Automated determination of arterial input function for DCE-MRI of the prostate

Yingxuan Zhu, Ming Ching Chang, Sandeep Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with expert traced manual AIF.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7963
DOIs
StatePublished - 2011
Externally publishedYes
EventMedical Imaging 2011: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: Feb 15 2011Feb 17 2011

Other

OtherMedical Imaging 2011: Computer-Aided Diagnosis
CountryUnited States
CityLake Buena Vista, FL
Period2/15/112/17/11

Fingerprint

Magnetic resonance imaging
Prostate
Pixels
pixels
inhomogeneity
cancer
Prostatic Neoplasms
Arteries
Message passing
messages
arteries
quantitative analysis
education
Neoplasms
optimization
Monitoring
curves
Chemical analysis

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Zhu, Y., Chang, M. C., & Gupta, S. (2011). Automated determination of arterial input function for DCE-MRI of the prostate. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 7963). [79630W] https://doi.org/10.1117/12.878213

Automated determination of arterial input function for DCE-MRI of the prostate. / Zhu, Yingxuan; Chang, Ming Ching; Gupta, Sandeep.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7963 2011. 79630W.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhu, Y, Chang, MC & Gupta, S 2011, Automated determination of arterial input function for DCE-MRI of the prostate. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 7963, 79630W, Medical Imaging 2011: Computer-Aided Diagnosis, Lake Buena Vista, FL, United States, 2/15/11. https://doi.org/10.1117/12.878213
Zhu Y, Chang MC, Gupta S. Automated determination of arterial input function for DCE-MRI of the prostate. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7963. 2011. 79630W https://doi.org/10.1117/12.878213
Zhu, Yingxuan ; Chang, Ming Ching ; Gupta, Sandeep. / Automated determination of arterial input function for DCE-MRI of the prostate. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7963 2011.
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