An optimal radial profile order based on the Golden Ratio for time-resolved MRI

5D image reconstruction exploiting space-motion-echo sparsity for accelerated free-breathing quantitative liver MRI

Recent advances in 3D non-Cartesian multi-echo gradient-echo (mGRE) imaging and compressed sensing (CS)-based 4D (3D image space + 1D respiratory motion) motion-resolved image reconstruction, which applies temporal total variation to the respiratory motion dimension, have enabled free-breathing liver tissue MR parameter mapping. This technology now allows for robust reconstruction of high-resolution proton density fat fraction (PDFF), R2∗, and quantitative susceptibility mapping (QSM), previously unattainable with conventional Cartesian mGRE imaging. However, long scan times remain a persistent challenge in free-breathing 3D non-Cartesian mGRE imaging. Recognizing that the underlying dimension of the imaging data is essentially 5D (4D + 1D echo signal evolution), we propose a CS-based 5D motion-resolved mGRE image reconstruction method to further accelerate the acquisition. Our approach integrates discrete wavelet transforms along the echo and spatial dimensions into a CS-based reconstruction model and devises a solution algorithm capable of handling such a 5D complex-valued array. Through phantom and in vivo human subject studies, we evaluated the effectiveness of leveraging unexplored correlations by comparing the proposed 5D reconstruction with the 4D reconstruction (i.e., motion-resolved reconstruction with temporal total variation) across a wide range of acceleration factors. The 5D reconstruction produced more reliable and consistent measurements of PDFF, R2∗, and QSM compared to the 4D reconstruction. In conclusion, the proposed 5D motion-resolved image reconstruction demonstrates the feasibility of achieving accelerated, reliable, and free-breathing liver mGRE imaging for the measurement of PDFF, R2∗, and QSM.

Ultra-high temporal resolution 4D angiography using arterial spin labeling with subspace reconstruction

PURPOSE

To achieve ultra-high temporal resolution non-contrast 4D angiography with improved spatiotemporal fidelity.

METHODS

Continuous data acquisition using 3D golden-angle sampling following an arterial spin labeling preparation allows for flexibly reconstructing 4D dynamic angiograms at arbitrary temporal resolutions. However, k-space data is often temporally "binned" before image reconstruction, negatively affecting spatiotemporal fidelity and limiting temporal resolution. In this work, a subspace was extracted by linearly compressing a dictionary constructed from simulated curves of an angiographic kinetic model. The subspace was used to represent and reconstruct the voxelwise signal timecourse at the same temporal resolution as the data acquisition without temporal binning. Physiological parameters were estimated from the resulting images using a Bayesian fitting approach. A group of eight healthy subjects were scanned and the in vivo results reconstructed by different methods were compared. Because of the difficulty of obtaining ground truth 4D angiograms with ultra-high temporal resolution, the in vivo results were cross-validated with numerical simulations.

RESULTS

The proposed method enables 4D time-resolved angiography with much higher temporal resolution (14.7 ms) than previously reported (˜50 ms) while maintaining high spatial resolution (1.1 mm isotropic). Blood flow dynamics were depicted in greater detail, thin vessel visibility was improved, and the estimated physiological parameters also exhibited more realistic spatial patterns with the proposed method.

CONCLUSION

Incorporating a subspace compressed kinetic model into the reconstruction of 4D ASL angiograms notably improved the temporal resolution and spatiotemporal fidelity, which was subsequently beneficial for accurate physiological modeling.

Inverse Design of Molecular Qudits for Quantum Circuitry

The development of molecular quantum bits (qubits) for quantum information processing is a lofty goal. While many contemporary works investigate their potential for error correction, fault-tolerance, memories, etc., there is still a lack of experimental examples of molecular multiqubit sequences. Herein, we perform a theoretical investigation of spin Hamiltonian parameter space to identify molecules that could be used to implement a 4-level superdense coding algorithm that has the least stringent requirements for experimental implementation. To do so, we analyze the zero-field splitting (ZFS) Hamiltonian of an S = 3/2 spin system to determine its effectiveness as a molecular qudit capable of performing the superdense coding circuit with X-band pulsed electron paramagnetic resonance (EPR), accounting for realistic constraints imposed by EPR spectrometers. For an S = 3/2 system, the optimal ZFS parameters are |D| ≈ 0.115 cm-1 and |E| ≈ -0.0383 cm-1 (|E/D| ≈ 0.33 approaching the rhombic limit of 1/3), with a field around 160 mT. Our findings highlight the need to maximize the rhombicity of the spin Hamiltonian for four-level molecular qudits.

T1 Relaxation-Enhanced Steady-State Acquisition With Radial k-Space Sampling: A Novel Family of Pulse Sequences for Motion-Robust Volumetric T1-Weighted MRI With Improved Lesion Conspicuity

OBJECTIVES

Magnetization-prepared rapid gradient-echo (MP-RAGE) sequences are routinely acquired for brain exams, providing high conspicuity for enhancing lesions. Vessels, however, also appear bright, which can complicate the detection of small lesions. T1RESS (T1 relaxation-enhanced steady-state) sequences have been proposed as an alternative to MP-RAGE, offering improved lesion conspicuity and suppression of blood vessels. This work aims to evaluate the performance of radial T1RESS variants for motion-robust contrast-enhanced brain MRI.

MATERIALS AND METHODS

Radial stack-of-stars sampling was implemented for steady-state free-precession-based rapid T1RESS acquisition with saturation recovery preparation. Three variants were developed using a balanced steady-state free-precession readout (bT1RESS), an unbalanced fast imaging steady precession (FISP) readout (uT1RESS-FISP), and an unbalanced reversed FISP readout (uT1RESS-PSIF). Image contrast was evaluated in numerical simulations and phantom experiments. The motion robustness of radial T1RESS was demonstrated with a motion phantom. Four patients and six healthy volunteers were scanned at 3 T and 0.55 T. Extensions were developed combining T1RESS with GRASP for dynamic imaging, with GRAPPA for accelerated scans, and with Dixon for fat/water separation.

RESULTS

In simulations and phantom scans, uT1RESS-FISP provided higher signal intensity for regions with lower T1 values (<500 ms) compared with MP-RAGE. In motion experiments, radial uT1RESS-FISP showed fewer artifacts than MP-RAGE and Cartesian uT1RESS-FISP. In patients, both unbalanced uT1RESS variants provided higher lesion conspicuity than MP-RAGE. Blood vessels appeared bright with MP-RAGE, gray with uT1RESS-FISP, and dark with uT1RESS-PSIF. At 0.55 T, bT1RESS provided high signal-to-noise ratio T1-weighted images without banding artifacts. Lastly, dynamic T1RESS images with a temporal resolution of 10.14 seconds/frame were generated using the GRASP algorithm.

CONCLUSIONS

Radial T1RESS sequences offer improved lesion conspicuity and motion robustness and enable dynamic imaging for contrast-enhanced brain MRI. Both uT1RESS variants showed higher tumor-to-brain contrast than MP-RAGE and may find application as alternative techniques for imaging uncooperative patients with small brain lesions.

Ungated, plug-and-play preclinical cardiac CEST-MRI using radial FLASH with segmented saturation

PURPOSE

Electrocardiography (ECG) and respiratory-gated preclinical cardiac CEST-MRI acquisitions are difficult because of variable saturation recovery with T1, RF interference in the ECG signal, and offset-to-offset variation in Z-magnetization and cardiac phase introduced by changes in cardiac frequency and trigger delays.

METHODS

The proposed method consists of segmented saturation modules with radial FLASH readouts and golden angle progression. The segmented saturation blocks drive the system to steady-state, and because center k-space is sampled repeatedly, steady-state saturation dominates contrast during gridding and reconstruction. Ten complete Z-spectra were acquired in healthy mice using both ECG and respiratory-gated and ungated methods. Z-spectra were also acquired at multiple saturation B1 values to optimize for amide and Cr contrasts.

RESULTS

There was no significant difference between CEST contrasts (amide, Cr, magnetization transfer) calculated from images acquired using ECG and respiratory-gated and ungated methods (p = 0.27, 0.11, 0.47). A saturation power of 1.8μT provides optimal contrast amplitudes for both amide and total Cr contrast without significantly complicating CEST contrast quantification because of water direct saturation, magnetization transfer, and RF spillover between amide and Cr pools. Further, variability in CEST contrast measurements was significantly reduced using the ungated radial FLASH acquisition (p = 0.002, 0.006 for amide and Cr, respectively).

CONCLUSION

This method enables CEST mapping in the murine myocardium without the need for cardiac or respiratory gating. Quantitative CEST contrasts are consistent with those obtained using gated sequences, and per-contrast variance is significantly reduced. This approach makes preclinical cardiac CEST-MRI easily accessible, even for investigators without prior experience in cardiac imaging.

Prospective acceleration of whole-brain CEST imaging by golden-angle view ordering in Cartesian coordinates and joint k-space and image-space parallel imaging (KIPI)

PURPOSE

To prospectively accelerate whole-brain CEST acquisition by joint k-space and image-space parallel imaging (KIPI) with a proposed golden-angle view ordering technique (GAVOT) in Cartesian coordinates.

THEORY AND METHODS

The T2-decay effect will vary across frames with variable acceleration factors (AF) in the prospective acquisition using sequences with long echo trains. The GAVOT method uses a subset strategy to eliminate the T2-decay inconsistency, where all frames use a subset of shots from the calibration frame to form their k-space view ordering. The golden-angle rule is adapted to ensure uniform k-space coverage for arbitrary AFs in Cartesian coordinates. Phantom and in vivo studies were conducted on a 3 T scanner.

RESULTS

The GAVOT view ordering yielded a higher g-factor than conventional uniformly centric ordering, whereas the noise propagation in amide proton transfer (APT) weighted images was similar between different view ordering. Compared to centric ordering, GAVOT successfully eliminated the T2-decay inconsistency across all frames, resulting in fewer image artifacts for both KIPI and conventional parallel imaging methods. The synergy of GAVOT and KIPI mitigated strong aliasing artifacts and achieved high-quality reconstruction of prospective variable-AF datasets. GAVOT-KIPI reduced the scan time to 2.1 min for whole-brain APT weighted imaging and 4.7 min for quantitative APT signal (APT#) mapping.

CONCLUSION

GAVOT makes the prospective variable AF strategy flexible and practical, and, in conjunction with KIPI, ensures high-quality reconstruction from highly undersampled data, facilitating the clinical translation of whole-brain CEST imaging.

T1 signal intensity ratio correlation with T1 mapping in pediatric pancreatitis

PURPOSE

Our primary purpose was to understand the correlation between pancreas T1-weighted signal intensity ratio (SIR) and T1 relaxation time in children. We also sought to characterize differences in T1 SIR between children without and with pancreatitis.

METHODS

Retrospective study of patients < 18-years-old. SIR-pancreas:spleen (SIR-PS) and SIR-pancreas:paraspinal muscle (SIR-PM) were generated from T1-weighted gradient recalled echo images. Subdivided by field strength, T1 SIR was correlated (Spearman's) with T1 relaxation time.

RESULTS

220 participants were included, 144 imaged at 1.5T (mean: 11.4 ± 4.2 years) and 76 imaged at 3T (mean: 10.9 ± 4.5 years). At 1.5T, SIR-PS (rho=-0.62, 95% CI: -0.71 to -0.51, p < 0.0001) and SIR-PM (rho=-0.57, 95% CI: -0.67 to -0.45, p < 0.0001) moderately negatively correlated with T1 relaxation time. At 3T, correlations between T1 SIR and T1 relaxation time were moderate (rho=-0.40 to -0.43, p ≤ 0.0003). SIR-PS was significantly different between patient groups at 1.5T (p < 0.0001) with pairwise differences between: normal vs. acute on chronic pancreatitis (1.52 vs. 1.13; p < 0.0001). SIR-PM was also significantly different between groups at 1.5T (p < 0.0001) with differences between: normal vs. acute pancreatitis (1.65 vs. 1.40; p = 0.0006), normal vs. acute on chronic pancreatitis (1.65 vs. 1.18; p < 0.0001), and normal vs. chronic pancreatitis (1.65 vs. 1.52; p = 0.0066). A SIR-PS cut-off of ≤ 1.31 had 44% sensitivity and 95% specificity and SIR-PM cut-off of ≤ 1.53 had 69% sensitivity and 70% specificity for pancreatitis. At 3T, SIR-PS was significantly different between groups (p = 0.033) but without significant pairwise differences.

CONCLUSION

At 1.5T pancreas T1 SIR moderately to strongly correlates with estimated T1 relaxation time and is significantly lower in children with pancreatitis.

Quantification of regional CMRO2 in human brain using dynamic 17O-MRI at 3T

OBJECTIVE

To investigate the feasibility of cerebral metabolic rate of oxygen consumption (CMRO2) measurements with MRI at 3 Tesla in different brain regions.

METHODS

CMRO2 represents a key indicator of the physiological state of brain tissue. Dynamic 17O-MRI with inhalation of isotopically enriched 17O gas has been used to quantify global CMRO2 in brain white (WM) and gray matter (GM). However, global CMRO2 can only reflect the overall oxygen metabolism of the brain and cannot provide enough information on local tissue oxygen metabolism. To investigate the feasibility of determination of regional CMRO2 at a clinical 3 T MRI system, CMRO2 values in frontal, parietal and occipital WM and GM were determined in 5 healthy volunteers and compared to evaluate the regional differences of oxygen metabolism in WM and GM. Additionally, regional CMRO2 values were determined in deep brain structures including thalamus, dorsal striatum, caudate nucleus and insula cortex and in the cerebella, and compared with literature values from 15O-PET studies.

RESULTS

In cortical GM the determined CMRO2 values were in good agreement with the literature, whereas values in WM were about 32-48% higher than literature values. Regional analysis revealed a significantly higher CMRO2 in the occipital GM compared to the frontal and parietal GM. By contrast, no significant difference of CMRO2 was observed across the WM. In addition, CMRO2 in deep brain structures was lower compared to literature values and in the cerebella a good hemispheric symmetry of the tissue oxygen metabolism was found.

CONCLUSION

Dynamic 17O-MRI enables direct, non-invasive determination of regional CMRO2 in brain structures in healthy volunteers at 3T.

Highly accelerated non-contrast-enhanced time-resolved 4D MRA using stack-of-stars golden-angle radial acquisition with a self-calibrated low-rank subspace reconstruction

PURPOSE

To develop a highly accelerated non-contrast-enhanced 4D-MRA technique by combining stack-of-stars golden-angle radial acquisition with a modified self-calibrated low-rank subspace reconstruction.

METHODS

A low-rank subspace reconstruction framework was introduced in radial 4D MRA (SUPER 4D MRA) by combining stack-of-stars golden-angle radial acquisition with control-label k-space subtraction-based low-rank subspace modeling. Radial 4D MRA data were acquired and reconstructed using the proposed technique on 12 healthy volunteers and 1 patient with steno-occlusive disease. The performance of SUPER 4D MRA was compared with two temporally constrained reconstruction methods (golden-angle radial sparse parallel [GRASP] and GRASP-Pro) at different acceleration rates in terms of image quality and delineation of blood dynamics.

RESULTS

SUPER 4D MRA outperformed the other two reconstruction methods, offering superior image quality with a clear background and detailed delineation of cerebrovascular structures as well as great temporal fidelity in blood flow dynamics. SUPER 4D MRA maintained excellent performance even at higher acceleration rates.

CONCLUSIONS

SUPER 4D MRA is a promising technique for highly accelerating 4D MRA acquisition without comprising both temporal fidelity and image quality.

QRAGE-Simultaneous multiparametric quantitative MRI of water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength

PURPOSE

To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength.

METHODS

QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of TI and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging.

RESULTS

In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE.

CONCLUSION

QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.

Rational approximation of golden angles: Accelerated reconstructions for radial MRI

PURPOSE

To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle between consecutive spokes in golden ratio-based sampling schemes enables a flexible retrospective choice of temporal resolution, while preserving good coverage of k-space for each individual bin. Nevertheless, irrational increments prohibit precomputation of the point-spread function (PSF), can lead to numerical problems, and require more complex processing steps. To avoid these problems, a new sampling scheme based on a rational approximation of golden angles (RAGA) is developed.

METHODS

The theoretical properties of RAGA sampling are mathematically derived. Sidelobe-to-peak ratios (SPR) are numerically computed and compared to the corresponding golden ratio sampling schemes. The sampling scheme is implemented in the BART toolbox and in a radial gradient-echo sequence. Feasibility is shown for quantitative imaging in a phantom and a cardiac scan of a healthy volunteer.

RESULTS

RAGA sampling can accurately approximate golden ratio sampling and has almost identical PSF and SPR. In contrast to golden ratio sampling, each frame can be reconstructed with the same equidistant trajectory using different sampling masks, and the angle of each acquired spoke can be encoded as a small index, which simplifies processing of the acquired data.

CONCLUSION

RAGA sampling provides the advantages of golden ratio sampling while simplifying data processing, rendering it a valuable tool for dynamic and quantitative MRI.

Understanding Permeability Changes in Vestibular Schwannomas as Part of the Dynamic Response to Radiosurgery Using Golden-Angle Radial Sparse Parallel Imaging: A Retrospective Study

BACKGROUND AND OBJECTIVES

Vestibular schwannomas demonstrate different responses after stereotactic radiosurgery (SRS), commonly including a transient loss of internal enhancement on postcontrast T1-weighted MRI thought to be due to an early reduction in tumor vascularity. We used dynamic contrast-enhanced based golden-angle radial sparse parallel (GRASP) MRI to characterize the vascular permeability changes underlying this phenomenon, with correlations to long-term tumor regression.

METHODS

Consecutive patients with vestibular schwannoma who underwent SRS between 2017 and 2019, had a transient loss of enhancement after SRS, and had long-term longitudinal GRASP studies (6, 18, and 30 months) were included in this retrospective cohort analysis (n = 19). Using GRAVIS ( https://gravis-imaging.org/gravis/ ), an analysis pipeline for GRASP studies, we extracted the key parameters normalized to the venous sinus from a region of interest within the tumor.

RESULTS

The peak, area under the curve (AUC), and wash-in phase slope were significantly reduced at 6, 18, and 30 months after SRS (corrected P < .05), even while the internal enhancement returned in the tumors. Larger pre-SRS tumors were more likely to have a greater reduction in peak ( P = .013) and AUC ( P = .029) at 6 months. In a subset of patients (N = 13) with long-term follow-up, the median percentage reduction in tumor volume was 58% at a median of 62 months. These patients showed a strong correlation between peak, AUC, and wash-in phase slope changes at 6 months and tumor volume at the last follow-up.

CONCLUSION

After SRS and loss of internal contrast uptake within vestibular schwannomas, a slow vascular permeability dynamic persisted, suggesting the presence of postradiation processes such as fibrosis. We show for the first time, using GRASP, a quantitative assessment of the vascular radiobiological effect.

Dynamic contrast-enhanced MRA of the aorta using a Golden-angle RAdial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved cartesian MRA (TWIST)

PURPOSE

To compare the application of two contrast-enhanced time-resolved magnetic resonance angiography sequences on an aortic disease patient cohort: the conventional Cartesian-sampling-based, Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) sequence, and the radial-sampling-based Golden-angle RAdial Sparse Parallel (GRASP) sequence. TWIST is highly sensitive to patient movement, which can lead to blurring and reduced sharpness of vascular structures, particularly in dynamic regions like the aorta. Such motion artifacts can compromise diagnostic accuracy. Radial-sampling-based techniques are less sensitive to motion than cartesian sampling and are expected to improve the image quality in body parts subjected to motion.

METHODS

30 patients (60.9 ± 16.1y.o.) with various aortic diseases underwent a 1.5T magnetic resonance angiography examination. Assessment of image quality in the ascending aorta (AA), descending aorta (DA), and abdominal aorta (AbA) on a 4-point Likert scale (1 = excellent, 4 = non-diagnostic) as well as max. aortic diameters (Dmax) were performed. T-test and multilevel mixed-effect proportional-odds models were used for the image analysis.

RESULTS

GRASP offered superior depiction of vascular structures in terms of vascular contrast for qualitative analysis (TWIST, reader 1: 1.6 ± 0.5; reader 2: 1.9 ± 0.4; reader 3: 1.1 ± 0.4; GRASP, reader 1: 1.5 ± 0.5; reader 2: 1.4 ± 0.5; reader 3: 1.0 ± 0.2) and vessel sharpness for qualitative (TWIST, reader 1: 1.9 ± 0.6; reader 2: 1.6 ± 0.6; reader 3: 2.0 ± 0.3; GRASP, reader 1: 1.4 ± 0.6; reader 2: 1.2 ± 0.4; reader 3: 1.3 ± 0.6) and quantitative analysis (TWIST, AA = 0.12 ± 0.04, DA = 0.12 ± 0.03, AbA = 0.11 ± 0.03; GRASP, AA = 0.20 ± 0.05, DA = 0.22 ± 0.06, AbA=0.20 ± 0.05). Streaking artefacts of GRASP were more visible compared to TWIST (TWIST, reader 1: 2.2 ± 0.6; reader 2: 1.9 ± 0.3; reader 3: 2.0 ± 0.5; GRASP, reader 1: 2.6 ± 0.6; reader 2: 2.3 ± 0.5; reader 3: 2.8 ± 0.6). Aortic Dmax comparison among the sequence showed no clinical relevance.

CONCLUSION

GRASP outperformed TWIST in SNR, vessel sharpness, and reduction in image blurring; streaking artefacts were stronger with GRASP, but did not affect diagnostic image quality.

Improved ECG-gated cardiac cine imaging with variable initial value based tiny golden-angle radial trajectory

BACKGROUND

Breath-held electrocardiogram-gated cardiac cine imaging (ECG-CINE), as the gold standard for assessing cardiac function in magnetic resonance imaging (MRI), is prone to motion artifacts. Conventional golden-angle (CGA) sampling has emerged as a promising technique for mitigating motion effects in real-time cardiac cine imaging. However, in ECG-CINE, the irregular re-binning of radial k-space profiles based on CGA can exacerbate k-space non-uniformity, resulting in severe streaking artifacts. The recently introduced segmented golden-angle ratio (SGA) scheme aims to solve this problem; nevertheless, it sacrifices the desired motion insensitivity.

PURPOSE

The study aims to develop a more efficient k-space sampling scheme for ECG-CINE that guarantees both improved motion insensitivity and optimized k-space coverage.

METHOD

Theoretically, to enhance motion insensitivity, it is essential that the single-frame radial k-space profiles acquired within each heartbeat (HB) span as close to a full 360-degree range as possible. Meanwhile, to ensure uniform data coverage, the sequentially acquired k-space profiles need to be evenly distributed both within each HB and across multiple HBs. In this study, we propose a Variable Initial value-based tiny Golden-Angle radial trajectory (VIGA) to achieve these two goals. Specifically, VIGA is a two-step approach: First, the tiny golden-angle ratio is applied to the k-space profiles within each HB to maintain motion insensitivity and k-space uniformity as in CGA. Second, a golden ratio of the golden angle used within each HB is applied to the initial k-space profiles across adjacent HBs to optimize coverage further. We validated the proposed VIGA method through numerical simulations, phantom experiments, and prospective and retrospective in vivo cardiac cine experiments.

RESULTS

Numerical simulations revealed that the k-space uniformity of CGA is highly dependent on the number of spokes per HB, whereas VIGA and SGA maintained nearly optimal k-space coverage regardless of this parameter. Both phantom and prospective studies demonstrated that VIGA outperforms CGA when the number of spokes per HB is suboptimal, and surpasses SGA in conditions with residual respiratory motion. The standard deviation of gradient scores indicates statistical significance between CGA and VIGA under free-breathing conditions (p = 0.039) and between SGA and VIGA under all conditions tested (Free-breathing, 200 spokes/HB: p = 0.028; Breath-holding, 200 spokes/HB: p = 0.008; Free-breathing, 200 spokes/HB: p = 0.013; Breath-holding, 200 spokes/HB: p = 0.011). Retrospective results demonstrated that doctor ratings for SGA were lower than those for VIGA, and the ratings for systole images using VIGA were significantly higher than those using CGA (2.55 ± 0.45 vs. 3.29 ± 0.52; p = 0.04).

CONCLUSION

A novel and efficient k-space sampling scheme, named VIGA, was proposed to improve k-space uniformity and motion insensitivity. VIGA facilitates robust image quality in both prospective and retrospective cardiac cine imaging, demonstrating its potential as a clinically viable alternative to CGA and SGA.

Reduction of ringing artifacts induced by diaphragm drifting in free-breathing dynamic pulmonary MRI using 3D koosh-ball acquisition

PURPOSE

To reduce the ringing artifacts of the motion-resolved images in free-breathing dynamic pulmonary MRI.

METHODS

A golden-step based interleaving (GSI) technique was proposed to reduce ringing artifacts induced by diaphragm drifting. The pulmonary MRI data were acquired using a superior-inferior navigated 3D radial UTE sequence in an interleaved manner during free breathing. Successive interleaves were acquired in an incoherent fashion along the polar direction. Four-dimensional images were reconstructed from the motion-resolved k-space data obtained by retrospectively binning. The reconstruction algorithms included standard nonuniform fast Fourier transform (NUFFT), Voronoi-density-compensated NUFFT, extra-dimensional UTE, and motion-state weighted motion-compensation reconstruction. The proposed interleaving technique was compared with a conventional sequential interleaving (SeqI) technique on a phantom and eight subjects.

RESULTS

The quantified ringing artifacts level in the motion-resolved image is positively correlated with the quantified nonuniformity level of the corresponding k-space. The nonuniformity levels of the end-expiratory and end-inspiratory k-space binned from GSI data (0.34 ± 0.07, 0.33 ± 0.05) are significantly lower with statistical significance (p < 0.05) than that binned from SeqI data (0.44 ± 0.11, 0.42 ± 0.12). Ringing artifacts are substantially reduced in the dynamic images of eight subjects acquired using the proposed technique in comparison with that acquired using the conventional SeqI technique.

CONCLUSION

Ringing artifacts in the motion-resolved images induced by diaphragm drifting can be reduced using the proposed GSI technique for free-breathing dynamic pulmonary MRI. This technique has the potential to reduce ringing artifacts in free-breathing liver and kidney MRI based on full-echo interleaved 3D radial acquisition.

Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications

Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1- and T2-weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2*, and T1ρ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Fat suppression using frequency-sweep RF saturation and iterative reconstruction

PURPOSE

To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities.

METHODS

Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water. This information is then utilized to generate water-only composite images. The principle is demonstrated in free-breathing abdominal and neck examinations using stack-of-stars 3D balanced SSFP (bSSFP) and gradient-recalled echo (GRE) sequences at 0.55 and 3T. Moreover, a potential extension toward quantitative fat/water separation is described.

RESULTS

Experiments with a proton density fat fraction (PDFF) phantom validated the reliability of fat/water separation using signal-response curves. As demonstrated for abdominal imaging at 0.55T, the approach resulted in more uniform fat suppression without loss of water signal and in improved CSF-to-fat signal ratio. Moreover, the approach provided consistent fat suppression in 3T neck exams where conventional spectrally-selective fat saturation failed due to strong local B0 inhomogeneities. The feasibility of simultaneous fat/water quantification has been demonstrated in a PDFF phantom.

CONCLUSION

The proposed principle achieves reliable fat suppression in low-field applications and adapts to high-field applications with strong B0 inhomogeneity. Moreover, the principle potentially provides a basis for developing an alternative approach for PDFF quantification.

Accelerated B 1 + $$ {\mathrm{B}}_1^{+} $$ mapping and robust parallel transmit pulse design for heart and prostate imaging at 7 T

PURPOSE

To investigate the impact of reduced k-space sampling onB 1 + $$ {\mathrm{B}}_1^{+} $$ mapping and the resulting impact on phase shimming and dynamic/universal parallel transmit (pTx) RF pulse design.

METHODS

Channel-wise 3DB 1 + $$ {\mathrm{B}}_1^{+} $$ maps were measured at 7 T in 35 and 23 healthy subjects for the heart and prostate region, respectively. With theseB 1 + $$ {\mathrm{B}}_1^{+} $$ maps, universal phase shims optimizing homogeneity andB 1 + $$ {\mathrm{B}}_1^{+} $$ efficiency were designed for heart and prostate imaging. In addition, universal 4kT-point pulses were designed for the heart. Subsequently, individual phase shims and individual 4kT-pulses were designed based onB 1 + $$ {\mathrm{B}}_1^{+} $$ maps with different acceleration factors and tested on the original maps. The performance of the pulses was compared by evaluating their coefficients of variation (CoV),B 1 + $$ {\mathrm{B}}_1^{+} $$ efficiencies and specific energy doses (SED). Furthermore, validation measurements were carried out for one heart and one prostate subject.

RESULTS

For both organs, the universal phase shims showed significantly higherB 1 + $$ {\mathrm{B}}_1^{+} $$ efficiencies and lower CoVs compared to the vendor provided default shim, but could still be improved with individual phase shims based on acceleratedB 1 + $$ {\mathrm{B}}_1^{+} $$ maps (acquisition time = 30 s). In the heart, the universal 4kT-pulse achieved significantly lower CoVs than tailored phase shims. Tailored 4kT-pulses based on acceleratedB 1 + $$ {\mathrm{B}}_1^{+} $$ maps resulted in even further reduced CoVs or a 2.5-fold reduction in SED at the same CoVs as the universal 4kT-pulse.

CONCLUSION

AcceleratedB 1 + $$ {\mathrm{B}}_1^{+} $$ maps can be used for the design of tailored pTx pulses for prostate and cardiac imaging at 7 T, which further improve homogeneity,B 1 + $$ {\mathrm{B}}_1^{+} $$ efficiency, or SED compared to universal pulses.

An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI

Objective. The acceleration of magnetic resonance imaging (MRI) acquisition is crucial for both clinical and research applications, particularly in dynamic MRI. Existing compressed sensing (CS) methods, despite being effective for fast imaging, face limitations such as the need for incoherent sampling and residual noise, which restrict their practical use for rapid MRI.Approach. To overcome these challenges, we propose a novel image reconstruction framework that integrates the MRI physical model with a flexible, self-adjusting, decoupling data-driven model. We validated this method through experiments using both simulated andin vivodynamic contrast-enhanced MRI datasets.Main results. The experimental results demonstrate that the proposed framework achieves high spatial and temporal resolution reconstructions. Additionally, when compared to state-of-the-art image reconstruction approaches, our method significantly enhances acceleration capabilities, enabling sparse and rapid imaging with high resolution.Significance. Our proposed framework offers a promising solution for real-time imaging and image-guided radiation therapy applications by providing superior performance in achieving high spatial and temporal resolution reconstructions, thus addressing the limitations of existing CS schemes.

Electric potential energy optimized 3D radial sampling trajectories for MRI

A novel method for creating "golden" 3D center-out radial MRI sampling trajectories was developed and analyzed. This method, called ELECTRO (ELECTRic potential energy Optimized), uses repulsive forces to minimize electric potential energy. An objective function [Formula: see text], the electric potential energies of all subsets of consecutive readouts in a 3D radial trajectory, and its reduced form were minimized using a multi-stage optimization strategy. A metric called normalized mean nearest neighbor angular distance (NMNA) was proposed for describing distributions of points on a sphere. ELECTRO and other relevant golden trajectories were compared in silico using NMNA and point spread function analysis. Consecutive readouts from an ELECTRO trajectory were well spread out, with consistent NMNA values across sphere sizes (σNMNA = 0.005) and between regions on the sphere (NMNA ≈ 1.49). Conversely, the supergolden trajectory had poor consistency in NMNA values (σNMNA = 0.090) and clustering (NMNA = 1.28 at the pole with 40,000 readouts) that lead to artifact in the point spread function. Multi-stage optimization was faster than single-stage and obtained lower values of [Formula: see text] (e.g., 0.87 vs. 0.91, for a sphere size of 40). In conclusion, ELECTRO trajectories are more golden than other 3D center-out radial trajectories, making them a suitable candidate for dynamic 3D MR imaging.

Limited utility of subendometrial enhancement in assessing the interface between the endometrium and myometrium

This study was conducted to re-evaluate the utility of subendometrial enhancement (SEE) in assessing the interface between the endometrium and myometrium. In total, 110 women who underwent pelvic magnetic resonance imaging (MRI) for non-endometrial gynecologic diseases were enrolled in this prospective study. Two radiologists independently assessed the presence or absence of subendometrial enhancement (SEE) on dynamic contrast-enhanced (CE) MRI. A subgroup analysis was performed to evaluate the impact of menstrual status on the detection of SEE. Identified areas of SEE were rated using a 4-point Likert scale to determine their reliability in assessing the interface between the endometrium and myometrium. SEE was identified in 44.5% (49/110) of cases by radiologist 1 and in 39.1% (43/110) by radiologist 2. A subgroup analysis indicated no significant differences in the detection of SEE based on menstrual status. The identified areas of SEE were deemed reliable for assessing the interface between the endometrium and myometrium in 24.5% (12/49) of the cases evaluated by radiologist 1 and in 32.6% (14/43) of those evaluated by radiologist 2. Among the 110 women studied, reliable areas of SEE for assessing the interface between the endometrium and myometrium were observed in 10.9% (n = 12) by radiologist 1 and 12.7% (n = 14) by radiologist 2. The evaluation of SEE using dynamic CE MRI may be limited in its ability to assess the interface between the endometrium and myometrium.

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods

PURPOSE

To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods.

METHODS

A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of theB 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneity on estimated parameters.

RESULTS

The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy ofv p $$ {v}_p $$ andPS $$ \mathrm{PS} $$ increase to about 33% and 51% without correction forB 1 + $$ {\mathrm{B}}_1^{+} $$ field.

CONCLUSION

We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

T1 mapping for Head and Neck Cancer Patients undergoing Chemoradiotherapy: Feasibility of 3D Stack of Star Imaging

BACKGROUND

Measuring tissue oxygen concentration is crucial in understanding the pathophysiological process of hypoxia in head and neck cancer (HNC) and its significant role in cancer biology. This study aimed to determine the feasibility of T1 mapping using a variable flip angle (VFA) technique with stack of stars (SOS) trajectory sampling in HNC patients undergoing chemoradiotherapy (CRT).

METHODS

To evaluate the ability of SOS acquisition to detect T1, a phantom study was conducted and compared to conventional Cartesian acquisition (CART). Additionally, four newly diagnosed patients were recruited and underwent two scans each at baseline and inter-treatment. The repeatability of SOS and CART acquisitions was assessed by comparing the T1 measurements of CSF from the baseline and intra-treatment MRI studies. The changes in ∆T1 of the tumors during air and oxygen inhalation between baseline and inter-treatment scans were also evaluated.

RESULTS

Our study found that the 3D VFA SOS sequence was effective in reducing motion artifacts compared to the conventional VFA sequence with CART sampling and the same scan time, as demonstrated by the results from the phantom and patient studies. In terms of repeatability, no significant correlation was observed between the variability in ΔT1 measurements of CSF obtained from SOS T1 maps. The SOS ΔT1 measurements showed higher consistency, as evidenced by the ICC values ranging from 0.52 to 0.92. The ∆T1 measurements on the primary tumors increased after the first CRT (p<0.05) for all patients who showed a positive treatment response, except for one patient (0.05

CONCLUSION

The 3D VFA SOS sequence is a feasible and reliable method for T1 mapping in HNC patients undergoing CRT. The use of this technique could potentially aid in the assessment of treatment response and contribute to improving patient outcomes.

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Key Words

• 3D Stack of Star
• Head and Neck Cancer
• Oxygen-Enhancement
• T1 measurements
• Tumor Hypoxia
• Variable Flip Angle Method

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MESH Headings

• Humans
• Head and Neck Neoplasms/diagnostic imaging
• Head and Neck Neoplasms/therapy
• Male
• Magnetic Resonance Imaging/methods
• Chemoradiotherapy
• Feasibility Studies
• Middle Aged
• Phantoms, Imaging
• Female
• Imaging, Three-Dimensional/methods
• Reproducibility of Results
• Aged
• Adult
• Oxygen
• Artifacts

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Grants

• UL1 TR002538 NCATS NIH HHS
• UM1 TR004409 NCATS NIH HHS

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Affiliation(s)

Seong-Eun Kim Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT, USA.
John A Roberts Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT, USA
Eugene G Kholmovski Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Ying Hitchcock Department of Radiation Oncology, University of Utah, Salt Lake City, UT, USA
Yoshimi Anzai Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT, USA

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Advanced MRI techniques in abdominal imaging

Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.

Optimizing High-Resolution MR Angiography: The Synergistic Effects of 3D Wheel Sampling and Deep Learning-Based Reconstruction

OBJECTIVE

The aim of this study was to assess the utility of the combined use of 3D wheel sampling and deep learning-based reconstruction (DLR) for intracranial high-resolution (HR)-time-of-flight (TOF)-magnetic resonance angiography (MRA) at 3 T.

METHODS

This prospective study enrolled 20 patients who underwent head MRI at 3 T, including TOF-MRA. We used 3D wheel sampling called "fast 3D" and DLR for HR-TOF-MRA (spatial resolution, 0.39 × 0.59 × 0.5 mm 3 ) in addition to conventional MRA (spatial resolution, 0.39 × 0.89 × 1 mm 3 ). We compared contrast and contrast-to-noise ratio between the blood vessels (basilar artery and anterior cerebral artery) and brain parenchyma, full width at half maximum in the P3 segment of the posterior cerebral artery among 3 protocols. Two board-certified radiologists evaluated noise, contrast, sharpness, artifact, and overall image quality of 3 protocols.

RESULTS

The contrast and contrast-to-noise ratio of fast 3D-HR-MRA with DLR are comparable or higher than those of conventional MRA and fast 3D-HR-MRA without DLR. The full width at half maximum was significantly lower in fast 3D-MRA with and without DLR than in conventional MRA ( P = 0.006, P < 0.001). In qualitative evaluation, fast 3D-MRA with DLR had significantly higher sharpness and overall image quality than conventional MRA and fast 3D-MRA without DLR (sharpness: P = 0.021, P = 0.001; overall image quality: P = 0.029, P < 0.001).

CONCLUSIONS

The combination of 3D wheel sampling and DLR can improve visualization of arteries in intracranial TOF-MRA.

T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects

T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI

BACKGROUND

Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality.

PURPOSE

This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique.

METHODS

In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images.

RESULTS

SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns.

CONCLUSIONS

The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.

Impact of undersampling on preclinical lung T2* mapping with 3D radial UTE MRI at 7 T

Lung diseases are almost invariably heterogeneous and progressive, making it imperative to capture temporally and spatially explicit information to understand the disease initiation and progression. Imaging the lung with MRI-particularly in the preclinical setting-has historically been challenging because of relatively low lung tissue density, rapid cardiac and respiratory motion, and rapid transverse (T2*) relaxation. These limitations can largely be mitigated using ultrashort-echo-time (UTE) sequences, which are intrinsically robust to motion and avoid significant T2* decay. A significant disadvantage of common radial UTE sequences is that they require inefficient, center-out k-space sampling, resulting in long acquisition times relative to conventional Cartesian sequences. Therefore, pulmonary images acquired with radial UTE are often undersampled to reduce acquisition time. However, undersampling reduces image SNR, introduces image artifacts, and degrades true image resolution. The level of undersampling is further increased if offline gating techniques like retrospective gating are employed, because only a portion (∼40-50%) of the data is used in the final image reconstruction. Here, we explore the impact of undersampling on SNR and T2* mapping in mouse lung imaging using simulation and in-vivo data. Increased scatter in both metrics was noticeable at around 50% sampling. Parenchymal apparent SNR only decreased slightly (average decrease ∼ 1.4) with as little as 10% sampling. Apparent T2* remained similar across undersampling levels, but it became significantly increased (p < 0.05) below 80% sampling. These trends suggest that undersampling can generate quantifiable, but moderate changes in the apparent value of T2*. Moreover, these approaches to assess the impact of undersampling are straightforward to implement and can readily be expanded to assess the quantitative impact of other MR acquisition and reconstruction parameters.

Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging

Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels and additionally leverage long-range contextual information using a transformer-based module to alleviate ambiguities in the presence of artifacts caused by undersampling. We combine local and global dependencies to perform simultaneous coarse and fine motion estimation. The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI. The impact of motion estimation accuracy on the downstream task of motion-compensated reconstruction was analyzed. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories (Cartesian and radial) and acceleration factors of up to 16x for cardiac motion and 30x for respiratory motion and achieves superior image quality in motion-compensated reconstruction qualitatively and quantitatively compared to conventional and recent deep learning-based approaches. The code is publicly available at https://github.com/lab-midas/GMARAFT.

Respiratory motion-corrected T1 mapping of the abdomen

OBJECTIVE

The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency.

MATERIALS AND METHODS

Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times.

RESULTS

For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8 s.

DISCUSSION

The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.

Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region-optimised virtual coils

High acceleration factors in radial magnetic resonance fingerprinting (MRF) of the prostate lead to strong streak-like artefacts from flow in the femoral blood vessels, possibly concealing important anatomical information. Region-optimised virtual (ROVir) coils is a beamforming-based framework to create virtual coils that maximise signal in a region of interest while minimising signal in a region of interference. In this study, the potential of removing femoral flow streak artefacts in prostate MRF using ROVir coils is demonstrated in silico and in vivo. The ROVir framework was applied to radial MRF k-space data in an automated pipeline designed to maximise prostate signal while minimising signal from the femoral vessels. The method was tested in 15 asymptomatic volunteers at 3 T. The presence of streaks was visually assessed and measurements of whole prostate T1, T2 and signal-to-noise ratio (SNR) with and without streak correction were examined. In addition, a purpose-built simulation framework in which blood flow through the femoral vessels can be turned on and off was used to quantitatively evaluate ROVir's ability to suppress streaks in radial prostate MRF. In vivo it was shown that removing selected ROVir coils visibly reduces streak-like artefacts from the femoral blood flow, without increasing the reconstruction time. On average, 80% of the prostate SNR was retained. A similar reduction of streaks was also observed in silico, while the quantitative accuracy of T1 and T2 mapping was retained. In conclusion, ROVir coils efficiently suppress streaking artefacts from blood flow in radial MRF of the prostate, thereby improving the visual clarity of the images, without significant sacrifices to acquisition time, reconstruction time and accuracy of quantitative values. This is expected to help enable T1 and T2 mapping of prostate cancer in clinically viable times, aiding differentiation between prostate cancer from noncancer and healthy prostate tissue.

Free-breathing MRI techniques for fat and R2* quantification in the liver

OBJECTIVE

To review the recent advancements in free-breathing MRI techniques for proton-density fat fraction (PDFF) and R2* quantification in the liver, and discuss the current challenges and future opportunities.

MATERIALS AND METHODS

This work focused on recent developments of different MRI pulse sequences, motion management strategies, and reconstruction approaches that enable free-breathing liver PDFF and R2* quantification.

RESULTS

Different free-breathing liver PDFF and R2* quantification techniques have been evaluated in various cohorts, including healthy volunteers and patients with liver diseases, both in adults and children. Initial results demonstrate promising performance with respect to reference measurements. These techniques have a high potential impact on providing a solution to the clinical need of accurate liver fat and iron quantification in populations with limited breath-holding capacity.

DISCUSSION

As these free-breathing techniques progress toward clinical translation, studies of the linearity, bias, and repeatability of free-breathing PDFF and R2* quantification in a larger cohort are important. Scan acceleration and improved motion management also hold potential for further enhancement.

Comparison of lung lesion assessment using free-breathing dynamic contrast-enhanced 1.5-T MRI with a golden-angle radial stack-of-stars VIBE sequence and CT

BACKGROUND

Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung.

PURPOSE

To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference.

MATERIAL AND METHODS

In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis.

RESULTS

The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05).

CONCLUSION

Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions.

Time-Resolved Quantification of Patellofemoral Cartilage Deformation in Response to Loading and Unloading via Dynamic MRI With Prospective Motion Correction

BACKGROUND

In vivo cartilage deformation has been studied by static magnetic resonance imaging (MRI) with in situ loading, but knowledge about strain dynamics after load onset and release is scarce.

PURPOSE

To measure the dynamics of patellofemoral cartilage deformation and recovery in response to in situ loading and unloading by using MRI with prospective motion correction.

STUDY TYPE

Prospective.

SUBJECTS

Ten healthy male volunteers (age: [31.4 ± 3.2] years).

FIELD STRENGTH/SEQUENCE

T1-weighted RF-spoiled 2D gradient-echo sequence with a golden angle radial acquisition scheme, augmented with prospective motion correction, at 3 T.

ASSESSMENT

In situ knee loading was realized with a flexion angle of approximately 40° using an MR-compatible pneumatic loading device. The loading paradigm consisted of 2 minutes of unloaded baseline followed by a 5-minute loading bout with 50% body weight and an unloading period of 38 minutes. The cartilage strain was assessed as the mean distance between patellar and femoral bone-cartilage interfaces as a percentage of the initial (pre-load) distance.

STATISTICAL TESTS

Wilcoxon signed-rank tests (significance level: P < 0.05), Pearson correlation coefficient (r).

RESULTS

The cartilage compression and recovery behavior was characterized by a viscoelastic response. The elastic compression ([-12.5 ± 3.1]%) was significantly larger than the viscous compression ([-7.6 ± 1.5]%) and the elastic recovery ([10.5 ± 2.1]%) was significantly larger than the viscous recovery ([6.1 ± 1.8]%). There was a significant residual offset strain ([-3.6 ± 2.3]%) across the cohort. A significant negative correlation between elastic compression and elastic recovery was observed (r = -0.75).

DATA CONCLUSION

The in vivo cartilage compression and recovery time course in response to loading was successfully measured via dynamic MRI with prospective motion correction. The clinical relevance of the strain characteristics needs to be assessed in larger subject and patient cohorts.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Highly Accelerated Three-Dimensional Free-Breathing Cardiac T2 Mapping with Modified Low-Rank Modeling of Local K-Space Neighborhoods

Three-dimensional (3D) cardiac magnetic resonance T2 mapping has been proven beneficial for the diagnosis of myocardial edema. However, the time-consuming nature of cardiac quantitative imaging (e.g., T2 mapping) poses challenges in a clinical context, limiting its application mainly to 2D scenarios where fewer k-space samples are required. Efforts to develop efficient acceleration techniques in 3D cardiac T2 mapping have long been a focus of research. This study aims to develop and evaluate a highly accelerated 3D free-breathing cardiac T2 Mapping sequence with lOw-rank modeling of lOcal k-space neighboRhoods with parallel and parametric data rEdundancies (3D T2MOORE). Utilizing a customized electrocardiogram (ECG)-based tiny-golden angle stack-of-star sequence, three different T2-weighted fully sampled reference data sets are acquired for T2 estimation. These sets are retrospectively under-sampled at four increasing acceleration rates (R = 3.84, 4.80, 6.40, and 9.60) and then reconstructed based on 3D T2MOORE. T2 values are derived by fitting pixel-wise signals to an exponential decay curve. We conducted in vivo experiments to compare 3D T2MOORE with 3D fully sampled balanced steady-state procession T2 mapping (3D T2bSSFP) and a previously published approach, 3D fast gradient echo T2 mapping (3D T2FGRE). Retrospective analysis demonstrated that 3D T2MOORE with 4.8-fold acceleration rate yielded T2 accuracy comparable to 3D T2bSSFP (both in good agreement with previously reported T2 results) and outperformed 3D T2FGRE significantly (3D T2FGRE: 48.01 ± 6.68 ms; 3D T2bSSFP: 38.56±1.65 ms; 3D T2MOORE: 38.50±2.71 ms).

High Spatiotemporal Resolution Radial Encoding Single-Vessel fMRI

High-field preclinical functional MRI (fMRI) is enabled the high spatial resolution mapping of vessel-specific hemodynamic responses, that is single-vessel fMRI. In contrast to investigating the neuronal sources of the fMRI signal, single-vessel fMRI focuses on elucidating its vascular origin, which can be readily implemented to identify vascular changes relevant to vascular dementia or cognitive impairment. However, the limited spatial and temporal resolution of fMRI is hindered hemodynamic mapping of intracortical microvessels. Here, the radial encoding MRI scheme is implemented to measure BOLD signals of individual vessels penetrating the rat somatosensory cortex. Radial encoding MRI is employed to map cortical activation with a focal field of view (FOV), allowing vessel-specific functional mapping with 50 × 50 µm2 in-plane resolution at a 1 to 2 Hz sampling rate. Besides detecting refined hemodynamic responses of intracortical micro-venules, the radial encoding-based single-vessel fMRI enables the distinction of fMRI signals from vessel and peri-vessel voxels due to the different contribution of intravascular and extravascular effects.

Paired conditional generative adversarial network for highly accelerated liver 4D MRI

Purpose.4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) to shorten the 4D MRI reconstruction time while maintaining the reconstruction quality.Methods.Patients who underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3×, 6× and 10×) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer (RST), were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D +t) as temporal slices (2D +t). A total of 48 patients with 12 332 temporal slices were split into training (37 patients with 10 721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results.Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15, 0.16, and 120 s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3× Re-Con-GAN 80.98%; 3× CS 80.74%; 3× UNet 79.88%) of unprocessed under-sampled images (3× 69.61%).Conclusion.A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

High-resolution free-breathing hepatobiliary phase MRI of the liver using XD-GRASP

PURPOSE

To evaluate the performance of high-resolution free-breathing (FB) hepatobiliary phase imaging of the liver using the eXtra-Dimension Golden-angle RAdial Sparse Parallel (XD-GRASP) MRI technique.

METHODS

Fifty-eight clinical patients (41 males, mean age = 52.9 ± 12.9) with liver lesions who underwent dynamic contrast-enhanced MRI with a liver-specific contrast agent were prospectively recruited for this study. Both breath-hold volumetric interpolated examination (BH-VIBE) imaging and FB imaging were performed during the hepatobiliary phase. FB images were acquired using a stack-of-stars golden-angle radial sequence and were reconstructed using the XD-GRASP method. Two experienced radiologists blinded to acquisition schemes independently scored the overall image quality, liver edge sharpness, hepatic vessel clarity, conspicuity of lesion, and overall artifact level of each image. The non-parametric paired two-tailed Wilcoxon signed-rank test was used for statistical analysis.

RESULTS

Compared to BH-VIBE images, XD-GRASP images received significantly higher scores (P < 0.05) for the liver edge sharpness (4.83 ± 0.45 vs 4.29 ± 0.46), the hepatic vessel clarity (4.64 ± 0.67 vs 4.15 ± 0.56) and the conspicuity of lesion (4.75 ± 0.53 vs 4.31 ± 0.50). There were no significant differences (P > 0.05) between BH-VIBE and XD-GRASP images for the overall image quality (4.61 ± 0.50 vs 4.74 ± 0.47) and the overall artifact level (4.13 ± 0.44 vs 4.05 ± 0.61).

CONCLUSION

Compared to conventional BH-VIBE MRI, FB radial acquisition combined with XD-GRASP reconstruction facilitates higher spatial resolution imaging of the liver during the hepatobiliary phase. This enhancement can significantly improve the visualization and evaluation of the liver.

Radial Undersampled MRI Reconstruction Using Deep Learning With Mutual Constraints Between Real and Imaginary Components of K-Space

The deep learning method is an efficient solution for improving the quality of undersampled magnetic resonance (MR) image reconstruction while reducing lengthy data acquisition. Most deep learning methods neglect the mutual constraints between the real and imaginary components of complex-valued k-space data. In this paper, a new complex-valued convolutional neural network, namely, Dense-U-Dense Net (DUD-Net), is proposed to interpolate the undersampled k-space data and reconstruct MR images. The proposed network comprises dense layers, U-Net, and other dense layers in sequence. The dense layers are used to simulate the mutual constraints between real and imaginary components, and U-Net performs feature sparsity and interpolation estimation for k-space data. Two MRI datasets were used to evaluate the proposed method: brain magnitude-only MR images and knee complex-valued k-space data. Several operations were conducted for data preprocessing. First, the complex-valued MR images were synthesized by phase modulation on magnitude-only images. Second, a radial trajectory based on the golden angle was used for k-space undersampling, whereby a reversible normalization method was proposed to balance the distribution of positive and negative values in k-space data. The optimal performance of DUD-Net was demonstrated based on a quantitative evaluation of inter-method and intra-method comparisons. When compared with other methods, significant improvements were achieved, PSNRs were increased by 10.78 and 5.74dB, whereas RMSEs were decreased by 71.53% and 30.31% for magnitude and phase image, respectively. It is concluded that DUD-Net significantly improves the performance of MR image reconstruction.

Effect of the Relationship between Respiratory Interval and Temporal Resolution on Image Quality in Free-breathing Abdominal MR Imaging

PURPOSE

To evaluate how the relationship between respiratory interval (RI) and temporal resolution (TR) impacts image quality in free-breathing abdominal MRI (FB-aMRI) using golden-angle radial sparse parallel (GRASP).

METHODS

Ten healthy volunteers (25.9 ± 2.5 years, four women) underwent 2 mins free-breathing fat-suppression T1-weighted imaging using GRASP at RIs of 3 and 5s (RI3 and RI5, respectively) and retrospectively reconstructed at TR of 1.8, 2.9, 4.8, and 7.7s (TR1.8, TR2.9, TR4.8, and TR7.7, respectively) in each patient. The standard deviation (SD) under the diaphragm was measured using SD maps showing the discrepancy for each horizontal section at all TRs. Two radiologists evaluated image quality (visualization of the right hepatic vein at the confluence of the inferior vena cava, posterior segment branch of portal vein, pancreas, left kidney, and artifacts) at all TRs using a 5-point scale.

RESULTS

The SD was significantly higher at TR1.8 compared to TR4.8 (P < 0.01) and TR7.7 (P < 0.001), as well as at TR2.9 compared to TR7.7 (P < 0.01) for both RIs. The SD between TR4.8 and TR7.7 did not differ for both RIs. For all visual assessment metrics, the TR1.8 scores were significantly lower than the TR4.8 and TR7.7 scores for both RIs. The pancreas and left kidney scores at TR2.9 were significantly lower than those at TR7.7 (P < 0.05) for RI5. Additionally, the left kidney score at TR1.8 was lower than that at TR2.9 (P < 0.05) for RI3. All scores at TR2.9, TR4.8, and TR7.7 were similar for RI3, while those at TR4.8 and TR7.7 were similar for RI5.

CONCLUSION

Prolonging the TRs compared to RIs enhances image quality in FB-aMRI using GRASP.

Retrospectively gated ultrashort-echo-time MRI T1 mapping reveals compromised pulmonary microvascular NO-dependent function in a murine model of acute lung injury

This study sought to develop noninvasive, in vivo imaging schemes that allow for quantitative assessment of pulmonary microvascular functional status based on the combination of pulmonary T1 mapping and dynamic contrast-enhanced (DynCE) imaging. Ultrashort-echo-time (UTE) imaging at 9.4 T of lung parenchyma was performed. Retrospective gating was based on modulation of the first point in each recorded spoke. T1 maps were obtained using a series of five consecutive images with varying RF angles and analyzed with the variable flip angle approach. The obtained mean T1 lung value of 1078 ± 38 ms correlated well with previous reports. Improved intersession variability was observed, as evident from a decreased standard deviation of motion-resolved T1 mapping (F-test = 0.051). Animals received lipopolysaccharide (LPS) and were imaged at t = 2, 6, and 12 h after administration. The nitric oxide (NO)-dependent function was assessed according to changes in lung T1 after L-NAME injection, while microvascular perfusion and oxidant stress were assessed with contrast-enhanced imaging after injection of gadolinium or 3-carbamoyl-proxyl nitroxide radical, respectively. Retrospectivel gated UTE allowed robust, motion-compensated imaging that could be used for T1 mapping of lung parenchyma. Changes in lung T1 after L-NAME injection indicated that LPS induced overproduction of NO at t = 2 and 6 h after LPS, but NO-dependent microvascular function was impaired at t = 12 h after LPS. DynCE imaging at t = 6 h after LPS injection revealed decreased microvascular perfusion, with increased vascular permeability and oxidant stress. MRI allows to visualize and quantify lung microvascular NO-dependent function and its concomitant impairment during acute respiratory distress syndrome development with high sensitivity. UTE T1 mapping appears to be sensitive and useful in probing pulmonary microvascular functional status.

Quantification of whole-organ individual and bilateral renal metabolic rate of oxygen

PURPOSE

Renal metabolic rate of oxygen (rMRO2 ) is a potentially important biomarker of kidney function. The key parameters for rMRO2 quantification include blood flow rate (BFR) and venous oxygen saturation (SvO2 ) in a draining vessel. Previous approaches to quantify renal metabolism have focused on the single organ. Here, both kidneys are considered as one unit to quantify bilateral rMRO2 . A pulse sequence to facilitate bilateral rMRO2 quantification is introduced.

METHODS

To quantify bilateral rMRO2 , measurements of BFR and SvO2 are made along the inferior vena cava (IVC) at suprarenal and infrarenal locations. From the continuity equation, these four parameters can be related to derive an expression for bilateral rMRO2 . The recently reported K-MOTIVE pulse sequence was implemented at four locations: left kidney, right kidney, suprarenal IVC, and infrarenal IVC. A dual-band variant of K-MOTIVE (db-K-MOTIVE) was developed by incorporating simultaneous-multi-slice imaging principles. The sequence simultaneously measures BFR and SvO2 at suprarenal and infrarenal locations in a single pass of 21 s, yielding bilateral rMRO2 .

RESULTS

SvO2 and BFR are higher in suprarenal versus infrarenal IVC, and the renal veins are highly oxygenated (SvO2  >90%). Bilateral rMRO2 quantified in 10 healthy subjects (8 M, 30 ± 8 y) was found to be 291 ± 247 and 349 ± 300 (μmolO2 /min)/100 g, derived from K-MOTIVE and db-K-MOTIVE, respectively. In comparison, total rMRO2 from combining left and right was 329 ± 273 (μmolO2 /min)/100 g.

CONCLUSION

The present work demonstrates that bilateral rMRO2 quantification is feasible with fair reproducibility and physiological plausibility. The indirect method is a promising approach to compute bilateral rMRO2 when individual rMRO2 quantification is difficult.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy

BACKGROUND

Magnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality's utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.

METHODS

We trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison.

RESULTS

Deep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution.

CONCLUSIONS

Deployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.

Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model

PURPOSE

To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters.

THEORY AND METHODS

We combine two recently proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi-solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two-pool model. We sparsely sample each time frame along this spin dynamics with a three-dimensional radial koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency.

RESULTS

We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6-min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and longer relaxation times, consistent with previous reports.

CONCLUSION

The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.

Minimization of eddy current artifacts in sequences with periodic dynamics

PURPOSE

To minimize eddy current artifacts in periodic pulse sequences with balanced gradient moments as, for example, used for quantitative MRI.

THEORY AND METHODS

Eddy current artifacts in balanced sequences result from large jumps in k-space. In quantitative MRI, one often samples some spin dynamics repeatedly while acquiring different parts of k-space. We swap individual k-space lines between different repetitions in order to minimize jumps in temporal succession without changing the overall trajectory. This reordering can be formulated as a traveling salesman problem and we tackle the discrete optimization with a simulated annealing algorithm.

RESULTS

Compared to the default ordering, we observe a substantial reduction of artifacts in the reconstructed images and the derived quantitative parameter maps. Comparing two variants of our algorithm, one that resembles the pairing approach originally proposed by Bieri et al., and one that minimizes all k-space jumps equally, we observe slightly lower artifact levels in the latter.

CONCLUSION

The proposed reordering scheme effectively reduces eddy current artifacts in sequences with balanced gradient moments. In contrast to previous approaches, we capitalize on the periodicity of the sampled signal dynamics, enabling both efficient k-space sampling and minimizing artifacts caused by eddy currents.

Advanced Abdominal MRI Techniques and Problem-Solving Strategies

MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.

A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this article, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework produces high quality reconstructions and segmentations, leading to undersampling factors that are optimised on a scan-by-scan basis. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform simulations of radial k-space acquisitions using in-vivo cine CMR data from 270 subjects from the UK Biobank (with synthetic phase) and in-vivo cine CMR data from 16 healthy subjects (with real phase). The results demonstrate that the optimal undersampling factor varies for different subjects by approximately 1 to 2 seconds per slice. We show that our method can produce quality-controlled images in a mean scan time reduced from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to lie within 5% mean absolute difference.

Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice

OBJECTIVE

We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI).

METHODS

Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a T1-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit).

RESULTS

Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven.

CONCLUSION

The proposed method outperforms the reported methods of comparison and yields accurate T1-maps. Although presented for T1-mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs.

SIGNIFICANCE

From a clinical perspective, the obtained T1-maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.