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Recently, multi-parametric Magnetic Resonance Imaging (mp-MRI) has been used to improve the sensitivity of detecting high-risk prostate cancer (PCa). Prior to biopsy, primary and secondary cancer lesions are identified on mp-MRI. The lesions are then targeted using TRUS guidance. In this paper, for the first time, we present a fused mp-MRI-temporal-ultrasound framework for characterization of PCa, in vivo. Cancer classification results obtained using temporal ultrasound are fused with those achieved using consolidated mp-MRI maps determined by multiple observers. We verify the outcome of our study using histopathology following deformable registration of ultrasound and histology images. Fusion of temporal ultrasound and mp-MRI for characterization of the PCa results in an area under the receiver operating characteristic curve (AUC) of 0.86 for cancerous regions with Gleason scores (GSs)≥3+3, and AUC of 0.89 for those with GSs≥3+4.

Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.

Magnetic resonance imaging (MRI), particularly dynamic contrast enhanced (DCE) imaging, has shown great potential in prostate cancer diagnosis and prognosis. The time course of the DCE images provides measures of the contrast agent uptake kinetics. Also, using pharmacokinetic modelling, one can extract parameters from the DCE-MR images that characterize the tumor vascularization and can be used to detect cancer. A requirement for calculating the pharmacokinetic DCE parameters is estimating the Arterial Input Function (AIF). One needs an accurate segmentation of the cross section of the external femoral artery to obtain the AIF. In this work we report a semi-automatic method for segmentation of the cross section of the femoral artery, using circular Hough transform, in the sequence of DCE images. We also report a machine-learning framework to combine pharmacokinetic parameters with the model-free contrast agent uptake kinetic parameters extracted from the DCE time course into a nine-dimensional feature vector. This combination of features is used with random forest and with support vector machine classi cation for cancer detection. The MR data is obtained from patients prior to radical prostatectomy. After the surgery, wholemount histopathology analysis is performed and registered to the DCE-MR images as the diagnostic reference. We show that the use of a combination of pharmacokinetic parameters and the model-free empirical parameters extracted from the time course of DCE results in improved cancer detection compared to the use of each group of features separately. We also validate the proposed method for calculation of AIF based on comparison with the manual method.

MRI and Computed Tomography (CT) are used in image-based solutions for guiding High Dose Rate (HDR) brachytherapy treatment of cervical cancer. MRI is costly and CT exposes the patients to ionizing radiation. Ultrasound, on the other hand, is affordable and safe. The long-term goal of our work is to enable the use of multiparametric ultrasound imaging in image-guided HDR for cervical cancer. In this paper, we report the development of enabling technology for ultrasound guidance and tissue typing. We report a system to obtain the 3D freehand transabdominal ultrasound RF signals and B-mode images of the uterus, and a method for registration of ultrasound to MRI. MRI and 3D ultrasound images of the female pelvis were registered by contouring the uterus in the two modalities, creating a surface model, followed by rigid and B-spline deformable registration. The resulting transformation was used to map the location of the tumor from the T2-weighted MRI to ultrasound images and to determine cancerous and normal areas in ultrasound. B-mode images show a contrast for cancer vs. normal tissue. Our study shows the potential and the challenges of ultrasound imaging in guiding cervical cancer treatments.

In this paper, we present a registration pipeline to compensate for prostate motion and deformation during targeted freehand prostate biopsies. We perform 2D-3D registration by reconstructing a thin-volume around the real-time 2D ultrasound imaging plane. Constrained Sum of Squared Differences (SSD) and gradient descent optimization are used to rigidly align the moving volume to the fixed thin-volume. Subsequently, B-spline de- formable registration is performed to compensate for remaining non-linear deformations. SSD and zero-bounded Limited memory Broyden Fletcher Goldfarb Shannon (LBFGS) optimizer are used to find the optimum B-spline parameters. Registration results are validated on five prostate biopsy patients. Initial experiments suggest thin- volume-to-volume registration to be more effective than slice-to-volume registration. Also, a minimum consistent 2 mm improvement of Target Registration Error (TRE) is achieved following the deformable registration.