Advances in sparse dynamic scanning in spectromicroscopy through compressive sensing.

Scanning microscopies and spectroscopies like X-ray Fluorescence (XRF), Scanning Transmission X-ray Microscopy (STXM), and Ptychography are of very high scientific importance as they can be employed in several research fields. Methodology and technology advances aim at analysing larger samples at better resolutions, improved sensitivities and higher acquisition speeds. The frontiers of those advances are in detectors, radiation sources, motors, but also in acquisition and analysis software together with general methodology improvements. We have recently introduced and fully implemented an intelligent scanning methodology based on compressive sensing, on a soft X-ray microscopy beamline. This demonstrated sparse low energy XRF scanning of dynamically chosen regions of interest in combination with STXM, yielding spectroimaging data in the megapixel-range and in shorter timeframes than were previously not feasible. This research has been further developed and has been applied to scientific applications in biology. The developments are mostly in the dynamic triggering decisional mechanism in order to incorporate modern Machine Learning (ML) but also in the suitable integration of the method in the control system, making it available for other beamlines and imaging techniques. On the applications front, the method was previously successfully used on different samples, from lung and ovarian human tissues to plant root sections. This manuscript introduces the latest methodology advances and demonstrates their applications in life and environmental sciences. Lastly, it highlights the auxiliary development of a mobile application, designed to assist the user in the selection of specific regions of interest in an easy way.

Operator Relaxation and the Optimal Depth of Classical Shadows.

Classical shadows are a powerful method for learning many properties of quantum states in a sample-efficient manner, by making use of randomized measurements. Here we study the sample complexity of learning the expectation value of Pauli operators via "shallow shadows," a recently proposed version of classical shadows in which the randomization step is effected by a local unitary circuit of variable depth t. We show that the shadow norm (the quantity controlling the sample complexity) is expressed in terms of properties of the Heisenberg time evolution of operators under the randomizing ("twirling") circuit-namely the evolution of the weight distribution characterizing the number of sites on which an operator acts nontrivially. For spatially contiguous Pauli operators of weight k, this entails a competition between two processes: operator spreading (whereby the support of an operator grows over time, increasing its weight) and operator relaxation (whereby the bulk of the operator develops an equilibrium density of identity operators, decreasing its weight). From this simple picture we derive (i) an upper bound on the shadow norm which, for depth t∼log(k), guarantees an exponential gain in sample complexity over the t=0 protocol in any spatial dimension, and (ii) quantitative results in one dimension within a mean-field approximation, including a universal subleading correction to the optimal depth, found to be in excellent agreement with infinite matrix product state numerical simulations. Our Letter connects fundamental ideas in quantum many-body dynamics to applications in quantum information science, and paves the way to highly optimized protocols for learning different properties of quantum states.

Reconstruction of 3D topographic landscape in soft X-ray fluorescence microscopy through an inverse X-ray-tracing approach based on multiple detectors.

The study of X-ray fluorescence (XRF) emission spectra is a powerful technique used in applications that range from biology to cultural heritage. Key objectives of this technique include identification and quantification of elemental traces composing the analyzed sample. However, precise derivation of elemental concentration is often hampered by self-absorption of the XRF signal emitted by light constituents. This attenuation depends on the amount of sample present between the radiation source and detection system and allows for the exploitation of self-absorption in order to recover a sample topography. In this work, an X-ray-tracing application based on the use of multiple silicon drift detectors, is introduced to inversely reconstruct a 3D sample with correct topographical landscape, from 2D XRF count rates maps obtained from spectroscopy. The reconstruction was tested on the XRF maps of a simulated sample, which is composed of three cells with different size but similar composition. We propose to use the recovered 3D sample topography in order to numerically compute the self-absorption effects on the X-ray fluorescence radiation, thereby showing that a quantitative correction is possible. Lastly, we present a web application which implements the suggested methodology, in order to demonstrate its feasibility and applicability, available at: https://github.com/ElettraSciComp/xrfstir .

Time-crystalline eigenstate order on a quantum processor.

Quantum many-body systems display rich phase structure in their low-temperature equilibrium states1. However, much of nature is not in thermal equilibrium. Remarkably, it was recently predicted that out-of-equilibrium systems can exhibit novel dynamical phases2-8 that may otherwise be forbidden by equilibrium thermodynamics, a paradigmatic example being the discrete time crystal (DTC)7,9-15. Concretely, dynamical phases can be defined in periodically driven many-body-localized (MBL) systems via the concept of eigenstate order7,16,17. In eigenstate-ordered MBL phases, the entire many-body spectrum exhibits quantum correlations and long-range order, with characteristic signatures in late-time dynamics from all initial states. It is, however, challenging to experimentally distinguish such stable phases from transient phenomena, or from regimes in which the dynamics of a few select states can mask typical behaviour. Here we implement tunable controlled-phase (CPHASE) gates on an array of superconducting qubits to experimentally observe an MBL-DTC and demonstrate its characteristic spatiotemporal response for generic initial states7,9,10. Our work employs a time-reversal protocol to quantify the impact of external decoherence, and leverages quantum typicality to circumvent the exponential cost of densely sampling the eigenspectrum. Furthermore, we locate the phase transition out of the DTC with an experimental finite-size analysis. These results establish a scalable approach to studying non-equilibrium phases of matter on quantum processors.

Respiratory motion correction for enhanced quantification of hepatic lesions in simultaneous PET and DCE-MR imaging.

Simultaneous positron-emission tomography (PET)-magnetic resonance (MR) imaging is a hybrid technique in oncological hepatic imaging combining soft-tissue and functional contrast of dynamic contrast enhanced MR (DCE-MR) with metabolic information from PET. In this context, respiratory motion represents a major challenge by introducing blurring, artifacts and misregistration in the liver. In this work, we propose a free-breathing 3D non-rigid respiratory motion correction framework for simultaneously acquired DCE-MR and PET data, which makes use of higher spatial resolution MR data to derive motion information used directly during image reconstruction to minimize image blurring and motion artifacts. The main aim was to increase contrast of hepatic metastases to improve their detection and characterization. DCE-MR data were acquired at 3T through a golden radial phase encoding scheme, enabling derivation of motion fields. These were used in the motion compensated image reconstruction of DCE-MR time-series (48 time-points, 6 s temporal resolution, 1.5 mm isotropic spatial resolution) and 3D PET activity map, which was subsequently interpolated to the DCE-MR resolution. The extended Tofts model was fitted to DCE-MR data, obtaining functional parametric maps related to perfusion such as the endothelial permeability (Kt). Fifty-seven hepatic metastases were identified and analyzed. Quantitative evaluations of motion correction in PET images demonstrated average percentage increases of 16% ± 5% (mean ± SD) in Contrast (C), 18% ± 6% in SUVmeanand 14% ± 2% in SUVmax, while DCE-MR andKtscored contrast-to-noise-ratio increases of 64% ± 3% and 90% ± 6%, respectively. Motion-corrected data visually showed improved image contrast of hepatic metastases and effectively reduced blurring and motion artefacts. Scatter plots of SUVmeanversusKtsuggested that the proposed framework improved differentiation ofKtmeasurements. The presented motion correction framework for simultaneously acquired PET-DCE-MR data provides accurately aligned images with increased contrast of hepatic lesions allowing for improved detection and characterization.

Postselection-Free Entanglement Dynamics via Spacetime Duality.

The dynamics of entanglement in "hybrid" nonunitary circuits (for example, involving both unitary gates and quantum measurements) has recently become an object of intense study. A major hurdle toward experimentally realizing this physics is the need to apply postselection on random measurement outcomes in order to repeatedly prepare a given output state, resulting in an exponential overhead. We propose a method to sidestep this issue in a wide class of nonunitary circuits by taking advantage of spacetime duality. This method maps the purification dynamics of a mixed state under nonunitary evolution onto a particular correlation function in an associated unitary circuit. This translates to an operational protocol which could be straightforwardly implemented on a digital quantum simulator. We discuss the signatures of different entanglement phases, and demonstrate examples via numerical simulations. With minor modifications, the proposed protocol allows measurement of the purity of arbitrary subsystems, which could shed light on the properties of the quantum error correcting code formed by the mixed phase in this class of hybrid dynamics.

Flexible numerical simulation framework for dynamic PET-MR data.

This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms. Contrast and tracer kinetics can also be simulated and are available as ground truth information. To closely emulate medical examination, input and output of the simulation are files in standardised open-source raw data formats. This enables the use of existing raw data as a template input and ensures seamless integration of the output into existing reconstruction pipelines. The proposed framework was validated in PET-MR and image registration applications. It was used to simulate a FDG-PET-MR scan with cardiac and respiratory motion. Ground truth motion information could be utilised to optimise parameters for PET and synergistic PET-MR image registration. In addition, a free-breathing dynamic contrast enhancement (DCE) abdominal scan of a patient with hepatic lesions was simulated. In order to correct for breathing motion, a motion-corrected image reconstruction scheme was used and a Toft's model was fit to the DCE data to obtain quantitative DCE-MRI parameters. Utilising the ground truth motion information, the dependency of quantitative DCE-MR images on the accuracy of the motion estimation was evaluated. We demonstrated that respiratory motion had to be available with an average accuracy of at least the spatial resolution of the DCE-MR images in order to ensure an improvement in lesions visualisation and quantification compared to no motion correction. The proposed framework provides a valuable tool with a wide range of scientific PET and MR applications and will be available as part of the open-source project Synergistic Image Reconstruction Framework (SIRF).

Dimensional Crossover of the Integer Quantum Hall Plateau Transition and Disordered Topological Pumping.

We study the quantum Hall plateau transition on rectangular tori. As the aspect ratio of the torus is increased, the two-dimensional critical behavior, characterized by a subthermodynamic number of topological states in a vanishing energy window around a critical energy, changes drastically. In the thin-torus limit, the entire spectrum is Anderson localized; however, an extensive number of states retain a Chern number C≠0. We resolve this apparent paradox by mapping the thin-torus quantum Hall system onto a disordered Thouless pump, where the Chern number corresponds to the winding number of an electron's path in real space during a pump cycle. We then characterize quantitatively the crossover between the one- and two-dimensional regimes for finite torus thickness, where the average Thouless conductance also shows anomalous scaling.

Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi-Site Reproducibility and Single-Site Robustness.

BACKGROUND AND PURPOSE: Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment. METHODS: A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm3 reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS: Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients. CONCLUSION: Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.

3D nonrigid motion correction for quantitative assessment of hepatic lesions in DCE-MRI.

PURPOSE: To provide nonrigid respiratory motion-corrected DCE-MRI images with isotropic resolution of 1.5 mm, full coverage of abdomen, and covering the entire uptake curve with a temporal resolution of 6 seconds, for the quantitative assessment of hepatic lesions. METHODS: 3D DCE-MRI data were acquired at 3 T during free breathing for 5 minutes using a 3D T1 -weighted golden-angle radial phase-encoding sequence. Nonrigid respiratory motion information was extracted and used in motion-corrected image reconstruction to obtain high-quality DCE-MRI images with temporal resolution of 6 seconds and isotropic resolution of 1.5 mm. An extended Tofts model was fitted to the dynamic data sets, yielding quantitative parametric maps of endothelial permeability using the hepatic artery as input function. The proposed approach was evaluated in 11 patients (52 ± 17 years, 5 men) with and without known hepatic lesions, undergoing DCE-MRI. RESULTS: Respiratory motion produced artifacts and misalignment between dynamic volumes (lesion average motion amplitude of 3.82 ± 1.11 mm). Motion correction minimized artifacts and improved average contrast-to-noise ratio of hepatic lesions in late phase by 47% (p < .01). Quantitative endothelial permeability maps of motion-corrected data demonstrated enhanced visibility of different pathologies (e.g., metastases, hemangiomas, cysts, necrotic tumor substructure) and showed improved contrast-to-noise ratio by 62% (p < .01) compared with uncorrected data. CONCLUSION: 3D nonrigid motion correction in DCE-MRI improves both visual and quantitative assessment of hepatic lesions by ensuring accurate alignment between 3D DCE images and reducing motion blurring. This approach does not require breath-holds and minimizes scan planning by using a large FOV with isotropic resolution.

Quantitative susceptibility mapping across two clinical field strengths: Contrast-to-noise ratio enhancement at 1.5T.

BACKGROUND: Quantitative susceptibility mapping (QSM) is an MRI postprocessing technique that allows quantification of the spatial distribution of tissue magnetic susceptibility in vivo. Contributing sources include iron, blood products, calcium, myelin, and lipid content. PURPOSE: To evaluate the reproducibility and consistency of QSM across clinical field strengths of 1.5T and 3T and to optimize the contrast-to-noise ratio (CNR) at 1.5T through bandwidth tuning. STUDY TYPE: Prospective. SUBJECTS: Sixteen healthy volunteers (10 men, 6 women; age range 24-37; mean age 27.8 ± 3.2 years). FIELD STRENGTHS/SEQUENCES: 1.5T and 3T systems from the same vendor. Four spoiled gradient echo (SPGR) sequences were designed with different acquisition bandwidths. ASSESSMENT: QSM reconstruction was achieved through a nonlinear morphology-enabled dipole inversion (MEDI) algorithm employing L1 regularization. CNR was calculated in seven regions of interest (ROIs), while reproducibility and consistency of QSM measurements were evaluated through voxel-based and region-specific linear correlation analyses and Bland-Altman plots. STATISTICAL TESTS: Interclass correlation, Wilcoxon rank sum test, linear regression analysis, Bland-Altman analysis, Welch's t-test. RESULTS: CNR analysis showed a statistically significant (P < 0.05) increase in four out of seven ROIs for the lowest bandwidth employed with respect to the highest (25.18% increase in CNR of caudate nucleus). All sequences reported an excellent correlation across field strength and bandwidth variation (R ≥ 0.96, widest limits of agreement from -18.7 to 25.8 ppb) in the ROI-based analysis, while the correlation was found to be good for the voxel-based analysis of averaged maps (R ≥ 0.90, widest limits of agreement from -9.3 to 9.1 ppb). DATA CONCLUSION: CNR of QSM images reconstructed from 1.5T acquisitions can be enhanced through bandwidth tuning. MEDI-based QSM reconstruction demonstrated to be reproducible and consistent both across field strengths (1.5T and 3T) and bandwidth variation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1410-1420.