Non cartesian MRI

Magnetic resonance spectroscopic imaging (MRSI) provides information about the spatial distribution of metabolites in the brain. These metabolite maps can be valuable in diagnosing central nervous system pathology. However, MRSI generally suffers from a long acquisition time, poor spatial resolution, and a low metabolite signal-to-noise ratio (SNR). Ultrahigh field strengths (≥ 7 T) can benefit MRSI with an improved SNR and allow high-resolution metabolic mapping. Non-Cartesian spatial-spectral encoding techniques, such as rosette spectroscopic imaging, can efficiently sample spatial and temporal domains, which significantly reduces the imaging time and enables high-resolution metabolic mapping in a clinically relevant scan time...

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches...

Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2C ) and non-Cartesian standard-resolution T2-weighted TSE sequence (T2NC ) between August and November 2022...

Magnetic resonance fingerprinting (MRF) can rapidly perform simultaneous imaging of multiple tissue parameters. However, the rapid acquisition schemes used in MRF inevitably introduce aliasing artifacts in the recovered tissue fingerprints, reducing the accuracy of the predicted parameter maps. Current regularized reconstruction methods are based on iterative procedures which are usually time-consuming. In addition, most of the current deep learning-based methods for MRF often lack interpretability owing to the black-box nature, and most deep learning-based methods are not applicable for non-Cartesian scenarios, which limits the practical applications...

PURPOSE: To present the validation of a new Flexible Ultra-Short Echo time (FUSE) pulse sequence using a short-T2 phantom. METHODS: FUSE was developed to include a range of RF excitation pulses, trajectories, dimensionalities, and long-T2 suppression techniques, enabling real-time interchangeability of acquisition parameters. Additionally, we developed an improved 3D deblurring algorithm to correct for off-resonance artifacts. Several experiments were conducted to validate the efficacy of FUSE, by comparing different approaches for off-resonance artifact correction, variations in RF pulse and trajectory combinations, and long-T2 suppression techniques...

BACKGROUND: Cardiac MRI has become the gold-standard imaging technique for assessing cardiovascular morphology and function. In spite of this, its slow data acquisition process presents imaging challenges due to the motion from heartbeats, respiration, and blood flow. In recent studies, deep learning (DL) algorithms have shown promising results for the task of image reconstruction. However, there have been instances where they have introduced artifacts that may be misinterpreted as pathologies or may obscure the detection of pathologies...

PURPOSE: Patient-induced inhomogeneities in the static magnetic field cause distortions and blurring (off-resonance artifacts) during acquisitions with long readouts such as in SWI. Conventional versatile correction methods based on extended Fourier models are too slow for clinical practice in computationally demanding cases such as 3D high-resolution non-Cartesian multi-coil acquisitions. THEORY: Most reconstruction methods can be accelerated when performing off-resonance correction by reducing the number of iterations, compressed coils, and correction components...

PURPOSE: To demonstrate the feasibility of high-resolution morphologic lung MRI at 0.55 T using a free-breathing balanced steady-state free precession half-radial dual-echo imaging technique (bSTAR). METHODS: Self-gated free-breathing bSTAR (TE1 /TE2 /TR of 0.13/1.93/2.14 ms) lung imaging in five healthy volunteers and a patient with granulomatous lung disease was performed using a 0.55 T MR-scanner. A wobbling Archimedean spiral pole (WASP) trajectory was used to ensure a homogenous coverage of k-space over multiple breathing cycles...

OBJECTIVE: Diagnostic-quality neuroimaging methods are vital for widespread clinical adoption of low field MRI. Spiral imaging is an efficient acquisition method that can mitigate the reduced signal-to-noise ratio at lower field strengths. As concomitant field artifacts are worse at lower field, we propose a generalizable quadratic gradient-field nulling as an echo-to-echo compensation and apply it to spiral TSE at 0.55 T. MATERIALS AND METHODS: A spiral in-out TSE acquisition was developed with a compensation for concomitant field variation between spiral interleaves, by adding bipolar gradients around each readout to minimize phase differences at each refocusing pulse...

A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed and implemented on 7 Tesla human MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method optimized for random undersampling of magnetic resonance spectroscopic imaging (MRSI) at ultra-high field. The approach provides flexible and random (k,t) sampling without temporal interleaving to improve spatial response function and spectral quality...

BACKGROUND: The assessment of resectability after neoadjuvant chemotherapy of hepatoblastoma is dependent on Post-Treatment EXTENT of Disease (POSTTEXT) staging and its annotation factors P (portal venous involvement) and V (hepatic venous/inferior vena cava [IVC] involvement), but MR performance in assessing them remains unclear. PURPOSE: To assess the diagnostic performance of contrast-enhanced MR imaging for preoperative POSTTEXT staging and diagnosing vascular involvement in terms of annotation factors P and V in pediatric hepatoblastoma following neoadjuvant chemotherapy...

MRI is a valuable diagnostic tool to investigate spinal cord (SC) pathology. SC MRI can benefit from the increased signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at ultra-high fields such as 7 T. However, SC MRI acquisitions with routine Cartesian readouts are prone to image artifacts caused by physiological motion. MRI acquisition techniques with non-Cartesian readouts such as rosette can help reduce motion artifacts. The purpose of this study was to demonstrate the feasibility of high-resolution SC imaging using rosette trajectory with magnetization transfer preparation (MT-prep) and compressed sensing (CS) at 7 T...

PURPOSE: Non-Cartesian MRI with long arbitrary readout directions are susceptible to off-resonance artifacts due to patient induced <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>B</mml:mi></mml:mrow> <mml:mrow><mml:mn>0</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {B}_0 $$</mml:annotation></mml:semantics> </mml:math> inhomogeneities...

PURPOSE: To develop an arterial spin labeling (ASL) perfusion imaging method with balanced steady-state free precession (bSSFP) readout and radial sampling for improved SNR and robustness to motion and off-resonance effects. METHODS: An ASL perfusion imaging method was developed with pseudo-continuous arterial spin labeling (pCASL), bSSFP readout, and multiple phase-cycling. Three-dimensional (3D) k-space data were collected in segmented acquisitions following a stack-of-stars sampling trajectory...

There is growing interest in learning Fourier domain sampling strategies (particularly for magnetic resonance imaging, MRI) using optimization approaches. For non-Cartesian sampling, the system models typically involve non-uniform fast Fourier transform (NUFFT) operations. Commonly used NUFFT algorithms contain frequency domain interpolation, which is not differentiable with respect to the sampling pattern, complicating the use of gradient methods. This paper describes an efficient and accurate approach for computing approximate gradients involving NUFFTs...

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...

PURPOSE: The expanded encoding model incorporates spatially- and time-varying field perturbations for correction during reconstruction. To date, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low-SNR cases like diffusion or high-resolution MRI. Here, we investigate the extent that <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn></mml:msub> </mml:mrow> <mml:annotation>$$ {\ell}_1 $$</mml:annotation></mml:semantics> </mml:math> -wavelet regularization, or equivalently compressed sensing (CS), combined with expanded encoding improves trade-offs between spatial resolution, readout time and SNR for single-shot spiral DWI at 7T...

Disorders of the lungs and airways are among the most common indications for diagnostic imaging in infants and children. Traditionally, chest radiograph has been the first-line imaging test for detecting these disorders and when cross-sectional imaging is necessary, computed tomography (CT) has typically been the next step. However, due to concerns about the potentially harmful effects of ionizing radiation, pediatric imaging in general has begun to shift away from CT toward magnetic resonance imaging (MRI) as a preferred modality...

This work introduced a stack-of-radial multi-echo asymmetric-echo MRI sequence for free-breathing liver volumetric acquisition. Regularized model-based reconstruction was implemented in Berkeley Advanced Reconstruction Toolbox (BART) to jointly estimate all physical parameter maps (water, fat, R*2 , and B0 field inhomogeneity maps) and coil sensitivity maps from self-gated k-space data. Specifically, locally low rank and temporal total variation regularization were employed directly on physical parameter maps...

PURPOSE: To validate a respiratory motion correction method called focused navigation (fNAV) for free-running radial whole-heart 4D flow MRI. METHODS: Using fNAV, respiratory signals derived from radial readouts are converted into three orthogonal displacements, which are then used to correct respiratory motion in 4D flow datasets. Hundred 4D flow acquisitions were simulated with non-rigid respiratory motion and used for validation. The difference between generated and fNAV displacement coefficients was calculated...