Simultaneous CT-MRI Reconstruction for Constrained Imaging Geometries Using Structural Coupling and Compressive Sensing

A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT.

Generalized backpropagation algorithm for training second-order neural networks

The artificial neural network is a popular framework in machine learning. To empower individual neurons, we recently suggested that the current type of neurons could be upgraded to second-order counterparts, in which the linear operation between inputs to a neuron and the associated weights is replaced with a nonlinear quadratic operation. A single second-order neurons already have a strong nonlinear modeling ability, such as implementing basic fuzzy logic operations. In this paper, we develop a general backpropagation algorithm to train the network consisting of second-order neurons. The numerical studies are performed to verify the generalized backpropagation algorithm.

Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression

Radiomics is an emerging area within clinical radiology research. It seeks to take full advantage of all the information contained in multiple medical imaging modalities. With a radiomics approach, medical images are not limited to providing only a qualitative assessment but can also provide quantitative data by parameterizing image features. These parameters can be used to identify regions and volumes of interest and discriminate normal healthy tissue from abnormal or diseased tissue. Radiomics is an interlinked sequence of processes of vital importance that begins with the acquisition and selection of medical images that involve standardization of acquisition protocols and inter-equipment normalization. This is followed by the identification and segmentation of regions or volumes of interest by expert radiologists through the use of computational tools that offer speed while reducing variability and bias. The segmentation process is the most critical stage in radiomics. This sometimes requires the incorporation of a pre-processing stage consisting of advanced techniques (reconstruction processes, filtering, etc.). Thereafter, representative characteristics of the region or volume of interest are extracted by approaches based on statistics, morphological features, and transform-based variables. Next, a statistical selection of the parameters that provide a high association and correlation with the clinical condition of interest is performed. Finally, processes such as data integration, standardization, classification, and mining processes can be applied as needed for particular applications. Ongoing research in radiomics aims to reduce the time and costs involved in interpreting medical images while simultaneously increasing the quality of diagnoses and monitoring of as well as the selection of treatment strategies. The results of many studies combining radiomics with standard medical techniques are highly encouraging, and these new approaches are increasingly used. This review article details the components of radiomics and discusses its applications, challenges, and future directions for this exciting new field of study.

In vivo pentamodal tomographic imaging for small animals

Multimodality molecular imaging emerges as a powerful strategy for correlating multimodal information. We developed a pentamodal imaging system which can perform positron emission tomography, bioluminescence tomography, fluorescence molecular tomography, Cerenkov luminescence tomography and X-ray computed tomography successively. Performance of sub-systems corresponding to different modalities were characterized. In vivo multimodal imaging of an orthotopic hepatocellular carcinoma xenograft mouse model was performed, and acquired multimodal images were fused. The feasibility of pentamodal tomographic imaging system was successfully validated with the imaging application on the mouse model. The ability of integrating anatomical, metabolic, and pharmacokinetic information promises applications of multimodality molecular imaging in precise medicine.