Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements.

Purpose: To compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. Method: The ME, TDM, and the reference single-echo (SE) sequences with six echo times (TE) were implemented using Pulseq with single-band (SB-) and multi-band 2 (MB2-) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized root mean squared error (NRMSE). For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD). Results: TDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4× larger NRMSEs for phantom, 2× for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the map and on average 3.5× larger bias in the covariance between relaxation-diffusion coefficients. Conclusion: Our analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI.

PURPOSE: To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform. METHODS: We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). RESULTS: Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences. CONCLUSION: The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.

PURPOSE: Optimizing three-dimensional (3D) k-space sampling trajectories is important for efficient MRI yet presents a challenging computational problem. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. METHODS: We built a differentiable simulation model to enable gradient-based methods for sampling trajectory optimization. The algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method can optimize sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. The corresponding code is available as an open-source toolbox. RESULTS: We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn-Cartesian samPling trajectorY (SNOPY) optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS-GR). Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from "strong" to "mild." CONCLUSION: SNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.

Off-resonance artifact correction for MRI: A review.

In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application. Off-resonance artifacts, such as signal loss, geometric distortions, and blurring, can compromise the clinical and scientific utility of MR images. In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance. Given recent advances and the great potential of low-field and/or portable MRI, we also highlight the advantages and challenges of imaging at low field with respect to off-resonance.

Open-source MR imaging and reconstruction workflow.

PURPOSE: This work presents an end-to-end open-source MR imaging workflow. It is highly flexible in rapid prototyping across the whole imaging process and integrates vendor-independent openly available tools. The whole workflow can be shared and executed on different MR platforms. It is also integrated in the JEMRIS simulation framework, which makes it possible to generate simulated data from the same sequence that runs on the MRI scanner using the same pipeline for image reconstruction. METHODS: MRI sequences can be designed in Python or JEMRIS using the Pulseq framework, allowing simplified integration of new sequence design tools. During the sequence design process, acquisition metadata required for reconstruction is stored in the MR raw data format. Data acquisition is possible on MRI scanners supported by Pulseq and in simulations through JEMRIS. An image reconstruction and postprocessing pipeline was implemented into a Python server that allows real-time processing of data as it is being acquired. The Berkeley Advanced Reconstruction Toolbox is integrated into this framework for image reconstruction. The reconstruction pipeline supports online integration through a vendor-dependent interface. RESULTS: The flexibility of the workflow is demonstrated with different examples, containing 3D parallel imaging with controlled aliasing in volumetric parallel imaging (CAIPIRINHA) acceleration, spiral imaging, and B0 mapping. All sequences, data, and the corresponding processing pipelines are publicly available. CONCLUSION: The proposed workflow is highly flexible and allows integration of advanced tools at all stages of the imaging process. All parts of this workflow are open-source, simplifying collaboration across different MR platforms or sites and improving reproducibility of results.

B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI.

Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and apply multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator to accurately reconstruct and back-propagate multi-coil non-Cartesian data. Penalties on slew rate and gradient amplitude enforce hardware constraints. Sampling and reconstruction are trained jointly using large public datasets. To correct for possible eddy-current effects introduced by the curved trajectory, we use a pencil-beam trajectory mapping technique. In both simulations and in- vivo experiments, the learned trajectory demonstrates significantly improved image quality compared to previous model-based and learning-based trajectory optimization methods for 10× acceleration factors. Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.

Joint Design of RF and Gradient Waveforms via Auto-differentiation for 3D Tailored Excitation in MRI.

This paper proposes a new method for joint design of radiofrequency (RF) and gradient waveforms in Magnetic Resonance Imaging (MRI), and applies it to the design of 3D spatially tailored saturation and inversion pulses. The joint design of both waveforms is characterized by the ODE Bloch equations, to which there is no known direct solution. Existing approaches therefore typically rely on simplified problem formulations based on, e.g., the small-tip approximation or constraining the gradient waveforms to particular shapes, and often apply only to specific objective functions for a narrow set of design goals (e.g., ignoring hardware constraints). This paper develops and exploits an auto-differentiable Bloch simulator to directly compute Jacobians of the (Bloch-simulated) excitation pattern with respect to RF and gradient waveforms. This approach is compatible with arbitrary sub-differentiable loss functions, and optimizes the RF and gradients directly without restricting the waveform shapes. For computational efficiency, we derive and implement explicit Bloch simulator Jacobians (approximately halving computation time and memory usage). To enforce hardware limits (peak RF, gradient, and slew rate), we use a change of variables that makes the 3D pulse design problem effectively unconstrained; we then optimize the resulting problem directly using the proposed auto-differentiation framework. We demonstrate our approach with two kinds of 3D excitation pulses that cannot be easily designed with conventional approaches: Outer-volume saturation (90° flip angle), and inner-volume inversion.

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 > 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.

Myelin water fraction estimation using small-tip fast recovery MRI.

PURPOSE: To demonstrate the feasibility of an optimized set of small-tip fast recovery (STFR) MRI scans for rapidly estimating myelin water fraction (MWF) in the brain. METHODS: We optimized a set of STFR scans to minimize the Cramér-Rao Lower Bound of MWF estimates. We evaluated the RMSE of MWF estimates from the optimized scans in simulation. We compared STFR-based MWF estimates (both modeling exchange and not modeling exchange) to multi-echo spin echo (MESE)-based estimates. We used the optimized scans to acquire in vivo data from which a MWF map was estimated. We computed the STFR-based MWF estimates using PERK, a recently developed kernel regression technique, and the MESE-based MWF estimates using both regularized non-negative least squares (NNLS) and PERK. RESULTS: In simulation, the optimized STFR scans led to estimates of MWF with low RMSE across a range of tissue parameters and across white matter and gray matter. The STFR-based MWF estimates that modeled exchange compared well to MESE-based MWF estimates in simulation. When the optimized scans were tested in vivo, the MWF map that was estimated using a 3-compartment model with exchange was closer to the MESE-based MWF map. CONCLUSIONS: The optimized STFR scans appear to be well suited for estimating MWF in simulation and in vivo when we model exchange in training. In this case, the STFR-based MWF estimates are close to the MESE-based estimates.

Practical considerations for territorial perfusion mapping in the cerebral circulation using super-selective pseudo-continuous arterial spin labeling.

PURPOSE: This paper discusses several challenges faced by super-selective pseudo-continuous arterial spin labeling, which is used to quantify territorial perfusion in the cerebral circulation. The effects of off-resonance, pulsatility, vessel movement, and label rotation scheme are investigated, and methods to maximize labeling efficiency and overall image quality are evaluated. A strategy to calculate the territorial perfusion fractions of individual vessels is proposed. METHODS: The effects of off-resonance, label rotation scheme, and vessel movement on labeling efficiency were simulated. Two off-resonance compensation strategies (multiphase prescan, field map), cardiac triggering, and vessel movement were studied in vivo in a group of 10 subjects. Subsequently, a territorial perfusion fraction map was acquired in 2 subjects based on the mean vessel labeling efficiency. RESULTS: Multiphase calibration provided the highest labeling efficiency (P = .002) followed by the field map compensation (P = .037) compared with the uncompensated acquisition. Cardiac triggering resulted in a qualitative improvement of the image and an increase in signal contrast between the perfusion territory and the surrounding tissue (P = .010) but failed to show a significant change in temporal and spatial SNR. The constant clockwise label rotation scheme yielded the highest labeling efficiency. Significant vessel movement (>2 mm according to simulations) was observed in 50% of subjects. The measured territorial perfusion fractions showed good agreement with anatomical data. CONCLUSION: Optimized labeling efficiency resulted in increased image quality and accuracy of territorial perfusion fraction maps. Labeling efficiency depends critically on off-resonance calibration, cardiac triggering, optimal label rotation scheme, and vessel location tracking.

A GRAPPA algorithm for arbitrary 2D/3D non-Cartesian sampling trajectories with rapid calibration.

PURPOSE: GRAPPA is a popular reconstruction method for Cartesian parallel imaging, but is not easily extended to non-Cartesian sampling. We introduce a general and practical GRAPPA algorithm for arbitrary non-Cartesian imaging. METHODS: We formulate a general GRAPPA reconstruction by associating a unique kernel with each unsampled k-space location with a distinct constellation, that is, local sampling pattern. We calibrate these generalized kernels using the Fourier transform phase shift property applied to fully gridded or separately acquired Cartesian Autocalibration signal (ACS) data. To handle the resulting large number of different kernels, we introduce a fast calibration algorithm based on nonuniform FFT (NUFFT) and adoption of circulant ACS boundary conditions. We applied our method to retrospectively under-sampled rotated stack-of-stars/spirals in vivo datasets, and to a prospectively under-sampled rotated stack-of-spirals functional MRI acquisition with a finger-tapping task. RESULTS: We reconstructed all datasets without performing any trajectory-specific manual adaptation of the method. For the retrospectively under-sampled experiments, our method achieved image quality (i.e., error and g-factor maps) comparable to conjugate gradient SENSE (cg-SENSE) and SPIRiT. Functional activation maps obtained from our method were in good agreement with those obtained using cg-SENSE, but required a shorter total reconstruction time (for the whole time-series): 3 minutes (proposed) vs 15 minutes (cg-SENSE). CONCLUSIONS: This paper introduces a general 3D non-Cartesian GRAPPA that is fast enough for practical use on today's computers. It is a direct generalization of original GRAPPA to non-Cartesian scenarios. The method should be particularly useful in dynamic imaging where a large number of frames are reconstructed from a single set of ACS data.

Improved sensitivity and temporal resolution in perfusion FMRI using velocity selective inversion ASL.

PURPOSE: This work aims to investigate the utility of velocity selective inversion pulses for perfusion weighted functional MRI. METHODS: Tracer kinetic properties of velocity selective inversion (VSI) pulses as an input function for an arterial spin labeling (ASL) experiment were characterized in a group of healthy participants. Numerical simulations were conducted to search for a robust set of timing parameters for FMRI time series acquisition with maximal signal to noise ratio efficiency. The performance of three VSI pulse sequences with different timing parameters was compared with a pseudocontinuous ASL sequence in a simple FMRI experiment conducted on healthy participants. RESULTS: The fit to the tracer kinetic model yielded arterial CBV of 1.24% ± 0.52% and 0.45 ± 0.11% and perfusion rates of 60.8 ± 32.3 and 34.4 ± 5.4 mL/min/100 g for gray and white matter, respectively. Bolus arrival times were estimated as 75.7 ± 21 ms and 349 ± 78 ms for gray and white matter, respectively. The FMRI experiments showed that VSI pulses yield comparable sensitivity to PCASL with similar timing parameters (TR = 4 s). However, VSI pulses could be used at a faster acquisition speed (TR = 3 s) and were more sensitive to neuronal activity than PCASL pulses, as evidenced by the 31% higher Z scores obtained on average in the active regions. CONCLUSION: VSI pulses can be very beneficial for perfusion weighted functional MRI because of their tracer kinetic characteristics, which allow a faster acquisition rate while maintaining an efficient labeling input function.

Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.

This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these parameter-measurement pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. PERK admits a simple implementation as per-voxel nonlinear lifting of MRI measurements followed by linear minimum mean-squared error regression. We demonstrate PERK for $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimation, a well-studied application where it is simple to compare PERK estimates against dictionary-based grid search estimates and iterative optimization estimates. Numerical simulations as well as single-slice phantom and in vivo experiments demonstrate that PERK and other tested methods produce comparable $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimates in white and gray matter, but PERK is consistently at least $140\times $ faster. This acceleration factor may increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel.

Pulseq-Graphical Programming Interface: Open source visual environment for prototyping pulse sequences and integrated magnetic resonance imaging algorithm development.

PURPOSE: To provide a single open-source platform for comprehensive MR algorithm development inclusive of simulations, pulse sequence design and deployment, reconstruction, and image analysis. METHODS: We integrated the "Pulseq" platform for vendor-independent pulse programming with Graphical Programming Interface (GPI), a scientific development environment based on Python. Our integrated platform, Pulseq-GPI, permits sequences to be defined visually and exported to the Pulseq file format for execution on an MR scanner. For comparison, Pulseq files using either MATLAB only ("MATLAB-Pulseq") or Python only ("Python-Pulseq") were generated. We demonstrated three fundamental sequences on a 1.5 T scanner. Execution times of the three variants of implementation were compared on two operating systems. RESULTS: In vitro phantom images indicate equivalence with the vendor supplied implementations and MATLAB-Pulseq. The examples demonstrated in this work illustrate the unifying capability of Pulseq-GPI. The execution times of all the three implementations were fast (a few seconds). The software is capable of user-interface based development and/or command line programming. CONCLUSION: The tool demonstrated here, Pulseq-GPI, integrates the open-source simulation, reconstruction and analysis capabilities of GPI Lab with the pulse sequence design and deployment features of Pulseq. Current and future work includes providing an ISMRMRD interface and incorporating Specific Absorption Ratio and Peripheral Nerve Stimulation computations.

TOPPE: A framework for rapid prototyping of MR pulse sequences.

PURPOSE: To introduce a framework for rapid prototyping of MR pulse sequences. METHODS: We propose a simple file format, called "TOPPE", for specifying all details of an MR imaging experiment, such as gradient and radiofrequency waveforms and the complete scan loop. In addition, we provide a TOPPE file "interpreter" for GE scanners, which is a binary executable that loads TOPPE files and executes the sequence on the scanner. We also provide MATLAB scripts for reading and writing TOPPE files and previewing the sequence prior to hardware execution. With this setup, the task of the pulse sequence programmer is reduced to creating TOPPE files, eliminating the need for hardware-specific programming. No sequence-specific compilation is necessary; the interpreter only needs to be compiled once (for every scanner software upgrade). We demonstrate TOPPE in three different applications: k-space mapping, non-Cartesian PRESTO whole-brain dynamic imaging, and myelin mapping in the brain using inhomogeneous magnetization transfer. RESULTS: We successfully implemented and executed the three example sequences. By simply changing the various TOPPE sequence files, a single binary executable (interpreter) was used to execute several different sequences. CONCLUSION: The TOPPE file format is a complete specification of an MR imaging experiment, based on arbitrary sequences of a (typically small) number of unique modules. Along with the GE interpreter, TOPPE comprises a modular and flexible platform for rapid prototyping of new pulse sequences. Magn Reson Med 79:3128-3134, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Design of spectral-spatial phase prewinding pulses and their use in small-tip fast recovery steady-state imaging.

PURPOSE: Spectrally selective "prewinding" radiofrequency pulses can counteract B0 inhomogeneity in steady-state sequences, but can only prephase a limited range of off-resonance. We propose spectral-spatial small-tip angle prewinding pulses that increase the off-resonance bandwidth that can be successfully prephased by incorporating spatially tailored excitation patterns. THEORY AND METHODS: We present a feasibility study to compare spectral and spectral-spatial prewinding pulses. These pulses add a prephasing term to the target magnetization pattern that aims to recover an assigned off-resonance bandwidth at the echo time. For spectral-spatial pulses, the design bandwidth is centered at the off-resonance frequency for each spatial location in a field map. We use these pulses in the small-tip fast recovery steady-state sequence, which is similar to balanced steady-state free precession. We investigate improvement of spectral-spatial pulses over spectral pulses using simulations and small-tip fast recovery scans of a gel phantom and human brain. RESULTS: In simulation, spectral-spatial pulses yielded lower normalized root mean squared excitation error than spectral pulses. For both experiments, the spectral-spatial pulse images are also qualitatively better (more uniform, less signal loss) than the spectral pulse images. CONCLUSION: Spectral-spatial prewinding pulses can prephase over a larger range of off-resonance than their purely spectral counterparts. Magn Reson Med 79:1377-1386, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Molecular, dynamic, and structural origin of inhomogeneous magnetization transfer in lipid membranes.

PURPOSE: To elucidate the dynamic, structural, and molecular properties that create inhomogeneous magnetization transfer (ihMT) contrast. METHODS: Amphiphilic lipids, lamellar phospholipids with cholesterol, and bovine spinal cord (BSC) specimens were examined along with nonlipid systems. Magnetization transfer (MT), enhanced MT (eMT, obtained with double-sided radiofrequency saturation), ihMT (MT - eMT), and dipolar relaxation, T1D , were measured at 2.0 and 11.7 T. RESULTS: The amplitude of ihMT ratio (ihMTR) is positively correlated with T1D values. Both ihMTR and T1D increase with increasing temperature in BSC white matter and in phospholipids and decrease with temperature in other lipids. Changes in ihMTR with temperature arise primarily from alterations in MT rather than eMT. Spectral width of MT, eMT, and ihMT increases with increasing carbon chain length. CONCLUSIONS: Concerted motions of phospholipids in white matter decrease proton spin diffusion leading to increased proton T1D times and increased ihMT amplitudes, consistent with decoupling of Zeeman and dipolar spin reservoirs. Molecular specificity and dynamic sensitivity of ihMT contrast make it a suitable candidate for probing myelin membrane disorders. Magn Reson Med 77:1318-1328, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Optimizing MR Scan Design for Model-Based ${T}_{1}$ , ${T}_{2}$ Estimation From Steady-State Sequences.

Rapid, reliable quantification of MR relaxation parameters T1 and T2 is desirable for many clinical applications. Steady-state sequences such as Spoiled Gradient-Recalled Echo (SPGR) and Dual-Echo Steady-State (DESS) are fast and well-suited for relaxometry because the signals they produce are quite sensitive to T1 and T2 variation. However, T1, T2 estimation with these sequences typically requires multiple scans with varied sets of acquisition parameters. This paper describes a systematic framework for selecting scan types (e.g., combinations of SPGR and DESS scans) and optimizing their respective parameters (e.g., flip angles and repetition times). The method is based on a Cramér-Rao Bound (CRB)-inspired min-max optimization that finds scan parameter combinations that robustly enable precise object parameter estimation. We apply this technique to optimize combinations of SPGR and DESS scans for T1, T2 relaxometry in white matter (WM) and grey matter (GM) regions of the human brain at 3T field strength. Phantom accuracy experiments show that SPGR/DESS scan combinations are in excellent agreement with reference measurements. Phantom precision experiments show that trends in T1,T2 pooled sample standard deviations reflect CRB-based predictions. In vivo experiments show that in WM and GM, T1 and T2 estimates from a pair of optimized DESS scans exhibit precision (but not necessarily accuracy) comparable to that of optimized combinations of SPGR and DESS scans. To our knowledge, T1 maps from DESS acquisitions alone are new. This example application illustrates that scan optimization may help reveal new parameter mapping techniques from combinations of established pulse sequences.

Pulseq: A rapid and hardware-independent pulse sequence prototyping framework.

PURPOSE: Implementing new magnetic resonance experiments, or sequences, often involves extensive programming on vendor-specific platforms, which can be time consuming and costly. This situation is exacerbated when research sequences need to be implemented on several platforms simultaneously, for example, at different field strengths. This work presents an alternative programming environment that is hardware-independent, open-source, and promotes rapid sequence prototyping. METHODS: A novel file format is described to efficiently store the hardware events and timing information required for an MR pulse sequence. Platform-dependent interpreter modules convert the file to appropriate instructions to run the sequence on MR hardware. Sequences can be designed in high-level languages, such as MATLAB, or with a graphical interface. Spin physics simulation tools are incorporated into the framework, allowing for comparison between real and virtual experiments. RESULTS: Minimal effort is required to implement relatively advanced sequences using the tools provided. Sequences are executed on three different MR platforms, demonstrating the flexibility of the approach. CONCLUSION: A high-level, flexible and hardware-independent approach to sequence programming is ideal for the rapid development of new sequences. The framework is currently not suitable for large patient studies or routine scanning although this would be possible with deeper integration into existing workflows. Magn Reson Med 77:1544-1552, 2017. © 2016 International Society for Magnetic Resonance in Medicine.