Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy

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.

Section: Introductionmentioning

confidence: 99%

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

Rizzo

Dziadosz

Kyathanahally

et al. 2022

“…Other deep learning applications in MRS include spectral reconstruction, denoising, artifact removal, and frequency and phase corrections. [25][26][27][28] Here we propose a novel neural network architecture to directly predict metabolite concentrations without using spectral fitting. We treat quantification of metabolites as a multiclass regression problem that can be effectively solved by deep learning.…”

Section: Introductionmentioning

confidence: 99%

Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting

Zhang

Shen

2023

Purpose To propose a novel end‐to‐end deep learning model to quantify absolute metabolite concentrations from in vivo J‐point resolved spectroscopy (JPRESS) without using spectral fitting. Methods A novel encoder‐decoder‐style neural network was created, which was trained to predict metabolite concentrations and individual component signals concurrently from 3T JPRESS data in the time domain. The training data set contained 100 000 samples created by spin‐density simulations using experimentally used RF pulses. Concentrations, phase, frequencies, linewidths, and T2 relaxation times in the training data set were varied over a large range with uniform distributions. Random synthesized noise and extraneous signals were added to the data set. Two thousand validation samples were created similarly to the training data set but with mean concentrations close to in vivo values. An in vivo test was conducted with 20 samples acquired from the human brain. Results Both validation and in vivo test results showed that the proposed model successfully predicted metabolite concentrations as well as individual metabolite signals without involving spectral fitting, while extraneous peaks or unregistered signals were filtered out. Compared with the short‐TE spectral fitting by LCModel, the proposed method had the advantage that the undesired correlations between the estimated concentrations and noise levels and between metabolites were eliminated or substantially reduced. Conclusion The proposed method provides a working deep learning model that directly maps in vivo JPRESS data to metabolite concentrations. Because spectral fitting is not used, the trained model does not depend on the assumptions associated with parameter tuning when applied to in vivo data.

“…The recent success of deep learning (DL), one of the latest machine learning (ML) approaches, in a wide range of tasks, including the MR field, 13 , 14 suggests that it could also handle FPC. Recently, DL‐based solutions have been proposed for metabolite quantification in the frequency domain, 15 , 16 detecting and removing ghosting artifacts, 17 FID reconstruction, 18 automatic peak picking, 19 enhancement of MRSI spatial resolution, 20 and identifying and filtering out poor‐quality spectra. 21 It has been shown that DL can also be used for FPC 7 , 22 and could speed up FPC once it has been successfully trained.…”

Section: Introductionmentioning

confidence: 99%

Model‐informed unsupervised deep learning approaches to frequency and phase correction of MRS signals

Shamaei

Starčuková

Pavlova

et al. 2022

Purpose A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised deep learning–based FPC. Methods Two novel deep learning–based FPC methods (deep learning–based Cr referencing and deep learning–based spectral registration), which use a priori physics domain knowledge, are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in vivo MEGA‐edited MRS data. The performance of our proposed FPC methods was compared with other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo study was carried out to investigate the performance of our proposed methods. Results The validation using low‐SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably with other methods. The evaluation showed that the deep learning–based spectral registration over a limited frequency range method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA‐edited in vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. Conclusions The proposed physics‐informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of large MRS data in a shorter time.