Ramping down a clinical 3 T scanner: a journey into MRI and MRS at 0.75 T.

OBJECT: Lower-field MR is reemerging as a viable, potentially cost-effective alternative to high-field MR, thanks to advances in hardware, sequence design, and reconstruction over the past decades. Evaluation of lower field strengths, however, is limited by the availability of lower-field systems on the market and their considerable procurement costs. In this work, we demonstrate a low-cost, temporary alternative to purchasing a dedicated lower-field MR system. MATERIALS AND METHODS: By ramping down an existing clinical 3 T MRI system to 0.75 T, proton signals can be acquired using repurposed 13C transmit/receive hardware and the multi-nuclei spectrometer interface. We describe the ramp-down procedure and necessary software and hardware changes to the system. RESULTS: Apart from presenting system characterization results, we show in vivo examples of cardiac cine imaging, abdominal two- and three-point Dixon-type water/fat separation, water/fat-separated MR Fingerprinting, and point-resolved spectroscopy. In addition, the ramp-down approach allows unique comparisons of, e.g., gradient fidelity of the same MR system operated at different field strengths using the same receive chain, gradient coils, and amplifiers. DISCUSSION: Ramping down an existing MR system may be seen as a viable alternative for lower-field MR research in groups that already own multi-nuclei hardware and can also serve as a testing platform for custom-made multi-nuclei transmit/receive coils.

Diffuse myocardial fibrosis precedes subclinical functional myocardial impairment and provides prognostic information in systemic sclerosis.

AIMS: Myocardial involvement is common in patients with systemic sclerosis (SSc) and causes myocardial fibrosis and subtle ventricular dysfunction. However, the temporal onset of myocardial involvement during the progression of the disease and its prognostic value are yet unknown. We used cardiovascular magnetic resonance (CMR) to investigate subclinical functional impairment and diffuse myocardial fibrosis in patients with very early diagnosis of SSc (VEDOSS) and established SSc and examined whether this was associated with mortality. METHODS AND RESULTS: One hundred and ten SSc patients (86 established SSc, 24 VEDOSS) and 15 healthy controls were prospectively recruited. The patients were followed-up for a median duration of 7.0 years (interquartile range 6.0-7.3 years). Study subjects underwent CMR including assessment of myocardial fibrosis [native T1 and extracellular volume (ECV)] and measurement of global longitudinal (GLS) and circumferential (GCS) myocardial strain. Native T1 values and ECV were elevated in VEDOSS and SSc patients compared with controls (P < 0.001). GLS was similar in VEDOSS and controls but significantly impaired in patients with established SSc (P < 0.001). GCS was similar over all groups (P = 0.88). There were 12 deaths during follow-up. Elevated native T1 [hazard ratio (HR) 5.8, 95% confidence interval (CI): 1.7-20.4; P = 0.006] and reduced GLS (HR 6.1, 95% CI: 1.3-29.9; P = 0.038) identified subjects with increased risk of death. Only native T1 was predictive for cardiovascular mortality (P < 0.001). CONCLUSION: Subclinical myocardial involvement first manifests as diffuse myocardial fibrosis identified by the expansion of ECV and increased native T1 in VEDOSS patients while subtle functional impairment only occurs in established SSc. Native T1 and GLS have prognostic value for all-cause mortality in SSc patients.

Free-breathing motion-informed locally low-rank quantitative 3D myocardial perfusion imaging.

PURPOSE: To propose respiratory motion-informed locally low-rank reconstruction (MI-LLR) for robust free-breathing single-bolus quantitative 3D myocardial perfusion CMR imaging. Simulation and in-vivo results are compared to locally low-rank (LLR) and compressed sensing reconstructions (CS) for reference. METHODS: Data were acquired using a 3D Cartesian pseudo-spiral in-out k-t undersampling scheme (R = 10) and reconstructed using MI-LLR, which encompasses two stages. In the first stage, approximate displacement fields are derived from an initial LLR reconstruction to feed a motion-compensated reference system to a second reconstruction stage, which reduces the rank of the inverse problem. For comparison, data were also reconstructed with LLR and frame-by-frame CS using wavelets as sparsifying transform ( ℓ1$$ {\ell}_1 $$ -wavelet). Reconstruction accuracy relative to ground truth was assessed using synthetic data for realistic ranges of breathing motion, heart rates, and SNRs. In-vivo experiments were conducted in healthy subjects at rest and during adenosine stress. Myocardial blood flow (MBF) maps were derived using a Fermi model. RESULTS: Improved uniformity of MBF maps with reduced local variations was achieved with MI-LLR. For rest and stress, intra-volunteer variation of absolute and relative MBF was lower in MI-LLR (±0.17 mL/g/min [26%] and ±1.07 mL/g/min [33%]) versus LLR (±0.19 mL/g/min [28%] and ±1.22 mL/g/min [36%]) and versus ℓ1$$ {\ell}_1 $$ -wavelet (±1.17 mL/g/min [113%] and ±6.87 mL/g/min [115%]). At rest, intra-subject MBF variation was reduced significantly with MI-LLR. CONCLUSION: The combination of pseudo-spiral Cartesian undersampling and dual-stage MI-LLR reconstruction improves free-breathing quantitative 3D myocardial perfusion CMR imaging under rest and stress condition.

Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac DTI.

Cardiac diffusion tensor imaging (cDTI) provides invaluable information about the state of myocardial microstructure. For further clinical dissemination, free-breathing acquisitions are desired, which however require image registration prior to tensor estimation. Due to the varying contrast and the intrinsically low signal-to-noise ratio (SNR), registration is very challenging and thus can introduce additional errors in the tensor estimation. In the work at hand it is hypothesized, that by incorporating spatial information and physiologically plausible priors into the fitting algorithm, the robustness of diffusion tensor estimation can be improved. To this end, we present a parameterized pipeline to generate synthetic data, that captures the statistics including spatial correlations of diffusion tensors and motion of the heart. The synthetic data is used to train a residual convolutional neural network (CNN) to estimate diffusion tensors from unregistered in-vivo cDTI data. Using in-silico data, the synthetically trained CNN is demonstrated to yield increased tensor estimation accuracy and precision when compared to conventional registration followed by least squares fitting. The network outputs fewer outliers especially at the myocardial borders. In-vivo feasibility using data from five healthy subjects demonstrates the utility of the synthetically trained network. The in-vivo results predicted by the synthetically trained CNN are found to be consistent with the registered least-squares estimates while showing fewer outliers and reduced noise. Even in low SNR regimes, the network results in robust tensor estimation, enabling scan time reduction by reduced-average acquisition in-vivo. Finally, to investigate the network's capability of discriminating between healthy and lesioned tissue, the in-vivo data was artificially augmented showing preserved classification of tissue states based on diffusion metrics.

Speed-of-sound imaging using diverging waves.

PURPOSE: Due to its safe, low-cost, portable, and real-time nature, ultrasound is a prominent imaging method in computer-assisted interventions. However, typical B-mode ultrasound images have limited contrast and tissue differentiation capability for several clinical applications. METHODS: Recent introduction of imaging speed-of-sound (SoS) in soft tissues using conventional ultrasound systems and transducers has great potential in clinical translation providing additional imaging contrast, e.g., in intervention planning, navigation, and guidance applications. However, current pulse-echo SoS imaging methods relying on plane wave (PW) sequences are highly prone to aberration effects, therefore suboptimal in image quality. In this paper we propose using diverging waves (DW) for SoS imaging and study this comparatively to PW. RESULTS: We demonstrate wavefront aberration and its effects on the key step of displacement tracking in the SoS reconstruction pipeline, comparatively between PW and DW on a synthetic example. We then present the parameterization sensitivity of both approaches on a set of simulated phantoms. Analyzing SoS imaging performance comparatively indicates that using DW instead of PW, the reconstruction accuracy improves by over 20% in root-mean-square-error (RMSE) and by 42% in contrast-to-noise ratio (CNR). We then demonstrate SoS reconstructions with actual US acquisitions of a breast phantom. With our proposed DW, CNR for a high contrast tumor-representative inclusion is improved by 42%, while for a low contrast cyst-representative inclusion a 2.8-fold improvement is achieved. CONCLUSION: SoS imaging, so far only studied using a plane wave transmission scheme, can be made more reliable and accurate using DW. The high imaging contrast of DW-based SoS imaging will thus facilitate the clinical translation of the method and utilization in computer-assisted interventions such as ultrasound-guided biopsies, where B-Mode contrast is often to low to detect potential lesions.

Joint image and field map estimation for multi-echo hyperpolarized 13 C metabolic imaging of the heart.

PURPOSE: Image reconstruction of metabolic images from hyperpolarized 13 C multi-echo data acquisition is sensitive to susceptibility-induced phase offsets, which are particularly challenging in the heart. A model-based framework for joint estimation of metabolite images and field map from echo shift-encoded data is proposed. Using simulations, it is demonstrated that correction of signal spilling due to incorrect decomposition of metabolites and geometrical distortions over a wide range of off-resonance gradients is possible. In vivo feasibility is illustrated using hyperpolarized [1-13 C]pyruvate in the pig heart. METHODS: The model-based reconstruction for multi-echo, multicoil data was implemented as a nonconvex minimization problem jointly optimizing for metabolic images and B0 . A comprehensive simulation framework for echo shift-encoded hyperpolarized [1-13 C]pyruvate imaging was developed and applied to assess reconstruction performance and distortion correction of the proposed method. In vivo data were obtained in four pigs using hyperpolarized [1-13 C]pyruvate on a clinical 3T MR system with a six-channel receiver coil. Dynamic images were acquired during suspended ventilation using cardiac-triggered multi-echo single-shot echo-planar imaging in short-axis orientation. RESULTS: Simulations revealed that off-resonance gradients up to ±0.26 ppm/pixel can be corrected for with reduced signal spilling and geometrical distortions yielding an accuracy of ≥90% in terms of Dice similarity index. In vivo, improved geometrical consistency (10% Dice improvement) compared to image reconstruction without field map correction and with reference to anatomical data was achieved. CONCLUSION: Joint image and field map estimation allows addressing off-resonance-induced geometrical distortions and metabolite spilling in hyperpolarized 13 C metabolic imaging of the heart.

Frequency-dependent attenuation reconstruction with an acoustic reflector.

Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequency-dependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04 dB/cm at 1 MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, consistently from different images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as facilitate attenuation compensation of other ultrasound-based imaging techniques.

Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation.

Simulation-based ultrasound (US) training can be an essential educational tool. Realistic US image appearance with typical speckle texture can be modeled as convolution of a point spread function with point scatterers representing tissue microstructure. Such scatterer distribution, however, is in general not known and its estimation for a given tissue type is fundamentally an ill-posed inverse problem. In this article, we demonstrate a convolutional neural network approach for probabilistic scatterer estimation from observed US data. We herein propose to impose a known statistical distribution on scatterers and learn the mapping between US image and distribution parameter map by training a convolutional neural network on synthetic images. In comparison with several existing approaches, we demonstrate in numerical simulations and with in vivo images that the synthesized images from scatterer representations estimated with our approach closely match the observations with varying acquisition parameters such as compression and rotation of the imaged domain.

Training Variational Networks With Multidomain Simulations: Speed-of-Sound Image Reconstruction.

Speed-of-sound (SoS) has been shown as a potential biomarker for breast cancer imaging, successfully differentiating malignant tumors from benign ones. SoS images can be reconstructed from time-of-flight measurements from ultrasound images acquired using conventional handheld ultrasound transducers. Variational networks (VNs) have recently been shown to be a potential learning-based approach for optimizing inverse problems in image reconstruction. Despite earlier promising results, these methods, however, do not generalize well from simulated to acquired data, due to the domain shift. In this work, we present for the first time a VN solution for a pulse-echo SoS image reconstruction problem using diverging waves with conventional transducers and single-sided tissue access. This is made possible by incorporating simulations with varying complexity into training. We use loop unrolling of gradient descent with momentum, with an exponentially weighted loss of outputs at each unrolled iteration in order to regularize the training. We learn norms as activation functions regularized to have smooth forms for robustness to input distribution variations. We evaluate reconstruction quality on the ray-based and full-wave simulations as well as on the tissue-mimicking phantom data, in comparison with a classical iterative [limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)] optimization of this image reconstruction problem. We show that the proposed regularization techniques combined with multisource domain training yield substantial improvements in the domain adaptation capabilities of VN, reducing the median root mean squared error (RMSE) by 54% on a wave-based simulation data set compared to the baseline VN. We also show that on data acquired from a tissue-mimicking breast phantom, the proposed VN provides improved reconstruction in 12 ms.

Determinants of myocardial function characterized by CMR-derived strain parameters in left ventricular non-compaction cardiomyopathy.

Clinical presentation of left ventricular non-compaction cardiomyopathy (LVNC) can be heterogeneous from asymptomatic expression to congestive heart failure. Deformation indices assessed by cardiovascular magnetic resonance (CMR) can determine subclinical alterations of myocardial function and have been reported to be more sensitive to functional changes than ejection fraction. The objective of the present study was to investigate the determinants of myocardial deformation indices in patients with LVNC. Twenty patients with LVNC (44.7 ± 14.0 years) and twenty age- and gender-matched controls (49.1 ± 12.4 years) underwent functional CMR imaging using an ECG-triggered steady state-free-precession sequence (SSFP). Deformation indices derived with a feature tracking algorithm were calculated including end-systolic global longitudinal strain (GLS), circumferential strain (GCS), longitudinal and circumferential strain rate (SRll and SRcc). Twist and rotation were determined using an in-house developed post-processing pipeline. Global deformation indices (GLS, GCS, SRll and SRcc) were significantly lower in patients with LVNC compared to healthy controls (all, p < 0.01), especially for midventricular and apical regions. Apical rotation and twist were impaired for LVNC (p = 0.007 and p = 0.012), but basal rotation was preserved. Deformation indices of strain, strain rate and twist correlated well with parameters of the non-compacted myocardium, but not with the total myocardial mass or the thinning of the compacted myocardium, e.g. r = 0.595 between GLS and the non-compacted mass (p < 0.001). In conclusion, CMR deformation indices are reduced in patients with LVNC especially in affected midventricular and apical slices. The impairment of all strain and twist parameters correlates well with the extent of non-compacted myocardium.

Isotropic Total Variation Regularization of Displacements in Parametric Image Registration.

Spatial regularization is essential in image registration, which is an ill-posed problem. Regularization can help to avoid both physically implausible displacement fields and local minima during optimization. Tikhonov regularization (squared l2 -norm) is unable to correctly represent non-smooth displacement fields, that can, for example, occur at sliding interfaces in the thorax and abdomen in image time-series during respiration. In this paper, isotropic Total Variation (TV) regularization is used to enable accurate registration near such interfaces. We further develop the TV-regularization for parametric displacement fields and provide an efficient numerical solution scheme using the Alternating Directions Method of Multipliers (ADMM). The proposed method was successfully applied to four clinical databases which capture breathing motion, including CT lung and MR liver images. It provided accurate registration results for the whole volume. A key strength of our proposed method is that it does not depend on organ masks that are conventionally required by many algorithms to avoid errors at sliding interfaces. Furthermore, our method is robust to parameter selection, allowing the use of the same parameters for all tested databases. The average target registration error (TRE) of our method is superior (10% to 40%) to other techniques in the literature. It provides precise motion quantification and sliding detection with sub-pixel accuracy on the publicly available breathing motion databases (mean TREs of 0.95 mm for DIR 4D CT, 0.96 mm for DIR COPDgene, 0.91 mm for POPI databases).