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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.
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