B. Nicolas Bloch

3T MR of the prostate: reducing susceptibility gradients by inflating the endorectal coil with a barium sulfate suspension.

Most prostate MRI/MRS examinations are performed with an endorectal coil inflated with air, leading to an air–tissue interface that induces magnetic susceptibility gradients within the gland. Inflation of the coil with a barium sulfate suspension is described and compared to inflation with air or liquid perfluorocarbon (PFC). The B0 field in the prostate gland was mapped for five healthy volunteers when the endorectal coil was inflated with each of the three agents. A marked decrease in the posterior‐anterior (P‐A) field gradient and a significant improvement in field homogeneity were evident in the presence of a barium suspension and PFC relative to air. MRS data acquired from the prostate gland in the presence of air, PFC, and a barium suspension in the endorectal coil showed similar trends, demonstrating improvement in line‐widths and spectral resolution when the barium suspension or the PFC were inflating the endorectal coil. On this basis we conclude that a barium suspension provides an available, cheap, and safe alternative to PFC, and we suggest that inflating the endorectal coil with a barium suspension should be considered for prostate MR studies, especially at high field strengths (such as 3T). Magn Reson Med 57:898–904, 2007.

Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer

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 cases, the prostate cancer extent from ex vivo whole-mount histology is to be mapped to in vivo MRI. The need for determining radiology-image-based disease signatures is important for (a) training radiologist residents and (b) for constructing an MRI-based computer aided diagnosis (CAD) system for disease detection in vivo. However, a prerequisite for this data mapping is the determination of slice correspondences (i.e. indices of each pair of corresponding image slices) between histological and magnetic resonance images. The explicit determination of such slice correspondences is especially indispensable when an accurate 3D reconstruction of the histological volume cannot be achieved because of (a) the limited tissue slices with unknown inter-slice spacing, and (b) obvious histological image artifacts (tissue loss or distortion). In the clinic practice, the histology-MRI slice correspondences are often determined visually by experienced radiologists and pathologists working in unison, but this procedure is laborious and time-consuming. We present an iterative method to automatically determine slice correspondence between images from histology and MRI via a group-wise comparison scheme, followed by 2D and 3D registration. The image slice correspondences obtained using our method were compared with the ground truth correspondences determined via consensus of multiple experts over a total of 23 patient studies. In most instances, the results of our method were very close to the results obtained via visual inspection by these experts.

Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI.

RATIONALE AND OBJECTIVES Accurate prostate volume estimation is useful for calculating prostate-specific antigen density and in evaluating posttreatment response. In the clinic, prostate volume estimation involves modeling the prostate as an ellipsoid or a spheroid from transrectal ultrasound, or T2-weighted magnetic resonance imaging (MRI). However, this requires some degree of manual intervention, and may not always yield accurate estimates. In this article, we present a multifeature active shape model (MFA) based segmentation scheme for estimating prostate volume from in vivo T2-weighted MRI. MATERIALS AND METHODS We aim to automatically determine the location of the prostate boundary on in vivo T2-weighted MRI, and subsequently determine the area of the prostate on each slice. The resulting planimetric areas are aggregated to yield the volume of the prostate for a given patient. Using a set of training images, the MFA learns the most discriminating statistical texture descriptors of the prostate boundary via a forward feature selection algorithm. After identification of the optimal image features, the MFA is deformed to accurately fit the prostate border. An expert radiologist segmented the prostate boundary on each slice and the planimetric aggregation of the enclosed areas yielded the ground truth prostate volume estimate. The volume estimation obtained via the MFA was then compared against volume estimations obtained via the ellipsoidal, Myschetzky, and prolated spheroids models. RESULTS We evaluated our MFA volume estimation method on a total 45 T2-weighted in vivo MRI studies, corresponding to both 1.5 Tesla and 3.0 Tesla field strengths. The results revealed that the ellipsoidal, Myschetzky, and prolate spheroid models overestimated prostate volumes, with volume fractions of 1.14, 1.53, and 1.96, respectively. By comparison, the MFA yielded a mean volume fraction of 1.05, evaluated using a fivefold cross-validation scheme. A correlation with the ground truth volume estimations showed that the MFA had an r(2) value of 0.82, whereas the clinical volume estimation schemes had a maximum value of 0.70. CONCLUSIONS Our MFA scheme involves minimal user intervention, is computationally efficient and results in volume estimations more accurate than state of the art clinical models.

Integrating Structural and Functional Imaging for Computer Assisted Detection of Prostate Cancer on Multi-Protocol In Vivo 3 Tesla MRI

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 paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.

A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In Vivo Prostate DCE-MRI

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 of contrast agent uptake by the lesion. Comparatively there is relatively little work in developing CAD schemes for prostate DCE-MRI. In this paper, we present a novel unsupervised detection scheme for CaP from 3 T DCE-MRI which comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from 3 T in vivo MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in projecting such high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. DCE-MRI data is embedded via LLE and then classified via unsupervised consensus clustering to identify distinct classes. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth estimates yielded a maximum CaP detection accuracy of 77.20% while the popular three time point (3TP) scheme yielded an accuracy of 67.37%.

An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer

IntroductionMagnetic resonance imaging (MRI) and MR spectroscopy can probe a variety of physiological (e.g. blood vessel permeability) and metabolic characteristics of prostate cancer. However, little is known about the changes in gene expression that underlie the spectral and imaging features observed in prostate cancer. Tumor induced changes in vascular permeability and angiogenesis are thought to contribute to patterns of dynamic contrast enhanced (DCE) MRI images of prostate cancer even though the genetic basis of tumor vasculogenesis is complex and the specific mechanisms underlying these DCEMRI features have not yet been determined.Materials and MethodsIn order to identify the changes in gene expression that correspond to MRS and DCEMRI patterns in human prostate cancers, we have utilized tissue print micropeel techniques to generate “whole mount” molecular maps of radical prostatectomy specimens that correspond to pre-surgical MRI/MRS studies. These molecular maps include RNA expression profiles from both Affymetrix GeneChip microarrays and quantitative reverse transcriptase PCR (qrt-PCR) analysis, as well as immunohistochemical studies.ResultsUsing these methods on patients with prostate cancer, we found robust over-expression of choline kinase a in the majority of primary tumors. We also observed overexpression of neuropeptide Y (NPY), a newly identified angiogenic factor, in a subset of prostate cancers, visualized on DCEMRI.ConclusionThese studies set the stage for establishing MRI/MRS parameters as validated biomarkers for human prostate cancer.

The role of magnetic resonance imaging (MRI) in prostate cancer imaging and staging at 1.5 and 3 Tesla: The Beth Israel Deaconess Medical Center (BIDMC) approach

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, rectal injury and impotence. Key elements for guiding appropriate treatment include: distinction of organ-confined disease from extracapsular extension (ECE); and determination of tumor volume and tumor grade, none of which have been satisfactorily accomplished in todays pre-treatment paradigm. Magnetic resonance imaging (MRI) has the capability to assess prostate tissue, both functionally and morphologically. MRI as a staging tool has not shown enough consistency or sufficient accuracy for widespread adoption in clinical practice; yet, recent technical developments in MRI have yielded improved results. At our institution we have combined the use of new endorectal 3 Tesla MRI technology, T2-weighted, and high spatial resolution dynamic-contrast enhanced (DCE) MRI to non-invasively assess the prostate with higher signal-to-noise ratio and spatial resolution than previously achieved. This approach allows assessment of prostate-tissue morphology and kinetics, thus providing a non-invasive tool for tumor detection and staging and, consequently, directing biopsy and treatment specifically to diseased areas for a pre-treatment evaluation that can assist in the rational selection of patients for appropriate prostate cancer therapy.

Principal component analysis of dynamic contrast enhanced mri in human prostate cancer

Objectives:To develop and evaluate a fast, objective and standardized method for image processing of dynamic contrast enhanced MRI of the prostate based on principal component analysis (PCA). Materials and Methods:The study was approved by the institutional internal review board; signed informed consent was obtained. MRI of the prostate at 3 Tesla was performed in 21 patients with biopsy proven cancers before radical prostatectomy. Seven 3-dimensional gradient echo datesets, 2 pre and 5 post-gadopentetate dimeglumine injection (0.1 mmol/kg), were acquired within 10.5 minutes at high spatial resolution. PCA of dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets and analysis by the 3-time points (3TP) method were applied using the latter method for adjusting the PCA eigenvectors. Results:PCA of 7 IS datasets and 6 ES datasets yielded their corresponding eigenvectors and eigenvalues. The first IS-eigenvector captured the major part of the signal variance because of a signal change between the precontrast and the first postcontrast arising from the inhomogeneous surface coil reception profile. The next 2 IS-eigenvectors and the 2 dominant ES-eigenvectors captured signal changes because of tissue contrast-enhancement, whereas the remaining eigenvectors captured noise changes. These eigenvectors were adjusted by rotation to reach congruence with the wash-in and wash-out kinetic parameters defined according to the 3TP method. The IS and ES-eigenvectors and rotation angles were highly reproducible across patients enabling the calculation of a general rotated eigenvector base that served to rapidly and objectively calculate diagnostically relevant projection coefficient maps for new cases. We found for the a priori selected prostate cancer patients that the projection coefficients of the IS-2nd eigenvector provided a higher accuracy for detecting biopsy proven cancers (94% sensitivity, 67% specificity, 80% ppv, and 89% npv) than the projection coefficients of the ES-2nd rotated and non rotated eigenvectors. Conclusions:PCA adjusted to correlate with physiological parameters selects a dominant eigenvector, free of the inhomogeneous radio-frequency field reception-profile and noise-components. Projection coefficient maps of this eigenvector provide a fast, objective, and standardized means for visualizing prostate cancer.

A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.

To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast en-hanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positivebreast lesions with low (<18, N = 55) and high (>30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively charac-terize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers.

Novel PCA‐VIP scheme for ranking MRI protocols and identifying computer‐extracted MRI measurements associated with central gland and peripheral zone prostate tumors

To identify computer‐extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI).