A Plug-and-Play Approach To Multiparametric Quantitative MRI:Image Reconstruction Using Pre-Trained Deep Denoisers

Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neural Network (CNN), trained to remove generic white gaussian noise (not a particular subsampling artefact) from data. This CNN denoiser is then used as a data-driven shrinkage operator within the iterative reconstruction algorithm. This algorithm with the same denoiser model is then tested on two simulated acquisition processes with distinct subsampling patterns. The results show consistent de-aliasing performance against both acquisition schemes and accurate mapping of tissues' quantitative bio-properties. Software available: https://github.com/ketanfatania/QMRI-PnP-Recon-PO

Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions

A Slow-Cycling Proton Driver for a Neutrino Factory

An 8 Hz proton driver for a neutrino factory of 4 MW beam power and an energy of 25-30 GeV is under study at CERN, in parallel with a similar investigation using a 2.2 GeV high-energy linac and an accumulator plus a compressor ring cycling at 75 Hz. At RAL, synchrotron drivers with final energies of 5 and 15 GeV cycling at 50 and 25 Hz, respectively, are being studied. With these four scenarios, one hopes to cope with all possible constraints emerging from the studies of the pion production target and the muon rotation and cooling system. The high beam energy of this scenario requires less proton current and could inject into the SPS above transition and upgrade LHC and fixed target physics. Its 440 kW booster would upgrade ISOLDE.The main problems of the driver synchrotron are: the requirement of about 4 MV RF voltage at 10 MHz for acceleration and adiabatic bunch compression to the required r.m.s length of 1 ns; the sensitivity of the compression to the impedance of the vacuum chamber and to non-linearities of the momentum compaction of the high-gt lattice

Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network

In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task network to produce correlated outputs as a framework to enable localisation of region of interest (RoI) via classification, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task. We demonstrate how the multi-task network with RoI focused loss offers an advantage over other configurations of the network to achieve higher accuracy of performance. This is relevant to sCT where failure to accurately estimate high Hounsfield Unit values of bone could lead to impaired accuracy in clinical applications. We compare the dose calculation maps from the proposed sCT and the real CT in a radiation therapy treatment planning setup

Proton Drivers for Neutrino Factories: The CERN Approach

The paper describes the CERN approach for a proton driver for a Neutrino Factory. Two main layouts are presented: the so-called CERN Reference Scenario, based on a 2.2 GeV linac and an alternative one, based on a 30 GeV synchrotron. Both produce bunches of 1 ns (r.m.s.) and a beam power of 4 MW

In vivo myelin water quantification using diffusion–relaxation correlation MRI: A comparison of 1D and 2D methods

Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion–relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion

CERN PS laser ion source development

CERN, together with ITEP and TRINITI (Russia), is developing a CO2 laser ion source. The key design parameters are: 1.4 1010 ions of Pb25+ in a pulse of 5.5 ms, with a 4-rms emittance of 0.2 10-6 rad m, working at a repetition rate of 1 Hz. This device is considered as one candidate source for LHC heavy ion operation. The status of the laser development, the experimental set-up of the source consisting of the target area and its illumination, the plasma expansion area and extraction, beam transport and ion pre-acceleration by an RFQ, will be given

A Plug-and-Play Approach To Multiparametric Quantitative MRI:Image Reconstruction Using Pre-Trained Deep Denoisers

Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neural Network (CNN), trained to remove generic white gaussian noise (not a particular subsampling artefact) from data. This CNN denoiser is then used as a data-driven shrinkage operator within the iterative reconstruction algorithm. This algorithm with the same denoiser model is then tested on two simulated acquisition processes with distinct subsampling patterns. The results show consistent dealiasing performance against both acquisition schemes and accurate mapping of tissues' quantitative bio-properties. Software available: https://github.com/ketanfatania/QMRI-PnP-Recon-POC</p