Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH cardiovascular magnetic resonance

Joint MAPLE: Accelerated joint T1 and T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ mapping with scan-specific self-supervised networks

PURPOSE

Quantitative MRI finds important applications in clinical and research studies. However, it is encoding intensive and may suffer from prohibitively long scan times. Accelerated MR parameter mapping techniques have been developed to help address these challenges. Here, an accelerated joint T1,T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ , frequency and proton density mapping technique with scan-specific self-supervised network reconstruction is proposed to synergistically combine parallel imaging, model-based, and deep learning approaches to speed up parameter mapping.

METHODS

Proposed framework, Joint MAPLE, includes parallel imaging, signal modeling, and data consistency blocks which are optimized jointly in a combined loss function. A scan-specific self-supervised reconstruction is embedded into the framework, which takes advantage of multi-contrast data from a multi-echo, multi-flip angle, gradient echo acquisition.

RESULTS

In comparison with parallel reconstruction techniques powered by low-rank methods, emerging scan specific networks, and model-basedT 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ estimation approaches, the proposed framework reduces the reconstruction error in parameter maps by approximately two-fold on average at acceleration rates as high as R = 16 with uniform sampling. It can outperform evaluated parallel reconstruction techniques up to four-fold on average in the presence of challenging sub-sampling masks. It is observed that Joint MAPLE performs well at extreme acceleration rates of R = 25 and R = 36 with error values less than 20%.

CONCLUSION

Joint MAPLE enables higher fidelity parameter estimation at high acceleration rates by synergistically combining parallel imaging and model-based parameter mapping and exploiting multi-echo, multi-flip angle datasets. Utilizing a scan-specific self-supervised reconstruction obviates the need for large data sets for training while improving the parameter estimation ability.

Native myocardial T1 mapping using inversion recovery T1-weighted turbo field echo sequence

This study proposes the use of the inversion recovery T1-weighted turbo field echo (IR-T1TFE) sequence for myocardial T1 mapping and compares the results obtained with those of the modified Look-Locker inversion recovery (MOLLI) method for accuracy, precision, and reproducibility. A phantom containing seven vials with different T1 values was imaged, thereby comparing the T1 measurements between the inversion recovery spin-echo (IR-SE) technique, MOLLI, and the IR-T1TFE. The accuracy, precision, and reproducibility of the T1-mapping sequences were analyzed in a phantom study. Fifteen healthy subjects were recruited for the in vivo comparison of native myocardial T1 mapping using MOLLI and IR-T1TFE sequences. After myocardium segmentation, the T1 value of the entire myocardium was calculated. In the phantom study, excellent accuracy was achieved using IR-T1TFE for all T1 ranges. MOLLI displayed lower accuracy than IR-T1TFE (p =0.016), substantially underestimating T1 at large T1 values (> 1000 ms). In the in vivo study, the first mean myocardial T1 values ± SD using MOLLI and IR-T1TFE were 1306 ± 70 ms and 1484 ± 28 ms, respectively, and the second were 1297 ± 68 ms and 1474 ± 43 ms, respectively. The native myocardial T1 obtained with MOLLI was lower than that of IR-T1TFE (p < 0.001). The reproducibility of native myocardial T1 mapping within the same sequence was not statistically significant (p = 0.11). This study demonstrates the utility and validity of myocardial T1 mapping using IR-T1TFE, which is a common sequence. This method was found to have high accuracy and reproducibility.

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.

High resolution single-shot myocardial imaging using bSSFP with wave encoding

BACKGROUND

Single-shot balanced steady-state free precession (bSSFP) sequence is widely used in cardiac imaging. However, the limited scan time in one heartbeat greatly hinders its spatial resolution compared to the segmented acquisition mode. Therefore, a highly accelerated single-shot bSSFP imaging technology is needed for clinical use.

PURPOSE

To develop and evaluate a wave-encoded bSSFP sequence with high acceleration rates for single-shot myocardial imaging.

METHODS

The proposed Wave-bSSFP method is implemented by adding a sinusoidal wave gradient in the phase encoding direction during the readout of bSSFP sequence. Uniform undersampling is used for acceleration. Its performance was first validated via phantom studies by comparison with conventional bSSFP. Then it was evaluated in volunteer studies via anatomical imaging, T2 -prepared bSSFP, and T1 mapping in in-vivo cardiac imaging. All methods were compared with accelerated conventional bSSFP reconstructed using iterative SENSE and compressed sensing (CS), to demonstrate the advantage of wave encoding in suppressing the noise amplification and artifacts induced by acceleration.

RESULTS

The proposed Wave-bSSFP method achieved a high acceleration factor of 4 for single-shot acquisitions. The proposed method showed lower average g-factors than bSSFP, and fewer blurring artifacts than CS reconstruction. The Wave-bSSFP with R = 4 achieved higher spatial and temporal resolutions compared with the conventional bSSFP with R = 2 in several applications such as T2 -prepared bSSFP and T1 mapping, and could be applied in systolic imaging.

CONCLUSION

Wave encoding can be used to highly accelerate 2D bSSFP imaging with single-shot acquisitions. Compared with the conventional bSSFP sequence, the proposed Wave-bSSFP method can effectively reduce the g-factor and aliasing artifacts in cardiac imaging.

Motion-compensated T1 mapping in cardiovascular magnetic resonance imaging: a technical review

T 1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T 1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T 1 maps can be acquired jointly with other quantitative parameters such as T 2 , T 2 ∗ , fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T 1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T 1 mapping, with potential future directions for further technical development and clinical translation.

Free-breathing myocardial T1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction

PURPOSE

To develop a free-breathing myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction.

METHODS

Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incompleteT 1 $$ {\mathrm{T}}_1 $$ recovery into account for an accurateT 1 $$ {\mathrm{T}}_1 $$ calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction.

RESULTS

In comparison to an IR spin-echo reference, phantom results confirm goodT 1 $$ {\mathrm{T}}_1 $$ accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improvesT 1 $$ {\mathrm{T}}_1 $$ precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardialT 1 $$ {\mathrm{T}}_1 $$ maps can be obtained within 2 min with good precision and repeatability.

CONCLUSION

Motion-resolved myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.

Latest Advances in Image Acceleration: All Dimensions are Fair Game

Magnetic resonance imaging (MRI) is a versatile modality that can generate high-resolution images with a variety of tissue contrasts. However, MRI is a slow technique and requires long acquisition times, which increase with higher temporal and spatial resolution and/or when multiple contrasts and large volumetric coverage is required. In order to speedup MR data acquisition, several approaches have been introduced in the literature. Most of these techniques acquire less data than required and exploit intrinsic redundancies in the MR images to recover the information that was not sampled. This article presents a review of MR acquisition and reconstruction methods that have exploited redundancies in the temporal, spatial, and contrast/parametric dimensions to accelerate image data acquisition, focusing on cardiac and abdominal MR imaging applications. The review describes how each of these dimensions has been separately exploited for speeding up MR acquisition to then discuss more advanced techniques where multiple dimensions are exploited together for further reducing scan times. Finally, future directions for multidimensional image acceleration and remaining technical challenges are discussed. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.

Simultaneous multi-slice T1 mapping using MOLLI with blipped CAIPIRINHA bSSFP

BACKGROUND

This study evaluates the possibility for replacing conventional 3 slices, 3 breath-holds MOLLI cardiac T1 mapping with single breath-hold 3 simultaneous multi-slice (SMS3) T1 mapping using blipped-CAIPIRINHA SMS-bSSFP MOLLI sequence. As a major drawback, SMS-bSSFP presents unique artefacts arising from side-lobe slice excitations that are explained by imperfect RF modulation rendering and bSSFP low flip angle enhancement. Amplitude-only RF modulation (AM) is proposed to reduce these artefacts in SMS-MOLLI compared to conventional Wong multi-band RF modulation (WM).

MATERIALS AND METHODS

Phantoms and ten healthy volunteers were imaged at 1.5 T using a modified blipped-CAIPIRINHA SMS-bSSFP MOLLI sequence with 3 simultaneous slices. WM-SMS3 and AM-SMS3 were compared to conventional single-slice (SMS1) MOLLI. First, SNR degradation and T1 accuracy were measured in phantoms. Second, artefacts from side-lobe excitations were evaluated in a phantom designed to reproduce fat presence near the heart. Third, the occurrence of these artefacts was observed in volunteers, and their impact on T1 quantification was compared between WM-SMS3 and AM-SMS3 with conventional MOLLI as a reference.

RESULTS

In the phantom, larger slice gaps and slice thicknesses yielded higher SNR. There was no significant difference of T1 values between conventional MOLLI and SMS3-MOLLI (both WM and AM). Positive banding artefacts were identified from fat neighbouring the targeted FOV due to side-lobe excitations from WM and the unique bSSFP signal profile. AM RF pulses reduced these artefacts by 38%. In healthy volunteers, AM-SMS3-MOLLI showed similar artefact reduction compared to WM-SMS3-MOLLI (3 ± 2 vs 5 ± 3 corrupted LV segments out of 16). In-vivo native T1 values obtained from conventional MOLLI and AM-SMS3-MOLLI were equivalent in LV myocardium (SMS1-T1 = 935.5 ± 36.1 ms; AM-SMS3-T1 = 933.8 ± 50.2 ms; P = 0.436) and LV blood pool (SMS1-T1 = 1475.4 ± 35.9 ms; AM-SMS3-T1 = 1452.5 ± 70.3 ms; P = 0.515). Identically, no differences were found between SMS1 and SMS3 postcontrast T1 values in the myocardium (SMS1-T1 = 556.0 ± 19.7 ms; SMS3-T1 = 521.3 ± 28.1 ms; P = 0.626) and the blood (SMS1-T1 = 478 ± 65.1 ms; AM-SMS3-T1 = 447.8 ± 81.5; P = 0.085).

CONCLUSIONS

Compared to WM RF modulation, AM SMS-bSSFP MOLLI was able to reduce side-lobe artefacts considerably, providing promising results to image the three levels of the heart in a single breath hold. However, few artefacts remained even using AM-SMS-bSSFP due to residual RF imperfections. The proposed blipped-CAIPIRINHA MOLLI T1 mapping sequence provides accurate in vivo T1 quantification in line with those obtained with a single slice acquisition.

Evaluation of combined late gadolinium-enhancement and functional cardiac magnetic resonance imaging using spiral real-time acquisition

The purpose of the current study was to implement and validate joint real-time acquisition of functional and late gadolinium-enhancement (LGE) cardiac magnetic resonance (MR) images during free breathing. Inversion recovery cardiac real-time images with a temporal resolution of 50 ms were acquired using a spiral trajectory (IR-CRISPI) with a pre-emphasis based on the gradient system transfer function during free breathing. Functional and LGE cardiac MR images were reconstructed using a low-rank plus sparse model. Late gadolinium-enhancement appearance, image quality, and functional parameters of IR-CRISPI were compared with clinical standard balanced steady-state free precession breath-hold techniques in 10 patients. The acquisition of IR-CRISPI in free breathing of the entire left ventricle took 97 s on average. Bland-Altman analysis and Wilcoxon tests showed a higher artifact level for the breath-hold technique (p = 0.003), especially for arrhythmic patients or patients with dyspnea, but an increased noise level for IR-CRISPI of the LGE images (p = 0.01). The estimated transmural extent of the enhancement differed by not more than 25% and did not show a significant bias between the techniques (p = 0.50). The ascertained functional parameters were similar for the breath-hold technique and IR-CRISPI, that is, with a minor, nonsignificant (p = 0.16) mean difference of the ejection fraction of 2.3% and a 95% confidence interval from -4.8% to 9.4%. IR-CRISPI enables joint functional and LGE imaging in free breathing with good image quality but distinctly shorter scan times in comparison with breath-hold techniques.

Quantification of changes in myocardial T1 * values with exercise cardiac MRI using a free-breathing non-electrocardiograph radial imaging

PURPOSE

To develop and evaluate a free breathing non-electrocardiograph (ECG) myocardial T₁ * mapping sequence using radial imaging to quantify the changes in myocardial T₁ * between rest and exercise (T₁ *_reactivity ) in exercise cardiac MRI (Ex-CMR).

METHODS

A free-running T₁ * sequence was developed using a saturation pulse followed by three Look-Locker inversion-recovery experiments. Each Look-Locker continuously acquired data as radial trajectory using a low flip-angle spoiled gradient-echo readout. Self-navigation was performed with a temporal resolution of ∼100 ms for retrospectively extracting respiratory motion. The mid-diastole phase for every cardiac cycle was retrospectively detected on the recorded electrocardiogram signal using an empirical model. Multiple measurements were performed to obtain mean value to reduce effects from the free-breathing acquisition. Finally, data acquired at both mid-diastole and end-expiration are picked and reconstructed by a low-rank plus sparsity constraint algorithm. The performance of this sequence was evaluated by simulations, phantoms, and in vivo studies at rest and after physiological exercise.

RESULTS

Numerical simulation demonstrated that changes in T₁ * are related to the changes in T₁ ; however, other factors such as breathing motion could influence T₁ * measurements. Phantom T₁ * values measured using free-running T₁ * mapping sequence had good correlation with spin-echo T₁ values and was insensitive to heart rate. In the Ex-CMR study, the measured T₁ * reactivity was 10% immediately after exercise and declined over time.

CONCLUSION

The free-running T₁ * mapping sequence allows free-breathing non-ECG quantification of changes in myocardial T₁ * with physiological exercise. Although, absolute myocardial T₁ * value is sensitive to various confounders such as B₁ and B₀ inhomogeneity, quantification of its change may be useful in revealing myocardial tissue properties with exercise.

MaxGIRF: Image reconstruction incorporating concomitant field and gradient impulse response function effects

Purpose

To develop and evaluate an improved strategy for compensating concomitant field effects in non‐Cartesian MRI at the time of image reconstruction.

Theory

We present a higher‐order reconstruction method, denoted as MaxGIRF, for non‐Cartesian imaging that simultaneously corrects off‐resonance, concomitant fields, and trajectory errors without requiring specialized hardware. Gradient impulse response functions are used to predict actual gradient waveforms, which are in turn used to estimate the spatiotemporally varying concomitant fields based on analytic expressions. The result, in combination with a reference field map, is an encoding matrix that incorporates a correction for all three effects.

Methods

The MaxGIRF reconstruction is applied to noiseless phantom simulations, spiral gradient‐echo imaging of an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom, and axial and sagittal multislice spiral spin‐echo imaging of a healthy volunteer at 0.55 T. The MaxGIRF reconstruction was compared against previously established concomitant field‐compensation and image‐correction methods. Reconstructed images are evaluated qualitatively and quantitatively using normalized RMS error. Finally, a low‐rank approximation of MaxGIRF is used to reduce computational burden. The accuracy of the low‐rank approximation is studied as a function of minimum rank.

Results

The MaxGIRF reconstruction successfully mitigated blurring artifacts both in phantoms and in vivo and was effective in regions where concomitant fields counteract static off‐resonance, superior to the comparator method. A minimum rank of 8 and 30 for axial and sagittal scans, respectively, gave less than 2% error compared with the full‐rank reconstruction.

Conclusions

The MaxGIRF reconstruction simultaneously corrects off‐resonance, trajectory errors, and concomitant field effects. The impact of this method is greatest when imaging with longer readouts and/or at lower field strength.

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Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T₁ ), the spin-spin relaxation time (T₂ ), and the spin-lattice relaxation in the rotating frame (T_1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T₁ -, T₂ -, or T_1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.

Cardiac MR: From Theory to Practice

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

Respiratory Motion-Registered Isotropic Whole-Heart T2 Mapping in Patients With Acute Non-ischemic Myocardial Injury

Background: T₂ mapping is a magnetic resonance imaging technique that can be used to detect myocardial edema and inflammation. However, the focal nature of myocardial inflammation may render conventional 2D approaches suboptimal and make whole-heart isotropic 3D mapping desirable. While self-navigated 3D radial T₂ mapping has been demonstrated to work well at a magnetic field strength of 3T, it results in too noisy maps at 1.5T. We therefore implemented a novel respiratory motion-resolved compressed-sensing reconstruction in order to improve the 3D T₂ mapping precision and accuracy at 1.5T, and tested this in a heterogeneous patient cohort. Materials and Methods: Nine healthy volunteers and 25 consecutive patients with suspected acute non-ischemic myocardial injury (sarcoidosis, n = 19; systemic sclerosis, n = 2; acute graft rejection, n = 2, and myocarditis, n = 2) were included. The free-breathing T₂ maps were acquired as three ECG-triggered T₂-prepared 3D radial volumes. A respiratory motion-resolved reconstruction was followed by image registration of the respiratory states and pixel-wise T₂ mapping. The resulting 3D maps were compared to routine 2D T₂ maps. The T₂ values of segments with and without late gadolinium enhancement (LGE) were compared in patients. Results: In the healthy volunteers, the myocardial T₂ values obtained with the 2D and 3D techniques were similar (45.8 ± 1.8 vs. 46.8 ± 2.9 ms, respectively; P = 0.33). Conversely, in patients, T₂ values did differ between 2D (46.7 ± 3.6 ms) and 3D techniques (50.1 ± 4.2 ms, P = 0.004). Moreover, with the 2D technique, T₂ values of the LGE-positive segments were similar to those of the LGE-negative segments (T_2LGE-= 46.2 ± 3.7 vs. T_2LGE+ = 47.6 ± 4.1 ms; P = 0.49), whereas the 3D technique did show a significant difference (T_2LGE- = 49.3 ± 6.7 vs. T_2LGE+ = 52.6 ± 8.7 ms, P = 0.006). Conclusion: Respiratory motion-registered 3D radial imaging at 1.5T led to accurate isotropic 3D whole-heart T₂ maps, both in the healthy volunteers and in a small patient cohort with suspected non-ischemic myocardial injury. Significantly higher T₂ values were found in patients as compared to controls in 3D but not in 2D, suggestive of the technique's potential to increase the sensitivity of CMR at earlier stages of disease. Further study will be needed to demonstrate its accuracy.

Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping

PURPOSE

This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping.

METHODS

An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver).

RESULTS

ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers.

CONCLUSION

This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.

Synergistic multi-contrast cardiac magnetic resonance image reconstruction

Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Physics-based reconstruction methods for magnetic resonance imaging

Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction-addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Accelerated model-based quantitative diffusion MRI: A feasibility study for musculoskeletal application

PURPOSE

To develop a model-based reconstruction technique for diffusion quantification based on accelerated two-dimensional echo planar data, obtained with multiple b-weightings. In combination with a dedicated undersampling pattern, acceleration factors above three were proven feasible in a clinical setting.

METHODS

The proposed model-based method minimizes a cost function considering the l₂-norm of the difference between the Fourier transformation of a synthetic diffusion-model-generated k-space and the measured k-space data. Further regularization is performed by introduction of a total variation (TV) constraint to the cost function. Acceleration is achieved by a non-random undersampling pattern using acceleration factors that correspond to the total number of b-values. A rectangular region of variable size, centered in k-space, remains fully sampled for correction of phase variations, introduced by the different diffusion-encoding strengths.

RESULTS

Qualitative analysis of the resulting images (S₀ and ADC) demonstrates the potential of the suggested undersampling pattern in combination with a model-based iterative reconstruction. An edge analysis highlights the preservation of high-frequency information for all investigated undersampling factors. In comparison to a conventional SENSE-accelerated reconstruction, the quantitative analysis of the ADC maps revealed a significantly (P<0.05) superior performance of the suggested technique, enabling acceleration factors of R=3.65 without compromising diffusion data fidelity.

CONCLUSION

The presented work shows the potential of model-based ADC quantification, which, in combination with a suited undersampling pattern for multiple b-values, enables more than three-fold acceleration using two-dimensional EPI without sacrificing ADC fidelity.

Model-based reconstruction for simultaneous multi-slice T1 mapping using single-shot inversion-recovery radial FLASH

PURPOSE

To develop a single-shot multi-slice T 1 mapping method by combing simultaneous multi-slice (SMS) excitations, single-shot inversion-recovery (IR) radial fast low-angle shot (FLASH), and a nonlinear model-based reconstruction method.

METHODS

SMS excitations are combined with a single-shot IR radial FLASH sequence for data acquisition. A previously developed single-slice calibrationless model-based reconstruction is extended to SMS, formulating the estimation of parameter maps and coil sensitivities from all slices as a single nonlinear inverse problem. Joint-sparsity constraints are further applied to the parameter maps to improve T 1 precision. Validations of the proposed method are performed for a phantom and for the human brain and liver in 6 healthy adult subjects.

RESULTS

Phantom results confirm good T 1 accuracy and precision of the simultaneously acquired multi-slice T 1 maps in comparison to single-slice references. In vivo human brain studies demonstrate the better performance of SMS acquisitions compared to the conventional spoke-interleaved multi-slice acquisition using model-based reconstruction. Aside from good accuracy and precision, the results of 6 healthy subjects in both brain and abdominal studies confirm good repeatability between scan and re-scans. The proposed method can simultaneously acquire T 1 maps for 5 slices of a human brain ( 0.75 × 0.75 × 5 mm 3 ) or 3 slices of the abdomen ( 1.25 × 1.25 × 6 mm 3 ) within 4 seconds.

CONCLUSIONS

The IR SMS radial FLASH acquisition together with a nonlinear model-based reconstruction enable rapid high-resolution multi-slice T 1 mapping with good accuracy, precision, and repeatability.

Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method

Quantitative tissue characteristics, which provide valuable diagnostic information, can be represented by magnetic resonance (MR) parameter maps using magnetic resonance imaging (MRI); however, a long scan time is necessary to acquire them, which prevents the application of quantitative MR parameter mapping to real clinical protocols. For fast MR parameter mapping, we propose a deep model-based MR parameter mapping network called DOPAMINE that combines a deep learning network with a model-based method to reconstruct MR parameter maps from undersampled multi-channel k-space data. DOPAMINE consists of two networks: 1) an MR parameter mapping network that uses a deep convolutional neural network (CNN) that estimates initial parameter maps from undersampled k-space data (CNN-based mapping), and 2) a reconstruction network that removes aliasing artifacts in the parameter maps with a deep CNN (CNN-based reconstruction) and an interleaved data consistency layer by an embedded MR model-based optimization procedure. We demonstrated the performance of DOPAMINE in brain T₁ map reconstruction with a variable flip angle (VFA) model. To evaluate the performance of DOPAMINE, we compared it with conventional parallel imaging, low-rank based reconstruction, model-based reconstruction, and state-of-the-art deep-learning-based mapping methods for three different reduction factors (R = 3, 5, and 7) and two different sampling patterns (1D Cartesian and 2D Poisson-disk). Quantitative metrics indicated that DOPAMINE outperformed other methods in reconstructing T₁ maps for all sampling patterns and reduction factors. DOPAMINE exhibited quantitatively and qualitatively superior performance to that of conventional methods in reconstructing MR parameter maps from undersampled multi-channel k-space data. The proposed method can thus reduce the scan time of quantitative MR parameter mapping that uses a VFA model.

Deep convolution neural networks based artifact suppression in under-sampled radial acquisitions of myocardial T 1 mapping images

We developed a deep convolutional neural network (CNN) based method to remove streaking artefact from accelerated radial acquisitions of myocardial T ₁-mapping images. A deep CNN based on a modified U-Net architecture was developed and trained to remove the streaking artefacts from under-sampled T ₁ mapping images. A total of 2090 T ₁-weighted images for 33 patients (55 ± 15 years, 19 males) and five healthy subjects (30 ± 14 years, 2 males) were used for training and testing the network. The images were acquired using radial slice interleaved T ₁ mapping sequence (STONE) and retrospectively under-sampled to achieve acceleration rate of 4 (corresponding to 48 spokes). The dataset was split into training and testing subsets with 23 subjects (60%) and 15 subjects (40%), respectively. For generating voxel-wise T ₁ maps, a two-parameter fitting model was used. Network performance was evaluated using normalized mean square error (NMSE), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) metrics. The proposed network allowed fast (<0.3 s/image) removal of the artefact from all T ₁-weighted testing images and the corresponding T ₁ maps with PSNR = 64.3 ± 1.02, NMSE = 0.2 ± 0.09 and SSIM = 0.9 ± 0.3 × 10^-4. There was no statistically significant difference between the measured T ₁ maps for both per-subject (reference: 1085 ± 37 ms, CNN: 1088 ± 37 ms, p = 0.4) and per-segment (reference: 1084 ± 48 ms, CNN: 1083 ± 58 ms, p = 0.9) analyses. In summary, deep CNN allows fast and reliable removal of streaking artefact from under-sampled radial T ₁ mapping images. Our results show that the highly non-linear operations of deep CNN processing of T ₁ mapping images do not impact accurate reconstruction of myocardial T ₁ maps.