Multi-scale locally low-rank noise reduction for high-resolution dynamic quantitative cardiac MRI

Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data

OBJECTIVE

This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.

METHODS

Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject.

RESULTS

fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant (p = 4.88×10-4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data.

CONCLUSION

LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.

Joint Cardiac T1 Mapping and Cardiac Cine Using Manifold Modeling

The main focus of this work is to introduce a single free-breathing and ungated imaging protocol to jointly estimate cardiac function and myocardial T1 maps. We reconstruct a time series of images corresponding to k-space data from a free-breathing and ungated inversion recovery gradient echo sequence using a manifold algorithm. We model each image in the time series as a non-linear function of three variables: cardiac and respiratory phases and inversion time. The non-linear function is realized using a convolutional neural networks (CNN) generator, while the CNN parameters, as well as the phase information, are estimated from the measured k-t space data. We use a dense conditional auto-encoder to estimate the cardiac and respiratory phases from the central multi-channel k-space samples acquired at each frame. The latent vectors of the auto-encoder are constrained to be bandlimited functions with appropriate frequency bands, which enables the disentanglement of the latent vectors into cardiac and respiratory phases, even when the data are acquired with intermittent inversion pulses. Once the phases are estimated, we pose the image recovery as the learning of the parameters of the CNN generator from the measured k-t space data. The learned CNN generator is used to generate synthetic data on demand by feeding it with appropriate latent vectors. The proposed approach capitalizes on the synergies between cine MRI and T1 mapping to reduce the scan time and improve patient comfort. The framework also enables the generation of synthetic breath-held cine movies with different inversion contrasts, which improves the visualization of the myocardium. In addition, the approach also enables the estimation of the T1 maps with specific phases, which is challenging with breath-held approaches.

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.

Free-breathing simultaneous T1 , T2 , and T2∗ quantification in the myocardium

PURPOSE

To implement a free-breathing sequence for simultaneous quantification of T 1 , T 2 , and T 2 ∗ for comprehensive tissue characterization of the myocardium in a single scan using a multi-gradient-echo readout with saturation and T 2 preparation pulses.

METHODS

In the proposed Saturation And T 2 -prepared Relaxometry with Navigator-gating (SATURN) technique, a series of multi-gradient-echo (GRE) images with different magnetization preparations was acquired during free breathing. A total of 35 images were acquired in 26.5 ± 14.9 seconds using multiple saturation times and T 2 preparation durations and with imaging at 5 echo times. Bloch simulations and phantom experiments were used to validate a 5-parameter fit model for accurate relaxometry. Free-breathing simultaneous T 1 , T 2 , and T 2 ∗ measurements were performed in 10 healthy volunteers and 2 patients using SATURN at 3T and quantitatively compared to conventional single-parameter methods such as SASHA for T 1 , T 2 -prepared bSSFP, and multi-GRE for T 2 ∗ .

RESULTS

Simulations confirmed accurate fitting with the 5-parameter model. Phantom measurements showed good agreement with the reference methods in the relevant range for in vivo measurements. Compared to single-parameter methods comparable accuracy was achieved. SATURN produced in vivo parameter maps that were visually comparable to single-parameter methods. No significant difference between T 1 , T 2 , and T 2 ∗ times acquired with SATURN and single-parameter methods was shown in quantitative measurements (SATURN T 1 = 1573 ± 86 ms , T 2 = 33.2 ± 3.6 ms , T 2 ∗ = 25.3 ± 6.1 ms ; conventional methods: T 1 = 1544 ± 107 ms , T 2 = 33.2 ± 3.6 ms , T 2 ∗ = 23.8 ± 5.5 ms ; P > . 2 ) CONCLUSION: SATURN enables simultaneous quantification of T 1 , T 2 , and T 2 ∗ in the myocardium for comprehensive tissue characterization with co-registered maps, in a single scan with good agreement to single-parameter methods.

Improved simultaneous multislice cardiac MRI using readout concatenated k-space SPIRiT (ROCK-SPIRiT)

PURPOSE

To develop and evaluate a simultaneous multislice (SMS) reconstruction technique that provides noise reduction and leakage blocking for highly accelerated cardiac MRI.

METHODS

ReadOut Concatenated k-space SPIRiT (ROCK-SPIRiT) uses the concept of readout concatenation in image domain to represent SMS encoding, and performs coil self-consistency as in SPIRiT-type reconstruction in an extended k-space, while allowing regularization for further denoising. The proposed method is implemented with and without regularization, and validated on retrospectively SMS-accelerated cine imaging with three-fold SMS and two-fold in-plane acceleration. ROCK-SPIRiT is compared with two leakage-blocking SMS reconstruction methods: readout-SENSE-GRAPPA and split slice-GRAPPA. Further evaluation and comparisons are performed using prospectively SMS-accelerated cine imaging.

RESULTS

Results on retrospectively three-fold SMS and two-fold in-plane accelerated cine imaging show that ROCK-SPIRiT without regularization significantly improves on existing methods in terms of PSNR (readout-SENSE-GRAPPA: 33.5 ± 3.2, split slice-GRAPPA: 34.1 ± 3.8, ROCK-SPIRiT: 35.0 ± 3.3) and SSIM (readout-SENSE-GRAPPA: 84.4 ± 8.9, split slice-GRAPPA: 85.0 ± 8.9, ROCK-SPIRiT: 88.2 ± 6.6 [in percentage]). Regularized ROCK-SPIRiT significantly outperforms all methods, as characterized by these quantitative metrics (PSNR: 37.6 ± 3.8, SSIM: 94.2 ± 4.1 [in percentage]). The prospectively five-fold SMS and two-fold in-plane accelerated data show that ROCK-SPIRiT and regularized ROCK-SPIRiT have visually improved image quality compared with existing methods.

CONCLUSION

The proposed ROCK-SPIRiT technique reduces noise and interslice leakage in accelerated SMS cardiac cine MRI, improving on existing methods both quantitatively and qualitatively.

Self-calibrated interpolation of non-Cartesian data with GRAPPA in parallel imaging

PURPOSE

To develop a non-Cartesian k-space reconstruction method using self-calibrated region-specific interpolation kernels for highly accelerated acquisitions.

METHODS

In conventional non-Cartesian GRAPPA with through-time GRAPPA (TT-GRAPPA), the use of region-specific interpolation kernels has demonstrated improved reconstruction quality in dynamic imaging for highly accelerated acquisitions. However, TT-GRAPPA requires the acquisition of a large number of separate calibration scans. To reduce the overall imaging time, we propose Self-calibrated Interpolation of Non-Cartesian data with GRAPPA (SING) to self-calibrate region-specific interpolation kernels from dynamic undersampled measurements. The SING method synthesizes calibration data to adapt to the distinct shape of each region-specific interpolation kernel geometry, and uses a novel local k-space regularization through an extension of TT-GRAPPA. This calibration approach is used to reconstruct non-Cartesian images at high acceleration rates while mitigating noise amplification. The reconstruction quality of SING is compared with conjugate-gradient SENSE and TT-GRAPPA in numerical phantoms and in vivo cine data sets.

RESULTS

In both numerical phantom and in vivo cine data sets, SING offers visually and quantitatively similar reconstruction quality to TT-GRAPPA, and provides improved reconstruction quality over conjugate-gradient SENSE. Furthermore, temporal fidelity in SING and TT-GRAPPA is similar for the same acceleration rates. G-factor evaluation over the heart shows that SING and TT-GRAPPA provide similar noise amplification at moderate and high rates.

CONCLUSION

The proposed SING reconstruction enables significant improvement of acquisition efficiency for calibration data, while matching the reconstruction performance of TT-GRAPPA.

Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic Cardiac T 1 Mapping

Multi-dimensional, multi-contrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial T 1 mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. Tensors are well-suited to describe inter-dimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial T 1 mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in T 1 maps acquired in six healthy volunteers. All methods provided comparable T 1 values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic T 1 mapping at high spatio-temporal resolutions.

Functional LGE Imaging: Cardiac Phase-Resolved Assessment of Focal Fibrosis

Cardiac Magnetic Resonance Imaging (CMR) is a central tool for diagnosis of various ischemic and non-ischemic cardiomyopathies. CMR protocols commonly comprise assessment of functional properties using cardiac phase-resolved CINE MRI and characterization of myocardial viability using late gadolinium enhancement (LGE) imaging. Conventional LGE imaging requires inversion recovery preparation with a specific inversion time to null the healthy myocardium, which restricts the acquisition to a single cardiac phase. In turn, this necessitates separate scans for cardiac function and viability. In this work, we develop a new method for functional LGE imaging in a single breath-hold using a three-step approach: 1) ECG-triggered multi-contrast data is acquired for each cardiac phase, 2) semi-quantitative relaxation maps are generated, 3) LGE imaging contrast is synthesized based on the semi-quantitative maps. The proposed functional LGE method is evaluated in four healthy subject and 20 patients at 1.5T and 3T. Thorough suppression of the healthy myocardium, as well as 40-80ms temporal resolution are achieved, with no visually apparent temporal blurring at tissue interfaces. Functional LGE in patients with focal scar demonstrates robust hyperenhancement in the scar area throughout all cardiac phases, allowing for visual assessment of scar motility. The proposed technique bears the potential to simplify and speedup common cardiac imaging protocols, while enabling improved data fusion of functional and viability information for improved evaluation of CMR.

Saturation-Recovery Myocardial T1-Mapping during Systole: Accurate and Robust Quantification in the Presence of Arrhythmia

Myocardial T₁-mapping, a cardiac magnetic resonance imaging technique, facilitates a quantitative measure of fibrosis which is linked to numerous cardiovascular symptoms. To overcome the problems of common techniques, including lack of accuracy and robustness against partial-voluming and heart-rate variability, we introduce a systolic saturation-recovery T₁-mapping method. The Saturation-Pulse Prepared Heart-rate independent Inversion-Recovery (SAPPHIRE) T₁-mapping method was modified to enable imaging during systole. Phantom measurements were used to evaluate the insensitivity of systolic T₁-mapping towards heart-rate variability. In-vivo feasibility and accuracy were demonstrated in ten healthy volunteers with native and post-contrast T₁-mappping during systole and diastole. To show benefits in the presence of RR-variability, six arrhythmic patients underwent native T₁-mapping. Resulting systolic SAPPHIRE T₁-values showed no dependence on arrhythmia in phantom (CoV < 1%). In-vivo, significantly lower T₁ (1563 ± 56 ms, precision: 84.8 ms) and ECV-values (0.20 ± 0.03) than during diastole (T₁ = 1580 ± 62 ms, p = 0.0124; precision: 60.2 ms, p = 0.03; ECV = 0.21 ± 0.03, p = 0.0098) were measured, with a strong correlation of systolic and diastolic T₁ (r = 0.89). In patients, mis-triggering-induced motion caused significant imaging artifacts in diastolic T₁-maps, whereas systolic T₁-maps displayed resilience to arrythmia. In conclusion, the proposed method enables saturation-recovery T₁-mapping during systole, providing increased robustness against partial-voluming compared to diastolic imaging, for the benefit of T₁-measurements in arrhythmic patients.

Temporally resolved parametric assessment of Z-magnetization recovery (TOPAZ): Dynamic myocardial T1 mapping using a cine steady-state look-locker approach

PURPOSE

To develop and evaluate a cardiac phase-resolved myocardial T₁ mapping sequence.

METHODS

The proposed method for temporally resolved parametric assessment of Z-magnetization recovery (TOPAZ) is based on contiguous fast low-angle shot imaging readout after magnetization inversion from the pulsed steady state. Thereby, segmented k-space data are acquired over multiple heartbeats, before reaching steady state. This results in sampling of the inversion-recovery curve for each heart phase at multiple points separated by an R-R interval. Joint T₁ and B1+ estimation is performed for reconstruction of cardiac phase-resolved T₁ and B1+ maps. Sequence parameters are optimized using numerical simulations. Phantom and in vivo imaging are performed to compare the proposed sequence to a spin-echo reference and saturation pulse prepared heart rate-independent inversion-recovery (SAPPHIRE) T₁ mapping sequence in terms of accuracy and precision.

RESULTS

In phantom, TOPAZ T₁ values with integrated B1+ correction are in good agreement with spin-echo T₁ values (normalized root mean square error = 4.2%) and consistent across the cardiac cycle (coefficient of variation = 1.4 ± 0.78%) and different heart rates (coefficient of variation = 1.2 ± 1.9%). In vivo imaging shows no significant difference in TOPAZ T₁ times between the cardiac phases (analysis of variance: P = 0.14, coefficient of variation = 3.2 ± 0.8%), but underestimation compared with SAPPHIRE (T₁ time ± precision: 1431 ± 56 ms versus 1569 ± 65 ms). In vivo precision is comparable to SAPPHIRE T₁ mapping until middiastole (P > 0.07), but deteriorates in the later phases.

CONCLUSIONS

The proposed sequence allows cardiac phase-resolved T₁ mapping with integrated B1+ assessment at a temporal resolution of 40 ms. Magn Reson Med 79:2087-2100, 2018. © 2017 International Society for Magnetic Resonance in Medicine.