A Deep Primal-Dual Network for Guided Depth Super-Resolution

In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.Comment: BMVC 201

Temporal Interpolation via Motion Field Prediction

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning and guidance. Navigator slices are vital for retrospective stacking of 2D data slices in this method. However, they also prolong the acquisition sessions. Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking. In this work, we propose a convolutional neural network (CNN) based method for temporal interpolation via motion field prediction. The proposed formulation incorporates the prior knowledge that a motion field underlies changes in the image intensities over time. Previous approaches that interpolate directly in the intensity space are prone to produce blurry images or even remove structures in the images. Our method avoids such problems and faithfully preserves the information in the image. Further, an important advantage of our formulation is that it provides an unsupervised estimation of bi-directional motion fields. We show that these motion fields can be used to halve the number of registrations required during 4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherland

NEMA NU 2-2007 performance characteristics of GE Signa integrated PET/MR for different PET isotopes

BackgroundFully integrated PET/MR systems are being used frequently in clinical research and routine. National Electrical Manufacturers Association (NEMA) characterization of these systems is generally done with F-18 which is clinically the most relevant PET isotope. However, other PET isotopes, such as Ga-68 and Y-90, are gaining clinical importance as they are of specific interest for oncological applications and for follow-up of Y-90-based radionuclide therapy. These isotopes have a complex decay scheme with a variety of prompt gammas in coincidence. Ga-68 and Y-90 have higher positron energy and, because of the larger positron range, there may be interference with the magnetic field of the MR compared to F-18. Therefore, it is relevant to determine the performance of PET/MR for these clinically relevant and commercially available isotopes.MethodsNEMA NU 2-2007 performance measurements were performed for characterizing the spatial resolution, sensitivity, image quality, and the accuracy of attenuation and scatter corrections for F-18, Ga-68, and Y-90. Scatter fraction and noise equivalent count rate (NECR) tests were performed using F-18 and Ga-68. All phantom data were acquired on the GE Signa integrated PET/MR system, installed in UZ Leuven, Belgium.Results(18)F, Ga-68, and Y-90 NEMA performance tests resulted in substantially different system characteristics. In comparison with F-18, the spatial resolution is about 1mm larger in the axial direction for Ga-68 and no significative effect was found for Y-90. The impact of this lower resolution is also visible in the recovery coefficients of the smallest spheres of Ga-68 in image quality measurements, where clearly lower values are obtained. For Y-90, the low number of counts leads to a large variability in the image quality measurements. The primary factor for the sensitivity change is the scale factor related to the positron emission fraction. There is also an impact on the peak NECR, which is lower for Ga-68 than for F-18 and appears at higher activities.ConclusionsThe system performance of GE Signa integrated PET/MR was substantially different, in terms of NEMA spatial resolution, image quality, and NECR for Ga-68 and Y-90 compared to F-18. But these differences are compensated by the PET/MR scanner technologies and reconstructions methods