR algorithms on both CPU and GPU. To test the speed of each algorithm, we simulate DRR projections of a XCAT phantom data set at 360 directions in a full rotation about the superior‐inferior axis on an NVIDIA Tesla C1060 GPU card and a 2.27GHz Intel Xeon CPU. Speedup factors were then calculated. To test accuracy, the aforementioned projections were used as input to a Feldkamp‐Davis‐Kress 3D reconstruction, the current standard CBCT reconstruction algorithm. We then calculated the mean squared error (MSE) between the original and reconstructed data sets. In these tests we simulated a detector resolution of 512×384 and CBCT data resolution or 512×512×104. Results: We found a modified version Siddon's algorithm to run the fastest on GPU, with an average projection time of 30 ms. A distance‐driven algorithm had the smallest MSE of 3.11×10−6 cm2. A fixed stride ray‐casting algorithm had a speedup factor of 204. Conclusion: We found substantial speedup factors for DRR algorithms running on GPU. The accuracy of each algorithm has also been objectively tested. Having a clear picture of the behavior of each algorithm will enable researchers to make an informed decision about which algorithm to implement in their own future work.

AB - Purpose: The Digitally Reconstructed Radio‐graph (DRR) is a fundamental computation task in cancer radiotherapy. It is used in 2D–3D patient registration, dose calculation, and iterative 3D and 4D cone‐beam CT reconstruction (CBCT). Most DRR algorithms are designed to run on a single processor (CPU). However, significant speed up factors are realized by running DRR algorithms on recently developed graphical processing units (GPUs). It is the goal of this work to systematically evaluate various DRR algorithms running on GPU with emphases on computational efficiency and reliability. Method and Materials: We have implemented and evaluated three different DRR algorithms on both CPU and GPU. To test the speed of each algorithm, we simulate DRR projections of a XCAT phantom data set at 360 directions in a full rotation about the superior‐inferior axis on an NVIDIA Tesla C1060 GPU card and a 2.27GHz Intel Xeon CPU. Speedup factors were then calculated. To test accuracy, the aforementioned projections were used as input to a Feldkamp‐Davis‐Kress 3D reconstruction, the current standard CBCT reconstruction algorithm. We then calculated the mean squared error (MSE) between the original and reconstructed data sets. In these tests we simulated a detector resolution of 512×384 and CBCT data resolution or 512×512×104. Results: We found a modified version Siddon's algorithm to run the fastest on GPU, with an average projection time of 30 ms. A distance‐driven algorithm had the smallest MSE of 3.11×10−6 cm2. A fixed stride ray‐casting algorithm had a speedup factor of 204. Conclusion: We found substantial speedup factors for DRR algorithms running on GPU. The accuracy of each algorithm has also been objectively tested. Having a clear picture of the behavior of each algorithm will enable researchers to make an informed decision about which algorithm to implement in their own future work.

UR - http://www.scopus.com/inward/record.url?scp=84870913992&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870913992&partnerID=8YFLogxK

U2 - 10.1118/1.3469159

DO - 10.1118/1.3469159

M3 - Article

AN - SCOPUS:84870913992

VL - 37

SP - 3367

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 6

ER -