A New Application for Displaying and Fusing Multimodal Data Sets

A recently developed, freely available, application specifically designed for the visualization of multimodal data sets is presented. The application allows multiple 3D data sets such as CT (x-ray computer tomography), MRI (magnetic resonance imaging), PET (positron emission tomography), and SPECT (single photon emission tomography) of the same subject to be viewed simultaneously. This is done by maintaining synchronization of the spatial location viewed within all modalities, and by providing fused views of the data where multiple data sets are displayed as a single volume. Different options for the fused views are provided by plug-ins. Plug-ins typically used include color-overlays and interlacing, but more complex plug-ins such as those based on different color spaces, and component analysis techniques are also supported. Corrections for resolution differences and user preference of contrast and brightness are made. Pre-defined and custom color tables can be used to enhance the viewing experience. In addition to these essential capabilities, multiple options are provided for mapping 16-bit data sets onto an 8-bit display, including windowing, automatically and dynamically defined tone transfer functions, and histogram based techniques. The 3D data sets can be viewed not only as a stack of images, but also as the preferred three orthogonal cross sections through the volume. More advanced volumetric displays of both individual data sets and fused views are also provided. This includes the common MIP (maximum intensity projection) both with and without depth correction for both individual data sets and multimodal data sets created using a fusion plug-in

An Assessment of PET Dose Reduction with Penalized Likelihood Image Reconstruction Using a Computationally Efficient Model Observer

Developing PET reconstruction algorithms with improved low-count capabilities may provide a timely and cost- effective means of reducing radiation dose in promising clinical applications such as immuno-PET that require long-lived radiotracers. For many PET clinics, the reconstruction protocol consists of postsmoothed ordered-sets expectation-maximization (OSEM) reconstruction, but penalized likelihood methods based on total-variation (TV) regularization could substantially reduce dose. We performed a task-based comparison of postsmoothed OSEM and higher-order TV (HOTV) reconstructions using simulated images of a contrast-detail phantom. An anthropomorphic visual-search model observer read the images in a location-known receiver operating characteristic (ROC) format. Acquisition counts, target uptake, and target size were study variables, and the OSEM postfiltering was task-optimized based on count level. A psychometric analysis of observer performance for the selected task found that the HOTV algorithm allowed a two-fold reduction in dose compared to the optimized OSEM algorithm

Multimodal display techniques with application to breast imaging

Application of a multimodality approach is advantageous for detection, diagnosis and management of breast cancer. In this context, F-18-FDG positron emission tomography (PET), and high-resolution and dynamic contrast-enhanced magnetic resonance imaging (MRI) have steadily gained clinical acceptance. Obtaining the spatial relationships between these modalities and conveying them to the observer maximizes the benefit that can be achieved. Traditionally the registered images are displayed side by side. However, it is believed that a combined MRI/PET display may be more beneficial. The advantage of a combined image lies in our inability to visually judge spatial relationships between images when they are viewed side by side. The process of combining the MRI and PET 3D images into a single 3D image is called image fusion. Color tables were defined for the fusion of MRI/PET images. This included color tables, which satisfy specific requirements, that were generated by a previously developed genetic algorithm. Radiologists were asked to evaluate images created using the selected fusion-for-visualization techniques. The study determined radiologists’ preference, ease of use, understanding, efficiency, and accuracy when reading images using each technique. The data studied, the data collected, the applications used to administer the study and analyze the results, and the processed results are provided through this interactive document

Ethanolic Extract of Propolis Augments TRAIL-Induced Apoptotic Death in Prostate Cancer Cells

Prostate cancer is a commonly diagnosed cancer in men. The ethanolic extract of propolis (EEP) and its phenolic compounds possess immunomodulatory, chemopreventive and antitumor effects. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL/APO2L) is a naturally occurring anticancer agent that preferentially induces apoptosis in cancer cells and is not toxic to normal cells. We examined the cytotoxic and apoptotic effects of EEP and phenolic compounds isolated from propolis in combination with TRAIL on two prostate cancer cell lines, hormone-sensitivity LNCaP and hormone-refractory DU145. The cytotoxicity was evaluated by MTT and LDH assays. The apoptosis was determined using flow cytometry with annexin V-FITC/propidium iodide. The prostate cancer cell lines were proved to be resistant to TRAIL-induced apoptosis. Our study demonstrated that EEP and its components significantly sensitize to TRAIL-induced death in prostate cancer cells. The percentage of the apoptotic cells after cotreatment with 50 μg mL−1 EEP and 100 ng mL−1 TRAIL increased to 74.9 ± 0.7% for LNCaP and 57.4 ± 0.7% for DU145 cells. The strongest cytotoxic effect on LNCaP cells was exhibited by apigenin, kaempferid, galangin and caffeic acid phenylethyl ester (CAPE) in combination with TRAIL (53.51 ± 0.68–66.06 ± 0.62% death cells). In this work, we showed that EEP markedly augmented TRAIL-mediated apoptosis in prostate cancer cells and suggested the significant role of propolis in chemoprevention of prostate cancer

Sparsity Promoting Regularization for Effective Noise Suppression in SPECT Image Reconstruction

The purpose of this research is to develop an advanced reconstruction method for low-count, hence high-noise, Single-Photon Emission Computed Tomography (SPECT) image reconstruction. It consists of a novel reconstruction model to suppress noise while conducting reconstruction and an efficient algorithm to solve the model. A novel regularizer is introduced as the nonconvex denoising term based on the approximate sparsity of the image under a geometric tight frame transform domain. The deblurring term is based on the negative log-likelihood of the SPECT data model. To solve the resulting nonconvex optimization problem a Preconditioned Fixed-point Proximity Algorithm (PFPA) is introduced. We prove that under appropriate assumptions, PFPA converges to a local solution of the optimization problem at a global O (1/k) convergence rate. Substantial numerical results for simulation data are presented to demonstrate the superiority of the proposed method in denoising, suppressing artifacts and reconstruction accuracy. We simulate noisy 2D SPECT data from two phantoms: hot Gaussian spheres on random lumpy warm background, and the anthropomorphic brain phantom, at high- and low-noise levels (64k and 90k counts, respectively), and reconstruct them using PFPA. We also perform limited comparative studies with selected competing state-of-the-art total variation (TV) and higher-order TV (HOTV) transform-based methods, and widely used post-filtered maximum-likelihood expectation-maximization. We investigate imaging performance of these methods using: Contrast-to-Noise Ratio (CNR), Ensemble Variance Images (EVI), Background Ensemble Noise (BEN), Normalized Mean-Square Error (NMSE), and Channelized Hotelling Observer (CHO) detectability. Each of the competing methods is independently optimized for each metric. We establish that the proposed method outperforms the other approaches in all image quality metrics except NMSE where it is matched by HOTV. The superiority of the proposed method is especially evident in the CHO detectability tests results. We also perform qualitative image evaluation for presence and severity of image artifacts where it also performs better in terms of suppressing staircase artifacts, as compared to TV methods. However, edge artifacts on high-contrast regions persist. We conclude that the proposed method may offer a powerful tool for detection tasks in high-noise SPECT imaging

A Fast Convergent Ordered-Subsets Algorithm with Subiteration-Dependent Preconditioners for PET Image Reconstruction

We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35\%-50\% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.Comment: 12 pages, 9 figure

A Deblurring/Denoising Corrected Scintigraphic Planar Image Reconstruction Model for Targeted Alpha Theory

Scintigraphy is a common nuclear medicine method to image molecular target’s bio-distribution and pharmacokinetics through the use of radiotracers and gamma cameras. The patient’s images are obtained by using a pair of opposing large flat gamma ray detectors equipped with parallel-hole lead or tungsten collimators that preferentially detect gamma-rays that are emitted perpendicular to the plane of the detector. The resulting images form an anterior/posterior (A/P) planar image pairs. The obtained images are contaminated by noise and contain artifacts caused by gamma-ray attenuation, collimator penetration, scatter and other detrimental factors. Post-filtering of the images can reduce the noise, but at the cost of spatial resolution loss, and cannot remove any of the aforementioned artifacts. In this study, we introduced a new image reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation, system spatial resolution and collimator penetration, using the A/P image pair (two conjugated views) as data. To accomplish this task, we used a system model based on the gamma camera detectors physical properties and applied regularization method based on sparse image representation to control noise while preserving spatial resolution. In this proof-of-concept study, we evaluated the proposed approach using simple numerical phantoms. The images were evaluated for simulated lesions images contrast and background variability. Our initial results indicate that the proposed method outperforms the conventional methods. We conclude, that the proposed approach is a promising methodology for improved planar scintigraphic image quality and warrants further exploration

Deformable Model for 3D Intramodal Nonrigid Breast Image Registration with Fiducial Skin Markers

We implemented a new approach to intramodal non-rigid 3D breast image registration. Our method uses fiducial skin markers (FSM) placed on the breast surface. After determining the displacements of FSM, finite element method (FEM) is used to distribute the markers’ displacements linearly over the entire breast volume using the analogy between the orthogonal components of the displacement field and a steady state heat transfer (SSHT). It is valid because the displacement field in x, y and z direction and a SSHT problem can both be modeled using LaPlace’s equation and the displacements are analogous to temperature differences in SSHT. It can be solved via standard heat conduction FEM software with arbitrary conductivity of surface elements significantly higher than that of volume elements. After determining the displacements of the mesh nodes over the entire breast volume, moving breast volume is registered to target breast volume using an image warping algorithm. Very good quality of the registration was obtained. Following similarity measurements were estimated: Normalized Mutual Information (NMI), Normalized Correlation Coefficient (NCC) and Sum of Absolute Valued Differences (SAVD). We also compared our method with rigid registration technique