MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks

Purpose A Magnetic Resonance Imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required. Methods Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the "style" for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on. Results This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly. Conclusion Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR contrasts or as a data augmentation technique for AI training in MRI.

Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks

A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. Each sequence can be parameterized through multiple acquisition parameters affecting MR image contrast, signal-to-noise ratio, resolution, or scan time. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize additional MR contrasts, generate synthetic data, or augment existing data for AI training. However, current generative approaches for the synthesis of MR images are only trained on images with a specific set of acquisition parameter values, limiting the clinical value of these methods as various sets of acquisition parameter settings are used in clinical practice. Therefore, we trained a generative adversarial network (GAN) to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, image orientation). This approach enables us to synthesize MR images with adjustable image contrast. In a visual Turing test, two experts mislabeled 40.5% of real and synthetic MR images, demonstrating that the image quality of the generated synthetic and real MR images is comparable. This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.

System Design for a Data-driven and Explainable Customer Sentiment Monitor

The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to identify customers who potentially become unsatisfied and might lead to escalations. Today this prioritization of customers is often done manually. Data science on IoT data (esp. log data) for machine health monitoring, as well as analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. In this paper, we present a framework for a data-driven decision support system which combines IoT and enterprise data to model customer sentiment. Such decision support systems can help to prioritize customers and service resources to effectively troubleshoot problems or even avoid them. The framework is applied in a real-world case study with a major medical device manufacturer. This includes a fully automated and interpretable machine learning pipeline designed to meet the requirements defined with domain experts and end users. The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices. Furthermore, we provide an anonymized industrial benchmark dataset for the research community.