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Many pharmacological and clinical studies have demonstrated the importance of the dopaminergic (DA) system for cognitive functioning but little is known about the genetic basis of general cognitive ability that has been demonstrated to be highly heritable. Attempts to detect associations between certain gene loci and endophenotypes of general cognitive(More)
Mapping the spatial disease extent in a certain anatomical organ/tissue from histology images to radiological images is important in defining the disease signature in the radiological images. One such scenario is in the context of men with prostate cancer who have had pre-operative magnetic resonance imaging (MRI) before radical prostatectomy. For these(More)
PURPOSE By performing registration of preoperative multiprotocol in vivo magnetic resonance (MR) images of the prostate with corresponding whole-mount histology (WMH) sections from postoperative radical prostatectomy specimens, an accurate estimate of the spatial extent of prostate cancer (CaP) on in vivo MR imaging (MRI) can be retrospectively established.(More)
Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this(More)
Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function(More)
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(More)
Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of(More)
Management decisions for patients with prostate cancer present a dilemma for both patients and their clinicians because prostate cancers demonstrate a wide range in biologic activity, with the majority of cases not leading to a prostate cancer related death. Furthermore, the current treatment options have significant side effects, such as incontinence,(More)
Statistical imaging atlases allow for integration of information from multiple patient studies collected across different image scales and modalities, such as multi-parametric (MP) MRI and histology, providing population statistics regarding a specific pathology within a single canonical representation. Such atlases are particularly valuable in the(More)
In this work, we present a novel learning based fiducial driven registration (LeFiR) scheme which utilizes a point matching technique to identify the optimal configuration of landmarks to better recover deformation between a target and a moving image. Moreover, we employ the LeFiR scheme to model the localized nature of deformation introduced by a new(More)