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Magnetic resonance imaging (MRI) is widely used as a non-invasive diagnostic technique to visualize the internal structure of biological systems. Quantitative analysis of magnetic resonance signal lifetimes, i.e., relaxation times, can reveal molecular scale information and has significance in the study of brain, spinal cord, articular cartilage, and cancer discrimination. Determination of MR relaxation spectra (relaxometry) is an inherently ill-posed problem. Conventional methods to extract MR relaxation spectra are computationally intensive, requiring high quality data and generally lacking the spectrum peak width information. A novel computationally efficient signal analysis method, based on neural networks (NN), has been developed to provide accurate real-time quantitative MR relaxation spectrum analysis. 
Deep learning with NN is a technique for solving complex nonlinear problems. NN have been optimized to determine 1D and 2D MR relaxation spectra. Simulated signals with Rician noise were employed for training the neural networks. The network performance was evaluated with simulated and experimental data, and compared with the traditional inverse Laplace transform (ILT) method. NN outperformed ILT. The 1D spectrum peak widths, generally considered not reliable with the traditional approach, could be determined accurately by the NN, noise permitting. 
The proposed exponential analysis method is not restricted to magnetic resonance. It is readily applicable in other areas with exponential analysis, such as fluorescence decay and radioactive decay. The method could be extended to higher dimensional spectra and adopted to solve other ill-posed problems.

CAP Virtual Congress 2021

Magnetic Resonance Relaxometry with Neural Networks

exponential analysis

magnetic resonance

machine learning

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technical paper

Magnetic resonance imaging (MRI) is widely used as a non-invasive diagnostic technique to visualize the internal structure of biological systems. MRI has limited spatial resolution and the microscopic behaviour within an image voxel cannot be visualized with qualitative images. Quantitative analysis of molecular diffusion provides insights into the microscopic structure beyond the MRI image resolution. It is challenging to analytically derive the MR diffusion signals for complex microscopic environments, particularly with susceptibility effects. In this work, an easy to use open-source Monte Carlo algorithm has been developed to simulate MR diffusion measurements under arbitrary conditions.
The self diffusion of water molecules can be described by Brownian motion. The Monte Carlo method was applied to simulate the Brownian motion in a user-defined microscopic environment. The fast simulation can be performed on any MRI experiments with user-defined magnetic field distribution. The method has been applied to predict nanoparticle configuration. Magnetic nanoparticles, serving as biosensors, distort the local magnetic field leading to changes in the MR diffusion signals. The simulation agreed with the experimental results. The nanoparticle concentration in water can be determined with MR diffusion measurements. 
We have developed an efficient, easy to use algorithm for rapid diffusion simulation in different microscopic environments with arbitrary magnetic fields. This simulation will be employed to optimize the nanoparticle biosensor systems for a wide range of targets, including cancer cells and COVID virus.

Monte Carlo Simulation for Magnetic Resonance Diffusion Measurements

In this talk, we show that any non-constant quantity defined on density matrices that is additive on tensor products and invariant under permutations cannot be "more than asymptotically continuous."The proof is a direct consequence of generalizing a protocol for embezzling entanglement. Joint work with Andrea Coladangelo.

Additive quantities cannot be more than asymptotically continuous

Quantum field theory is completely characterized by the field correlations between spacetime points. In turn, some of these can be accessed by locally coupling to the field simple quantum systems, a.k.a. particle detectors. In this work, we consider what happens when a quantum- controlled superposition of detectors at different space-time points is used to probe the correlations of the field. We show that, due to quantum interference effects, two detectors can gain information on field correlations which would not be otherwise accessible. This has relevant consequences for information theoretic quantities, like entanglement and mutual information harvested from the field. In particular, the quantum control allows for extraction of entanglement in scenarios where this is otherwise provably impossible.

Quantum Temporal Superposition: the case of QFT

DarkSide-20k is a planned two-phase liquid Argon time-projection-chamber (LAr-TPC) for direct WIMP search. The S1 and S2 light from the 20 ton fiducial volume is detected with Silicon photomultipliers and digitized. Due to the expected data rate of hundreds of MB/s it is impractical to record full waveform data continuously like in the smaller DarkSide-50 experiment, instead complex filtering and processing is required to select meaningful data. Since this processing needs to take into account all (above threshold) data channels at once, it cannot happen directly in the acquisition module, but all data has to be transferred to a processing farm, where processing time will likely exceed acquisition time. In order to address this, data will be compiled into time slices (on the order of a second), which get distributed to individual processing machines. The process is somewhat complicated by the fact that due to the long drift time in the TPC signals from the same physical event can be spread out over milliseconds, it is thus necessary for time slices to overlap enough to ensure each physical event is contained completely in one slice.
This presentation will give an overview of the DarkSide vertical slice scheme and show how it is implemented within a MIDAS-based data acquisition system.

The Vertical Slice Data Processing Scheme for DarkSide

Attosecond science is a relatively new research field founded on high-harmonic generation (HHG) in atomic and molecular gases, which has recently transitioned to experiment in 3-dimensional solids. Most of the physics underlying attosecond pulse emission from 2-dimensional semiconductors is unknown. Following years of research focusing on HHG in bulk semiconductors, this project focuses on the mostly unstudied van der Waals materials ReS$_2$ and MoS$_2$. We measure the power of the emitted high harmonics for varying orientation of the crystallographic axis with respect to the linear polarization of the incident driving laser beam. Both crystals emit high harmonics in a very distinctive pattern which reveals the symmetry of the crystal as well as microscopic details about the orbitals involved in the emission. Our demonstration inches us closer to understanding high-field phenomena in van der Waals materials, and controlling high-harmonic emission with unprecedented precision thanks to the high degree of tunability of 2-dimensional materials and their heterostructures.

An exploration for new phenomena using high-harmonic generation in two-dimensional van der Waals materials

Graphene is among the most promising materials considered for next-generation gas sensing due to its properties including high mechanical strength and flexibility, high surface-to-volume ratio, large conductivity, and low electrical noise. While gas sensors based on graphene devices have already demonstrated high sensitivity, one of the most important figures of merit, selectivity, remains a challenge. In the last few years, however, surface functionalization emerged as a potential route to achieve selectivity. In this talk, we focus on experiments where we functionalized the surface of CVD graphene field-effect transistors (GFET) through thermal evaporation of metal-free phthalocyanines and copper phthalocyanines. We present and discuss sensitivity and selectivity results obtained when such sensors are exposed to volatile organic compounds such as ethanol, toluene, formaldehyde, and acetone. In general, the functionalized GFET presented enhanced selectivity for oxygen-containing molecules (formaldehyde and acetone).

Functionalized surfaces of graphene field effect transistors for gas sensing

Quantum confinement and manipulation of charge carriers are critical for achieving devices practical for various quantum technologies. Atomically thin transition metal dichalcogenides (TMDCs) have attractive properties such as spin-valley locking, large spin-orbit coupling and high confinement energies which provide a promising platform for novel quantum technologies. In this talk, we present the design and fabrication of electrostatically gated quantum structures based on fully encapsulated monolayer tungsten diselenide (WSe2) aimed at probing the confined electron states in these structures. Furthermore, we show that laterally gated quantum point contacts successfully pinch-off the current across the device with gate voltages consistent with their lithographic widths. Finally, we discuss the origins of the observed mesoscopic transport features related to the emergence of the quantum dots through the WSe2 channel.

Electrostatically gated quantum dots in van der Waals materials

Approximation of the ground state wave function and energy of a quantum system is often achieved using perturbation theory or the variational method. The former approach suffers from the requirement that the Hamiltonian perturbation be small enough for the series to converge while the variational method is only as good as the choice of functions used in the expansion, providing only an approximate ground state whose mean energy is an upper bound to the ground state energy. The method of gradient descent, typically used for optimizing functions over finite dimensions, may be generalized to the infinite-dimensional Hilbert space of a quantum mechanical system to avoid both previous limitations. One may find the ground state of a quantum system by minimizing the energy functional of the wave function of the system via iterative calculation of better approximations using the gradient of the functional. This can be achieved in an infinite-dimensional setting by careful bookkeeping of only the non-zero components of the state vector in the chosen basis of expansion and those matrix elements of the Hamiltonian in that basis required to calculate the next iterative approximation via gradient descent. The sparse nature of the Hamiltonian matrix elements in a chosen basis for many quantum systems ensures that subsequent iterations of the state vector have only a finite number of non-zero components, thereby making the method computationally tractable.

The gradient descent method will be formulated for a quantum system with a time-independent Hamiltonian. The algorithm will be illustrated with a simple quantum system. The impact of symmetry in the choice of initial state vector will be discussed. Limitations of the method and potential improvements (e.g. conjugate gradient) will be considered.

Implementing the Gradient Descent Method in an Infinite Dimensional Hilbert Space

This talk will focus on an overview of Eliashberg theory, a formalism that Jules was very well known for. But I will also discuss some potential shortcomings of this framework, as time permits, from the weak coupling to the strong coupling limit, with polaron physics, and applicability to the hydride and superhydride materials.

Eliashberg Theory and Jules Carbotte

In this work we explore the use of multi-layer graphene (MLG) films grown by chemical vapor deposition for adaptive thermal camouflage. Using different ionic liquids, we tune the opto-electronic properties of MLG (150 – 200 layers) and investigate changes in optical reflectivity and emissivity in the infrared region (IR). We fabricate devices having a metallic back electrode supporting a porous membrane onto which we deposit the MLG. We use both non-stretchable polyethylene (PE), and stretchable polydimethylsiloxane (PDMS) as porous membranes. Using a thermal imaging system, we demonstrate that even when the device temperature is maintained higher than the environment, the MLG emissivity can be electrically controlled such that the device appears indistinguishable from the environment [1]. Moreover, we evaluate the performance of such devices based on flexible textiles towards developing a new material platform for defense applications.

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Magnetic Resonance Relaxometry with Neural Networks

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