Accelerated parallel magnetic resonance imaging with compressed sensing using structured sparsity.

Purpose: We present a method that combines compressed sensing with parallel imaging that takes advantage of the structure of the sparsifying transformation. Approach: Previous work has combined compressed sensing with parallel imaging using model-based reconstruction but without taking advantage of the structured sparsity. Blurry images for each coil are reconstructed from the fully sampled center region. The optimization problem of compressed sensing is modified to take these blurry images into account, and it is solved to estimate the missing details. Results: Using data of brain, ankle, and shoulder anatomies, the combination of compressed sensing with structured sparsity and parallel imaging reconstructs an image with a lower relative error than does sparse SENSE or L1 ESPIRiT, which do not use structured sparsity. Conclusions: Taking advantage of structured sparsity improves the image quality for a given amount of data as long as a fully sampled region centered on the zero frequency of the appropriate size is acquired.

A pharmacokinetic model for hyperpolarized 13C-pyruvate MRI when using metabolite-specific bSSFP sequences.

PURPOSE: Metabolite-specific balanced SSFP (MS-bSSFP) sequences are increasingly used in hyperpolarized [1-13C]Pyruvate (HP 13C) MRI studies as they improve SNR by refocusing the magnetization each TR. Currently, pharmacokinetic models used to fit conversion rate constants, kPL and kPB, and rate constant maps do not account for differences in the signal evolution of MS-bSSFP acquisitions. METHODS: In this work, a flexible MS-bSSFP model was built that can be used to fit conversion rate constants for these experiments. The model was validated in vivo using paired animal (healthy rat kidneys n = 8, transgenic adenocarcinoma of the mouse prostate n = 3) and human renal cell carcinoma (n = 3) datasets. Gradient echo (GRE) acquisitions were used with a previous GRE model to compare to the results of the proposed GRE-bSSFP model. RESULTS: Within simulations, the proposed GRE-bSSFP model fits the simulated data well, whereas a GRE model shows bias because of model mismatch. For the in vivo datasets, the estimated conversion rate constants using the proposed GRE-bSSFP model are consistent with a previous GRE model. Jointly fitting the lactate T2 with kPL resulted in less precise kPL estimates. CONCLUSION: The proposed GRE-bSSFP model provides a method to estimate conversion rate constants, kPL and kPB, for MS-bSSFP HP 13C experiments. This model may also be modified and used for other applications, for example, estimating rate constants with other hyperpolarized reagents or multi-echo bSSFP.

Quantification of the in vivo brain ultrashort-T2* component in healthy volunteers.

PURPOSE: Recent work has shown MRI is able to measure and quantify signals of phospholipid membrane-bound protons associated with myelin in the human brain. This work seeks to develop an improved technique for characterizing this brain ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component in vivo accounting for T 1 $$ {\mathrm{T}}_1 $$ weighting. METHODS: Data from ultrashort echo time scans from 16 healthy volunteers with variable flip angles (VFA) were collected and fitted into an advanced regression model to quantify signal fraction, relaxation time, and frequency shift of the ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component. RESULTS: The fitted components show intra-subject differences of different white matter structures and significantly elevated ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ signal fraction in the corticospinal tracts measured at 0.09 versus 0.06 in other white matter structures and significantly elevated ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ frequency shift in the body of the corpus callosum at - $$ - $$ 1.5 versus - $$ - $$ 2.0 ppm in other white matter structures. CONCLUSION: The significantly different measured components and measured T 1 $$ {\mathrm{T}}_1 $$ relaxation time of the ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component suggest that this method is picking up novel signals from phospholipid membrane-bound protons.