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Magnetic Resonance Imaging (MRI) is a powerful imaging modality with excellent soft tissue contrast. Contrast agent such as iron oxide nanoparticles can be used to “tag” individual cells, distorting the magnetic field around them and allowing the imaging of single cells. Time-lapse MRI can be used to track the motion of tagged cells, providing insights in the studies of inflammatory diseases and metastasis of cancer. Current methods have a very limited temporal resolution resulting in a detection limit of 1µm/s . In addition, the manual cell counting is time consuming and difficult. 
In this work, a dictionary learning based technique has been developed to accelerate the MRI data acquisition and aid in the task of locating cells. Dictionary learning is a machine learning technique, in which features of an image can be ‘learned’ as atoms. Images can be represented using a sparse combination of atoms. The sparsity property can be used as a constraint in non-linear image reconstruction with data sampled below the Nyquist criteria. The undersampling improved the temporal resolution of the in-vivo measurements by approximately an order of magnitude. The dictionary atom coefficients provided information on the cell locations for feature detection.

CAP Virtual Congress 2021

Magnetic Resonance Imaging Data Acquisition Acceleration and Feature Detection with Dictionary Learning

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**WELCOME TO THE 2021 CAP VIRTUAL CONGRESS UNDERLINE PLATFORM**

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technical paper

The versatility of gamma-ray spectroscopy has given rise to its many applications, from quantification of trace elements in a sample to maintaining nuclear material safeguards. Depending on the application and gamma-ray detector, there is often a compromise made between detection efficiency and energy resolution. While characterizing or quantifying trace radionuclide concentrations in an unknown sample, energy resolution is often the more important property. In these situations, high-purity germanium (HPGe) detectors are the detector of choice as they have a superior energy resolution which significantly reduce measurement uncertainties and improve minimum detection limits.

Applications using HPGe detectors are limited to counting rates on the order of a few tens of thousands of counts per second (cps) before the performance of the detector is severely diminished. The limiting factor for high counting rate measurements comes from the need to shape signals with a relatively long shaping time, on the order of 1-6 μs, to maintain good energy resolution. At higher counting rates however, if a signal is received while a previous signal is still being shaped, pulse-pileup occurs. Pulse-pileup distorts the energy measurement of the previous signal and entirely drops the measurement of the second signal. Traditionally, this situation is usually handled with a pileup rejection method. At ultra high counting rates, on the order of a million cps, the percentage of the pileup rejection is so high that the spectroscopy system suffers from extremely high deadtimes.

In order to solve this critical problem, the present study aims to develop a data acquisition system capable of deconvoluting pileup signals into two or more recovered signals in real-time. This technique has been applied for NaI(Tl) and silicon drift detectors, which showed very promising results. However, applications of this technique for HPGe detectors have been unsuccessful as it relies on a fixed signal rising edge shape, a feature which cannot applied for HPGe detectors due to the variation in the rising shape. Our study has been focused on developing a deconvolution algorithm to identify and recover piled-up signals using a planar HPGe. During this development stage, signal waveforms are analyzed offline, optimizing the algorithm over a range of counting rates while building a library of the rising edge shapes. Once completed, the deconvolution algorithm will be benchmarked for various high-rate measurements. The preliminary result of the pile-up deconvolution performance will be presented.

Development of a digital data acquisition system capable of pulse-pileup recovery for HPGe detectors

Polycrystalline monolayer graphene films grown by chemical vapor deposition were exposed to a low-pressure inductively-coupled plasma operated in a gaseous mixture of argon and diborane. Optical emission spectroscopy and plasma sampling mass spectrometry reveal high B2H6 fragmentation leading to significant populations of both boron and hydrogen species in the gas phase. X-ray photoelectron spectroscopy indicates the formation of a boron-containing layer at the surface and provides evidence of a substitutional incorporation of boron atoms within the graphene lattice. To probe plasma’s influence on graphene structure, Hyperspectral Raman Imaging (RIMA for Raman Imaging) is used to obtain qualitative as well as quantitative data on a macroscopic scale. Graphene domains doping by graphitic boration is then confirmed by hyperspectral Raman Imaging of graphene domains. These results demonstrate that diborane-containing low-pressure plasmas are an efficient mean for boron substitutional incorporation in graphene with minimal domain hydrogenation and defect generation.

Boron substitutional doping of graphene by low- pressure diborane-argon plasma

We seek to design experimentally feasible broadband multiplexed optical quantum memory with near-term applications to telecom bands. Specifically, we devise dispersion compensation for an impedance-matched narrow-band quantum memory by exploiting Raman processes over two three-level atomic subensembles, one for memory and the other for dispersion compensation. Our proposed broadband quantum memory employs three-level atoms with atomic density, cavity quality, and Raman-laser power and detuning chosen such that inverse cavity lifetime equal so ptical depth, the delay-bandwidth product exceeds $10^6$, power efficiency exceeding 90% and at least one second of storage time, thereby leading to $10^6$ modes for multiplexing. Our design will lead to significant multiplexing enhancement for quantum repeaters to be used for telecom quantum networks.

Broadband quantum memory in a cavity via zero spectral dispersion

The radioactive decay of radon in the home is the leading cause of lung cancer in non-smoking Canadians (REF 1,2). Radon produced by the decay of uranium and thorium minerals entering the home may accumulate in concentrations that exceed the national maximum guideline for indoor air of 200 Bq/m3. There is a critical need to develop a practical tool to assess an individual’s exposure to radon and eventually one’s lung cancer risk. An important opportunity is to use keratinizing tissues in the body (hair, nails) as archives of radon exposure. The lead is sequestered from the environment in toenails, including the relatively long-lived (22 yrs) 210Pb isotope, which comes from 222Rn decay. 

In this project, we are using isotope ratio mass spectrometry to quantify the amount of 210Pb in a known amount of sample. This method has the advantage of providing a direct and relatively rapid count of the numbers of 210Pb atoms. The challenge is that the actual number of 210Pb atoms is very low and achieving reliable results requires high sensitivity methods specifically designed for the extraction of lead from the biological matrix. In the first stages of the project, we are using isotope dilution methods coupled with multiple collector inductively coupled plasma mass spectrometry (MC-ICPMS). Initial results demonstrate that femtogram quantities of lead can be measured.

The next stage of the project involves the design and construction of a laser ablation ion source coupled to the Multiple Reflection Time of Flight (MR-TOF MS) at the TITAN instrument at TRIUMF. The laser ion source in combination with the MR-TOF MS offers high sensitivity and the ability to separate isobars of 210Pb. The laser beam, after passing through optical telescope system and polarizers for pulse energy modulation, is focused on a small point on the sample surface located in a high-vacuum chamber. Thus, the laser source enables spatial mapping of 210Pb isotopic composition and allow one to map the accumulation of the radon daughter products over the growth of tissue. Ultimately, an accurate measurement of the number of accumulated atoms in an individual’s biological tissue may be a personalized biodosimeter for radon.

1. Gogna, Priyanka, et al. "Estimates of the current and future burden of lung cancer attributable to residential radon exposure in Canada." Preventive medicine 122 (2019): 100-108
2. Stanley, F. K., Zarezadeh, S., Dumais, C. D., Dumais, K., MacQueen, R., Clement, F., & Goodarzi, A. A. (2017). Comprehensive survey of household radon gas levels and risk factors in southern Alberta. CMAJ open, 5(1), E255–E264. https://doi.org/10.9778/cmajo.20160142
3. Stanley, Fintan KT, et al. "Radon exposure is rising steadily within the modern North American residential environment and is increasingly uniform across seasons." Scientific reports 9.1 (2019): 1-17.

Development of an isotopic biodosimeter to assess radon gas exposure

We model a number of both closed-packed and non-closed-packed crystals inside a cholesteric liquid crystal (LC) with different pitch values and nematic LC through the Landau–de Gennes free-energy method[1]. We investigated the anisotropic interactions between particles with heterogeneous boundary conditions inside both nematic and cholesteric liquid crystals[2]. The results show that it is energetically favorable for the particles to remain in a plane parallel to the far-field director in a nematic liquid crystal, while for particles immersed in a cholesteric there are multiple energy minima not all located in the same plane. Therefore, We became interested in investigating the stability of binary crystals [3]. The results indicate that body-centered-cubic (BCC) crystals have a lower-energy lattice defect structure than the diamond crystal. Furthermore, it is shown that a pair of binary colloids can be self-assemble into a stable face-centered- cubic lattice structure inside a nematic LC, as it has the lowest energy comparing to diamond and BCC crystals.

1-P.G. de Gennes and J. Prost, The Physics of Liquid Crystals (Oxford University Press, Oxford, 1993)
2-Phys. Rev. E 95, 052703 – Published 30 May 2017
3-Phys. Rev. E 99, 052701 – Published 13 May 2019

Stability Of Binary Colloidal Crystals Immersed In a Cholesteric Liquid Crystal

High-power lasers are rapidly becoming standard tools in advanced manufacturing, mainly in the form of laser welding, laser cutting, and laser additive manufacturing. Of these applications, laser welding in the electric mobility sector---particularly in the manufacturing of battery packs---presents unique challenges. Weld depth needs to be precisely controlled, not only to ensure joint strength, but also to ensure the weld does not puncture into the lithium ion cell. In addition, these processes often involve highly reflective metals (such as copper), which have material properties that lead to unstable welds; this requires an unprecedented level of control to ensure weld quality and depth. To better monitor and control weld quality, we need fully in-line monitoring during the process. Inline Coherent Imaging (ICI) is a process monitoring technique that has been demonstrated to measure keyhole depth (down to 15 um axial resolution) at high imaging rates (~200 kHz), even in high aspect ratio features. Due to its interferometric nature, coherent imaging has unparalleled sensitivity and dynamic range. However, it suffers from speckle noise, which degrades high-speed measurements by orders of magnitude, and false interfaces that arise from unwanted interferences (“autocorrelation” peaks). These pose a significant challenge to quality assurance and closed-loop control, particularly in highly dynamic laser processing applications, such as copper welding. To mitigate these problems, we have integrated a second, automatically synchronized, imaging channel into a standard ICI system, by exploiting a previously unused part of the imaging window. This “witness” image allows us to identify real signatures based on correlation and filter out the uncorrelated noise. Using this system, we have demonstrated the complete removal of autocorrelation artifacts, and have increased signal to noise by a factor of two, with no loss of imaging rate or spatial resolution compared to standard ICI. When applied to imaging laser keyhole welding, the false interface detection rate is reduced from 10% to 0.15%, yielding improved tracking of the true morphology.

Self-witnessing coherent imaging for artifact removal and noise filtering in laser keyhole welding

Raman spectroscopy is an efficient method to characterize the graphene structure. The technique gives distinctive features for pristine, damaged and even doped graphene. Nonetheless, especially when graphene is grown on a polycrystalline substrate, strong discrepancies may appear on the macroscopic scale. Moreover, in the case of plasma irradiation of graphene, it is essential to understand the impact of the small heterogeneities in pristine graphene (local defects, grain boundaries, etc.) on the resulting graphene structure after treatment [1]. Hyperspectral Raman Imaging (RIMA for Raman Imaging) is a powerful method enabling the capture of qualitative as well as quantitative data on a macroscopic scale [2]. Grain Boundaries (GBs) reveal themselves being more resistant to plasma treatment than pristine graphene domains. After careful consideration of Raman parameters, it appears clearly that preferential self-healing of GBs and its surrounding is taking place, a phenomenon observed in 3D materials, yet to be observed in graphene. This mechanism is governed by carbon adatoms generated from impacts of low-energy argon ions with graphene film. Under constant irradiation from exited species (ions, metastable, VUV photons), carbon adatoms can easily migrate on graphene surface and, in particular, alongside GBs. Hence, defects created at GBs or present nearby might be healed by the adatoms influx [3]. Furthermore, another plasma conditions shown that energy fluence from Argon metastable deexcitation can be linked to an enhanced defect migration and self-healing at GBs [4]. Finally, the previous study in Argon plasma enable the determination of ideal operating conditions for Argon plasma with B2H6 trace. The exposition of graphene to such plasma reveals boron in-plane substitution combined with low-level hydrogenation and defect generation [5].

Fundamental study of plasma-graphene interactions in Argon/B2H6 plasma

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.

Magnetic Resonance Relaxometry with Neural Networks

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.

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Magnetic Resonance Imaging Data Acquisition Acceleration and Feature Detection with Dictionary Learning

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