Denoising single MR spectra by deep learning: Miracle or mirage?

PURPOSE The inherently poor SNR of MRS measurements presents a significant hurdle to its clinical application. Denoising by machine or deep learning (DL) was proposed as a remedy. It is investigated whether such denoising leads to lower estimate uncertainties or whether it essentially reduces noise in signal-free areas only. METHODS Noise removal based on supervised DL with U-nets was implemented using simulated 1 H MR spectra of human brain in two approaches: (1) via time-frequency domain spectrograms and (2) using 1D spectra as input. Quality of denoising was evaluated in three ways: (1) by an adapted fit quality score, (2) by traditional model fitting, and (3) by quantification via neural networks. RESULTS Visually appealing spectra were obtained; hinting that denoising is well-suited for MRS. However, an adapted denoising score showed that noise removal is inhomogeneous and more efficient for signal-free areas. This was confirmed by quantitative analysis of traditional fit results as well as DL quantitation following DL denoising. DL denoising, although apparently successful as judged by mean squared errors, led to substantially biased estimates in both implementations. CONCLUSION The implemented DL-based denoising techniques may be useful for display purposes, but do not help quantitative evaluations, confirming expectations based on estimation theory: Cramér Rao lower bounds defined by the original data and the appropriate fitting model cannot be circumvented in an unbiased way for single data sets, unless additional prior knowledge can be incurred in the form of parameter restrictions/relations or applicable substates

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.

PURPOSE The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution. METHODS Simulated 1D spectra and 2D spectrograms that mimic an extensive range of pathological in vivo conditions are used to train and test 24 different DL architectures. Active learning through altered training and testing data distributions is probed to optimize quantification performance. Ensembles of networks are explored to improve DL robustness and reduce the variance of estimates. A set of scores compares performances of DL predictions and traditional model fitting (MF). RESULTS Ensembles of heterogeneous networks that combine 1D frequency-domain and 2D time-frequency domain spectrograms as input perform best. Dataset augmentation with active learning can improve performance, but gains are limited. MF is more accurate, although DL appears to be more precise at low SNR. However, this overall improved precision originates from a strong bias for cases with high uncertainty toward the dataset the network has been trained with, tending toward its average value. CONCLUSION MF mostly performs better compared to the faster DL approach. Potential intrinsic biases on training sets are dangerous in a clinical context that requires the algorithm to be unbiased to outliers (i.e., pathological data). Active learning and ensemble of networks are good strategies to improve prediction performances. However, data quality (sufficient SNR) has proven as a bottleneck for adequate unbiased performance-like in the case of MF

Quantification of NAD+ in human brain with 1 H MR spectroscopy at 3 T: Comparison of three localization techniques with different handling of water magnetization.

PURPOSE The detection of nicotinamide-adenine-dinucleotide (NAD+ ) is challenging using standard 1 H MR spectroscopy, because it is of low concentration and affected by polarization-exchange with water. Therefore, this study compares three techniques to access NAD+ quantification at 3 T-one with and two without water presaturation. METHODS A large brain volume in 10 healthy subjects was investigated with three techniques: semi-LASER with water-saturation (WS) (TE = 35 ms), semi-LASER with metabolite-cycling (MC) (TE = 35 ms), and the non-water-excitation (nWE) technique 2D ISIS-localization with chemical-shift-selective excitation (2D I-CSE) (TE = 10.2 ms). Spectra were quantified with optimized modeling in FiTAID. RESULTS NAD+ could be well quantified in cohort-average spectra with all techniques. Obtained apparent NAD+ tissue contents are all lower than expected from literature confirming restricted visibility by 1 H MRS. The estimated value from WS-MRS (58 μM) was considerably lower than those obtained with non-WS techniques (146 μM for MC-semi-LASER and 125 μM for 2D I-CSE). The nWE technique with shortest TE gave largest NAD+ signals but suffered from overlap with large amide signals. MC-semi-LASER yielded best estimation precision as reflected in relative Cramer-Rao bounds (14%, 21 μM/146 μM) and also best robustness as judged by the coefficient-of-variance over the cohort (11%, 10 μM/146 μM). The MR-visibility turned out as 16% with WS and 41% with MC. CONCLUSION Three methods to assess NAD+ in human brain at 3 T have been compared. NAD+ could be detected with a visibility of ∼41% for the MC method. This may open a new window for the observation of pathological changes in the clinical research setting

Non-water-excitation MR spectroscopy techniques to explore exchanging protons in human brain at 3 T.

PURPOSE To develop localization sequences for in vivo MR spectroscopy (MRS) on clinical scanners of 3 T to record spectra that are not influenced by magnetization transfer from water. METHODS Image-selected in vivo spectroscopy (ISIS) localization and chemical-shift-selective excitation (termed I-CSE) was combined in two ways: first, full ISIS localization plus a frequency-selective spin-echo and second, two-dimensional (2D) ISIS plus a frequency-selective excitation and slice-selective refocusing. The techniques were evaluated at 3 T in phantoms and human subjects in comparison to standard techniques with water presaturation or metabolite-cycling. ISIS included gradient-modulated offset-independent adiabatic (GOIA)-type adiabatic inversion pulses; echo times were 8-10 ms. RESULTS The novel 2D and 3D I-CSE methods yield upfield spectra that are comparable to those from standard MRS, except for shorter echo times and a limited frequency range. On the downfield/high-frequency side, they yield much more signal for exchangeable protons when compared to MRS with water presaturation or metabolite-cycling and longer echo times. CONCLUSION Novel non-water-excitation MRS sequences offer substantial benefits for the detection of metabolite signals that are otherwise suppressed by saturation transfer from water. Avoiding water saturation and using very short echo times allows direct observation of faster exchanging moieties than was previously possible at 3 T and additionally makes the methods less susceptible to fast T2 relaxation

Reliability of Quantification Estimates in MR Spectroscopy: CNNs vs Traditional Model Fitting

Magnetic Resonance Spectroscopy (MRS) and Spectroscopic Imaging (MRSI) are non-invasive techniques to map tissue contents of many metabolites in situ in humans. Quantification is traditionally done via model fitting (MF), and Cramer Rao Lower Bounds (CRLBs) are used as a measure of fitting uncertainties. Signal-to-noise is limited due to clinical time constraints and MF can be very time-consuming in MRSI with thousands of spectra. Deep Learning (DL) has introduced the possibility to speed up quantitation while reportedly preserving accuracy and precision. However, questions arise about how to access quantification uncertainties in the case of DL. In this work, an optimal-performance DL architecture that uses spectrograms as input and maps absolute concentrations of metabolites referenced to water content as output was taken to investigate this in detail. Distributions of predictions and Monte-Carlo dropout were used to investigate data and model-related uncertainties, exploiting ground truth knowledge in a synthetic setup mimicking realistic brain spectra with metabolic composition that uniformly varies from healthy to pathological cases. Bias and CRLBs from MF are then compared to DL-related uncertainties. It is confirmed that DL is a dataset-biased technique where accuracy and precision of predictions scale with metabolite SNR but hint towards bias and increased uncertainty at the edges of the explored parameter space (i.e., for very high and very low concentrations), even at infinite SNR (noiseless training and testing). Moreover, training with uniform datasets or if augmented with critical cases showed to be insufficient to prevent biases. This is dangerous in a clinical context that requires the algorithm to be unbiased also for concentrations far from the norm, which may well be the focus of the investigation since these correspond to pathology, the target of the diagnostic investigatio

Analysis of Loss Signatures of Unidentified Falling Objects in the LHC

Particulates in the LHC beam pipes can interact with the proton beams and cause significant beam losses. The "UFOs" (unidentified falling objects) hypothesis describes a particle falling into the beam, creating particle showers, being ionized and repelled. Though the signals of the beam loss monitors support this, many aspects remain unknown. Neither the source of the dust nor the release mechanism from the beam pipe are understood. The same holds for the forces involved in the interaction and the observed UFO rate reduction over the years. These open questions are approached from different angles. Firstly, a new data analysis tool was established featuring advanced raw data selection and statistical analysis. Results of this analysis will be presented. Secondly, dust samples were extracted from LHC components and analyzed to gain insight into the size distribution and material composition of the contamination. The performed observations and analysis lead to a better modelling of the UFO events and helped to understand the physics involved. The validated UFO models will be crucial in view of the high luminosity upgrade of the LHC (HL-LHC) and the Future Circular Collider (FCC).Macroparticles in the LHC beam pipes can interact with the proton beams and cause significant beam losses. The "UFO" (Unidentified Falling Objects) hypothesis describes a macroparticle falling into the beam, creating particle show- ers, being ionized and repelled. Though the signals of the beam loss monitors support this, many aspects remain un- known. Neither the source of the dust nor the release mech- anism from the beam pipe are understood. The same holds for the forces involved in the interaction and the observed UFO rate reduction over the years. These open questions are approached from different angles. Firstly, a new data analysis tool was established which allowed advanced stud- ies of the post-mortem data. Secondly, dust samples were extracted from LHC components and are being analyzed to gain insight into the size distribution and material compo- sition of the contamination. The results from direct LHC observations lead to a better modeling of the UFO events and question the initial UFO model. Updated and validated UFO models will be crucial in view of the high luminosity project of the LHC and the Future Circular Collider

MD#2889: 16L2 Event Dynamics and UFO Nature Investigation

Micrometer sized particles (UFOs) entering the beam are a known cause of localized beam losses since the beginning of high intensity beam operation, however the origin of these particles is not fully known. Furthermore, during 2017 a new type of UFO events appeared around the 16L2 interconnection in the LHC, leading to beam instabilities resulting in major impact on beam availability. In MD#2036 a proof-of-concept method utilizing blown-up bunches and a fast loss detection system for studying the dynamics of UFOs, simulated by the wirescanners, was successfully attempted. In this MD it was shown that real UFO dynamics are possible to study employing this method. The MD was done in parallel with MD#2934 and the procedure was adjusted for both MDs to profit as much as possible. In the end a single 16L2 induced beam dump occurred, but several conclusions about the UFO dynamics can already be drawn