Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction is particularly challenging for PET-MRI as neither PET nor magnetic resonance imaging (MRI) can directly image tissue attenuation properties. MRI-based computed tomography (CT) synthesis has been proposed as an alternative to physics based and segmentation-based approaches that assign a population-based tissue density value in order to generate an attenuation map. We propose a novel deep fully convolutional neural network that generates synthetic CTs in a recursive manner by gradually reducing the residuals of the previous network, increasing the overall accuracy and generalisability, while keeping the number of trainable parameters within reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT pairs and a four-fold random bootstrapped validation with a 80:20 split is performed. Quantitative results show that the proposed framework outperforms a state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE) from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction error from 14.3% to 7.2%.Comment: Accepted at SASHIMI201

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

Validation & Verification of an EDA automated synthesis tool

Reliability and correctness are two mandatory features for automated synthesis tools. To reach the goals several campaigns of Validation and Verification (V&V) are needed. The paper presents the extensive efforts set up to prove the correctness of a newly developed EDA automated synthesis tool. The target tool, MarciaTesta, is a multi-platform automatic generator of test programs for microprocessors' caches. Getting in input the selected March Test and some architectural details about the target cache memory, the tool automatically generates the assembly level program to be run as Software Based Self-Testing (SBST). The equivalence between the original March Test, the automatically generated Assembly program, and the intermediate C/C++ program have been proved resorting to sophisticated logging mechanisms. A set of proved libraries has been generated and extensively used during the tool development. A detailed analysis of the lessons learned is reporte

Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs). However, it's still very challenging for translation tasks that require high quality, especially at high-resolution and photorealism. In this paper, we present Discriminative Region Proposal Adversarial Networks (DRPAN) for high-quality image-to-image translation. We decompose the procedure of image-to-image translation task into three iterated steps, first is to generate an image with global structure but some local artifacts (via GAN), second is using our DRPnet to propose the most fake region from the generated image, and third is to implement "image inpainting" on the most fake region for more realistic result through a reviser, so that the system (DRPAN) can be gradually optimized to synthesize images with more attention on the most artifact local part. Experiments on a variety of image-to-image translation tasks and datasets validate that our method outperforms state-of-the-arts for producing high-quality translation results in terms of both human perceptual studies and automatic quantitative measures.Comment: ECCV 201

Efficient Simulation of Structural Faults for the Reliability Evaluation at System-Level

In recent technology nodes, reliability is considered a part of the standard design ¿ow at all levels of embedded system design. While techniques that use only low-level models at gate- and register transfer-level offer high accuracy, they are too inefficient to consider the overall application of the embedded system. Multi-level models with high abstraction are essential to efficiently evaluate the impact of physical defects on the system. This paper provides a methodology that leverages state-of-the-art techniques for efficient fault simulation of structural faults together with transaction-level modeling. This way it is possible to accurately evaluate the impact of the faults on the entire hardware/software system. A case study of a system consisting of hardware and software for image compression and data encryption is presented and the method is compared to a standard gate/RT mixed-level approac