A convolutional neural network to filter artifacts in spectroscopic MRI

Purpose Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. Methods A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency‐domain spectra to detect artifacts. Results When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single‐voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole‐brain spectroscopic MRI volumes in real time. Conclusion The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning

In vivo magnetic resonance spectroscopy: basic methodology and clinical applications

The clinical use of in vivo magnetic resonance spectroscopy (MRS) has been limited for a long time, mainly due to its low sensitivity. However, with the advent of clinical MR systems with higher magnetic field strengths such as 3 Tesla, the development of better coils, and the design of optimized radio-frequency pulses, sensitivity has been considerably improved. Therefore, in vivo MRS has become a technique that is routinely used more and more in the clinic. In this review, the basic methodology of in vivo MRS is described—mainly focused on 1H MRS of the brain—with attention to hardware requirements, patient safety, acquisition methods, data post-processing, and quantification. Furthermore, examples of clinical applications of in vivo brain MRS in two interesting fields are described. First, together with a description of the major resonances present in brain MR spectra, several examples are presented of deviations from the normal spectral pattern associated with inborn errors of metabolism. Second, through examples of MR spectra of brain tumors, it is shown that MRS can play an important role in oncology

Across‐vendor standardization of semi‐LASER for single‐voxel MRS at 3T

The semi‐adiabatic localization by adiabatic selective refocusing (sLASER) sequence provides single‐shot full intensity signal with clean localization and minimal chemical shift displacement error and was recommended by the international MRS Consensus Group as the preferred localization sequence at high‐ and ultra‐high fields. Across‐vendor standardization of the sLASER sequence at 3 tesla has been challenging due to the B1 requirements of the adiabatic inversion pulses and maximum B1 limitations on some platforms. The aims of this study were to design a short‐echo sLASER sequence that can be executed within a B1 limit of 15 μT by taking advantage of gradient‐modulated RF pulses, to implement it on three major platforms and to evaluate the between‐vendor reproducibility of its perfomance with phantoms and in vivo. In addition, voxel‐based first and second order B0 shimming and voxel‐based B1 adjustments of RF pulses were implemented on all platforms. Amongst the gradient‐modulated pulses considered (GOIA, FOCI and BASSI), GOIA‐WURST was identified as the optimal refocusing pulse that provides good voxel selection within a maximum B1 of 15 μT based on localization efficiency, contamination error and ripple artifacts of the inversion profile. An sLASER sequence (30 ms echo time) that incorporates VAPOR water suppression and 3D outer volume suppression was implemented with identical parameters (RF pulse type and duration, spoiler gradients and inter‐pulse delays) on GE, Philips and Siemens and generated identical spectra on the GE ‘Braino’ phantom between vendors. High‐quality spectra were consistently obtained in multiple regions (cerebellar white matter, hippocampus, pons, posterior cingulate cortex and putamen) in the human brain across vendors (5 subjects scanned per vendor per region; mean signal‐to‐noise ratio [less than] 33; mean water linewidth between 6.5 Hz to 11.4 Hz). The harmonized sLASER protocol is expected to produce high reproducibility of MRS across sites thereby allowing large multi‐site studies with clinical cohorts

A novel method to measure T-1-relaxation times of macromolecules and quantification of the macromolecular resonances

Purpose: Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T1 relaxation times among individual MM peaks must be considered. Characterization of T1 -relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T1 -relaxation times of individual MM peaks is reported in this work. Methods: Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR-metabolite-cycled semi-LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T1 -relaxation times. Results: T1 -relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray- and white-matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported. Conclusion: A novel DIR method is reported in this work to calculate T1 -relaxation times of MMs in the human brain. T1 -relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray- and white-matter rich voxels for the first time at 9.4T

T2 relaxation times of macromolecules and metabolites in the human brain at 9.4 T

PURPOSE: Relaxation times can contribute to spectral assignment. In this study, effective T2 relaxation times ( Teff2 ) of macromolecules are reported for gray and white matter-rich voxels in the human brain at 9.4 T. The Teff2 of macromolecules are helpful to understand their behavior and the effect they have on metabolite quantification. Additionally, for absolute quantification of metabolites with magnetic resonance spectroscopy, appropriate T2 values of metabolites must be considered. The T2 relaxation times of metabolites are calculated after accounting for TE/sequence-specific macromolecular baselines. METHODS: Macromolecular and metabolite spectra for a series of TEs were acquired at 9.4 T using double inversion-recovery metabolite-cycled semi-LASER and metabolite-cycled semi-LASER, respectively. The T2 relaxation times were calculated by fitting the LCModel relative amplitudes of macromolecular peaks and metabolites to a mono-exponential decay across the TE series. Furthermore, absolute concentrations of metabolites were calculated using the estimated relaxation times and internal water as reference. RESULTS: The Teff2 of macromolecules are reported, which range from 13 ms to 40 ms, whereas, for metabolites, they range from 40 ms to 110 ms. Both macromolecular and metabolite T2 relaxation times are observed to follow the decreasing trend, with increasing B0 . The linewidths of metabolite singlets can be fully attributed to T2 and B0 components. However, in addition to these components, macromolecule linewidths have contributions from J-coupling and overlapping resonances. CONCLUSION: The T2 relaxation times of all macromolecular and metabolite peaks at 9.4 T in vivo are reported for the first time. Metabolite relaxation times were used to calculate the absolute metabolite concentrations

NIfTI-MRS: A standard data format for magnetic resonance spectroscopy

Purpose Multiple data formats in the MRS community currently hinder data sharing and integration. NIfTI-MRS is proposed as a standard spectroscopy data format, implemented as an extension to the Neuroimaging informatics technology initiative (NIfTI) format. This standardized format can facilitate data sharing and algorithm development as well as ease integration of MRS analysis alongside other imaging modalities. Methods A file format using the NIfTI header extension framework incorporates essential spectroscopic metadata and additional encoding dimensions. A detailed description of the specification is provided. An open-source command-line conversion program is implemented to convert single-voxel and spectroscopic imaging data to NIfTI-MRS. Visualization of data in NIfTI-MRS is provided by development of a dedicated plugin for FSLeyes, the FMRIB Software Library (FSL) image viewer. Results Online documentation and 10 example datasets in the proposed format are provided. Code examples of NIfTI-MRS readers are implemented in common programming languages. Conversion software, spec2nii, currently converts 14 formats where data is stored in image-space to NIfTI-MRS, including Digital Imaging and Communications in Medicine (DICOM) and vendor proprietary formats. Conclusion NIfTI-MRS aims to solve issues arising from multiple data formats being used in the MRS community. Through a single conversion point, processing and analysis of MRS data are simplified, thereby lowering the barrier to use of MRS. Furthermore, it can serve as the basis for open data sharing, collaboration, and interoperability of analysis programs. Greater standardization and harmonization become possible. By aligning with the dominant format in neuroimaging, NIfTI-MRS enables the use of mature tools present in the imaging community, demonstrated in this work by using a dedicated imaging tool, FSLeyes, for visualization