A structural-functional MRI-based disease atlas: Application to computer-aided-diagnosis of prostate cancer

G. Xiao, B. Bloch, J. Chappelow, E. Genega, N. Rofsky, R. Lenkinski, A. Madabhushi

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

5 Citations (Scopus)

Abstract

Different imaging modalities or protocols of a single patient may convey different types of information regarding a disease for the same anatomical organ/tissue. On the other hand, multi-modal/multi-protocol medical images from several different patients can also provide spatial statistics of the disease occurrence, which in turn can greatly aid in disease diagnosis and aid in improved, accurate biopsy and targeted treatment. It is therefore important to not only integrate medical images from multiple patients into a common coordinate frame (in the form of a population-based atlas), but also find the correlation between these multi-modal/multi-protocol data features and the disease spatial distribution in order to identify different quantitative structural and functional disease signatures. Most previous work on construction of anatomical atlases has focused on deriving a population-based atlas for the purpose of deriving the spatial statistics. Moreover, these models are typically derived from normal or healthy subjects, either explicitly or implicitly, where it is assumed that the inter-patient pathological variation is not large. These methods are not suitable for constructing a disease atlas, where significant differences between patients on account of disease related variations can be expected. In this paper, we present a novel framework for the construction of a multi-parametric MRI-based data-driven disease atlas consisting of multi-modal and multi-protocol data from across multiple patient studies. Our disease atlas contains 3 Tesla structural (T2) and functional (dynamic contrast enhanced (DCE)) prostate in vivo MRI with corresponding whole mount histology specimens obtained via radical prostatectomy. Our atlas construction framework comprises 3 distinct modules: (a) determination of disease spatial extent on the multi-protocol MR imagery for each patient, (b) construction of a multi-protocol MR imaging spatial atlas which captures the geographical proclivity of the disease, and (c) feature extraction and the construction of the data-driven multi-protocol MRI based prostate cancer atlas. The marriage of data driven and spatial atlases could serve as a useful tool for clinicians to identifying structural and functional imaging disease signatures so as to make better, more informed diagnoses. Each spatial location in this atlas can be associated with a high dimensional multi-attribute quantitative feature vector. Additionally, since the feature vectors are extracted from across multiple patient studies, each spatial location in the data-driven atlas can be characterized by a feature distribution (in turn characterized by a mean and standard deviation). Preliminary investigation in quantitatively correlating the disease signatures from across the spatial and data driven atlases suggests that our quantitative atlas framework could emerge as a powerful tool for discovering prostate cancer imaging signatures.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7623
EditionPART 1
DOIs
StatePublished - 2010
EventMedical Imaging 2010: Image Processing - San Diego, CA, United States
Duration: Feb 14 2010Feb 16 2010

Other

OtherMedical Imaging 2010: Image Processing
CountryUnited States
CitySan Diego, CA
Period2/14/102/16/10

Fingerprint

Computer aided diagnosis
Atlases
Prostatic Neoplasms
cancer
Magnetic Resonance Imaging
signatures
Magnetic resonance imaging
Imaging techniques
Statistics
statistics
Histology
histology
Biopsy
Imagery (Psychotherapy)
Prostatectomy
pattern recognition
organs
imagery
Marriage
Spatial distribution

Keywords

  • 3 Tesla
  • computer-aided-diagnosis
  • data driven atlas
  • DCE
  • Disease atlas
  • MRI
  • multi-functional
  • multi-modal
  • population atlas
  • prostate cancer
  • T2

ASJC Scopus subject areas

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

Cite this

Xiao, G., Bloch, B., Chappelow, J., Genega, E., Rofsky, N., Lenkinski, R., & Madabhushi, A. (2010). A structural-functional MRI-based disease atlas: Application to computer-aided-diagnosis of prostate cancer. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (PART 1 ed., Vol. 7623). [762303] https://doi.org/10.1117/12.845554

A structural-functional MRI-based disease atlas : Application to computer-aided-diagnosis of prostate cancer. / Xiao, G.; Bloch, B.; Chappelow, J.; Genega, E.; Rofsky, N.; Lenkinski, R.; Madabhushi, A.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7623 PART 1. ed. 2010. 762303.

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

Xiao, G, Bloch, B, Chappelow, J, Genega, E, Rofsky, N, Lenkinski, R & Madabhushi, A 2010, A structural-functional MRI-based disease atlas: Application to computer-aided-diagnosis of prostate cancer. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. PART 1 edn, vol. 7623, 762303, Medical Imaging 2010: Image Processing, San Diego, CA, United States, 2/14/10. https://doi.org/10.1117/12.845554
Xiao G, Bloch B, Chappelow J, Genega E, Rofsky N, Lenkinski R et al. A structural-functional MRI-based disease atlas: Application to computer-aided-diagnosis of prostate cancer. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. PART 1 ed. Vol. 7623. 2010. 762303 https://doi.org/10.1117/12.845554
Xiao, G. ; Bloch, B. ; Chappelow, J. ; Genega, E. ; Rofsky, N. ; Lenkinski, R. ; Madabhushi, A. / A structural-functional MRI-based disease atlas : Application to computer-aided-diagnosis of prostate cancer. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7623 PART 1. ed. 2010.
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