A Four-Dimensional Image Reconstruction Framework for PET under Arbitrary Geometries

Positron Emission Tomography (PET) is a functional imaging modality with applications ranging from the treatment of cancer, studying neurological diseases and disease models. Virtual-Pinhole PET technology improves the image quality in terms of resolution and contrast recovery. The technology calls for having a detector with smaller crystals placed near a region of interest in a conventional whole-body PET scanner. The improvement is from the higher spatial sampling of the imaging area near the detector. A prototype half-ring PET insert built to study head-and-neck cancer imaging was extended to breast cancer imaging. We have built a prototype half-ring PET insert for head-and-neck cancer imaging applications. In the first half of this work, we extend the use of the insert to breast imaging and show that such a system provides high resolution images of breast and axillary lymph nodes while maintaining the full imaging field of view capability of a clinical PET scanner. We are focused on designing unconventional PET geometries for specific applications. A general purpose 4D PET reconstruction framework was created to estimate the radionuclide uptake in the subject. Quantitative estimation in PET requires precise modeling of PET physics. Data acquired in a PET scanner is well modeled as a Poisson counting process. Reconstruction given the forward model is implemented using MAP-OSEM. The framework is capable of reconstructing PET data under arbitrary position of the detector elements and different crystal sizes. A novel symmetry finding algorithm is created to reduce the system matrix size, without loss of resolution. The framework motivates investigation into different PET system geometries for different applications, as well as optimizing the design of PET systems. A generalized normalization procedure was developed to model unknown components. The programs are parallelized using OpenMP and MPI to run on small workstations as well as super-computing clusters. The performance of our reconstruction framework is presented through four novel and unconventional PET systems, each designed specifically for a different geometry. The Virtual-Pinhole half-ring system is a half-ring insert integrated into a Siemens Biograph-40, for head and neck imaging. The Flat-panel system is a modular insert system integrated into the Biograph-40, designed for breast cancer imaging. The MicroInsert II is the second generation full ring insert device, integrated into the MicroPET scanner to improve the resolution and contrast recovery of the MicroPET scanner. The Plant PET system is a PET system designed to image plants vertically, and integrated into a plant growth chamber. The improvement in speed/memory from symmetry finding is as high as a factor of 50 in some cases. Further improvements to the framework and state of the field are also discussed

Super-Resolution Technique for MRI Images Using Artificial Neural Network

Image upscaling is an important field of digital image processing. It is often required to create higher resolution images from the lower resolution images at hand in computer graphics, media devices, satellite imagery etc. Upscaling is also referred to as ?single image super-resolution'. The process is a tradeoff between efficiency, time and the quality of output images obtained. Images with higher quality are needed and are essential in many areas like medical, astronomy, surveillance, satellite imaging etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the important diagnosis instrument to determine the presence of certain diseases. Many techniques like PET (Positron Emission Tomography), CT (Computed Tomography), MRI (Magnetic Resonance Imaging) in the medical field are used for detecting diseases. Generally, medical images suffer from low resolution, High level of noise and blur type of factors. In the present paper, a feed forward neural network using supervised training for image upscaling is proposed. The performance of a neural network is compared to different training function & measure PSNR

MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.Comment: 16 pages, 8 figure

Noninvasive Stem Cell Labeling Using USPIO Technique and their Detection with MRI

Background: To date, several imaging techniques to track stem cells are used such as positron emission tomography (PET), single photon emission computed tomography (SPECT), Bioluminescence imaging (BLI), fluorescence imaging, CT scan and magnetic resonance imaging (MRI). Although, overall sensitivity of MRI compared to SPECT and Bioluminescence techniques are lower, but due to high spatial resolution (~100 mm), long term three-dimensional imaging capability, in vivo quick access to images in three different sections, and noninvasiveness it is being used as the method of choice. Methods: The present study is the search results for authors and sources of information in the field of molecular and cellular imaging to examine the problems and perspectives about stem cells labeling with Ultrasmall Super Paramagnetic Iron Oxide (USPIO) and their tracking by MRI. Results: With the advancement of technology, including quantum physics, chemistry, and computer software, MRI with an excellent spatial resolution and contrast, is surpasses other imaging modalities in the analysis of anatomical and pathological features and images of all body tissues. From the other side, advances in the astronomical science, chemistry and nanotechnology, high biocompatibility and cytotoxicity of nanoparticles, and due to analysis in the metabolic pathways of iron made the procedure easier; however, there are still several fundamental questions in understanding the mechanism of biological molecules in the living cells including: 1- How to detect not only the location but also the performance of the labeled cells? Probably combination of USPIO nanoparticles with other reporter genes as contrast agents for MRI and PET can simultaneously be used to overcome these limitations 2) How to trace stem cells from pre-clinical models to translate to humans? Up to now, due to issues of bioethics, little studies have been done in this area. 3) Whether the transplanted stem cells that have reached the target tissue, will remain or migrate? Despite the fact that cell proliferation and exocytosis are two main factors for long term protection of USPIO nanoparticles inside cells, their signals cannot be received for a long time. 4) What mechanisms cause stem cells reaching the target tissue to react with their environment? And 5) what is the number of transplanted cells in live tissue, and what is their half-life? Conclusion: This study showed that USPIO nanoparticles can enter the cell with a clear dose without any adverse biological effects and could be detected by SWI and T2* techniques under MRI (1.5 Tesla) scanner for almost one month. MRI as a secure mean can illustrate with optimal resolution the spatial-resolution and three-dimensional positions of the stem cells. Keywords: Ultrasmall Super Paramagnetic Iron Oxide (USPIO), labeled stem cell, in vivo tracking, MRI