A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting.

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.

Accelerated real-time cardiac MRI using iterative sparse SENSE reconstruction: comparing performance in patients with sinus rhythm and atrial fibrillation.

OBJECTIVES: To compare accelerated real-time cardiac MRI (CMR) using sparse spatial and temporal undersampling and non-linear iterative SENSE reconstruction (RT IS SENSE) with real-time CMR (RT) and segmented CMR (SEG) in a cohort that included atrial fibrillation (AF) patients. METHODS: We evaluated 27 subjects, including 11 AF patients, by acquiring steady-state free precession cine images covering the left ventricle (LV) at 1.5 T with SEG (acceleration factor 2, TR 42 ms, 1.8 × 1.8 × 6 mm3), RT (acceleration factor 3, TR 62 ms, 3.0 × 3.0 × 7 mm3), and RT IS SENSE (acceleration factor 9.9-12, TR 42 ms, 2.0 × 2.0 × 7 mm3). We performed quantitative LV functional analysis in sinus rhythm (SR) patients and qualitatively scored image quality, noise and artefact using a 5-point Likert scale in the complete cohort and AF and SR subgroups. RESULTS: There was no difference between LV functional parameters between acquisitions in SR patients. RT IS SENSE short-axis image quality was superior to SEG (4.5 ± 0.6 vs. 3.9 ± 1.1, p = 0.007) and RT (3.8 ± 0.4, p = 0.003). There was reduced artefact in RT IS SENSE compared to SEG (4.4 ± 0.6 vs. 3.8 ± 1.2, p = 0.04), driven by arrhythmia performance. RT IS SENSE short-axis image quality was superior to SEG (4.6 ± 0.5 vs. 3.1 ± 1.0, p < 0.001) in the AF subgroup. CONCLUSION: Accelerated real-time CMR with iterative sparse SENSE provides excellent clinical performance, especially in patients with AF. KEY POINTS: • Iterative sparse SENSE significantly accelerates real-time cardiovascular MRI acquisitions. • It provides excellent qualitative and quantitative performance in sinus rhythm patients. • It outperforms standard segmented acquisitions in patients with atrial fibrillation. • It improves the trade-off between temporal and spatial resolution in real-time imaging.

AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting.

Existing approaches for reconstruction of multiparametric maps with magnetic resonance fingerprinting (MRF) are currently limited by their estimation accuracy and reconstruction time. We aimed to address these issues with a novel combination of iterative reconstruction, fingerprint compression, additional regularization, and accelerated dictionary search methods. The pipeline described here, accelerated iterative reconstruction for magnetic resonance fingerprinting (AIR-MRF), was evaluated with simulations as well as phantom and in vivo scans. We found that the AIR-MRF pipeline provided reduced parameter estimation errors compared to non-iterative and other iterative methods, particularly at shorter sequence lengths. Accelerated dictionary search methods incorporated into the iterative pipeline reduced the reconstruction time at little cost of quality.

Free-breathing Sparse Sampling Cine MR Imaging with Iterative Reconstruction for the Assessment of Left Ventricular Function and Mass at 3.0 T.

Purpose To prospectively evaluate the accuracy of left ventricle (LV) analysis with a two-dimensional real-time cine true fast imaging with steady-state precession (trueFISP) magnetic resonance (MR) imaging sequence featuring sparse data sampling with iterative reconstruction (SSIR) performed with and without breath-hold (BH) commands at 3.0 T. Materials and Methods Ten control subjects (mean age, 35 years; range, 25-56 years) and 60 patients scheduled to undergo a routine cardiac examination that included LV analysis (mean age, 58 years; range, 20-86 years) underwent a fully sampled segmented multiple BH cine sequence (standard of reference) and a prototype undersampled SSIR sequence performed during a single BH and during free breathing (non-BH imaging). Quantitative analysis of LV function and mass was performed. Linear regression, Bland-Altman analysis, and paired t testing were performed. Results Similar to the results in control subjects, analysis of the 60 patients showed excellent correlation with the standard of reference for single-BH SSIR (r = 0.93-0.99) and non-BH SSIR (r = 0.92-0.98) for LV ejection fraction (EF), volume, and mass (P < .0001 for all). Irrespective of breath holding, LV end-diastolic mass was overestimated with SSIR (standard of reference: 163.9 g ± 58.9, single-BH SSIR: 178.5 g ± 62.0 [P < .0001], non-BH SSIR: 175.3 g ± 63.7 [P < .0001]); the other parameters were not significantly different (EF: 49.3% ± 11.9 with standard of reference, 48.8% ± 11.8 with single-BH SSIR, 48.8% ± 11 with non-BH SSIR; P = .03 and P = .12, respectively). Bland-Altman analysis showed similar measurement errors for single-BH SSIR and non-BH SSIR when compared with standard of reference measurements for EF, volume, and mass. Conclusion Assessment of LV function with SSIR at 3.0 T is noninferior to the standard of reference irrespective of BH commands. LV mass, however, is overestimated with SSIR. © RSNA, 2016 Online supplemental material is available for this article.

Right ventricular assessment at cardiac MRI: initial clinical experience utilizing an IS-SENSE reconstruction.

Cardiac MR is considered the gold standard in assessing RV function. The purpose of this study is to evaluate the clinical utility of an investigational iterative reconstruction algorithm in the quantitative assessment of RV function. This technique has the potential to improve the clinical utility of CMR in the evaluation of RV pathologies, particularly in patients with dyspnea, by shortening acquisition times without adversely influencing imaging performance. Segmented cine images were acquired on 9 healthy volunteers and 29 patients without documented RV pathologies using conventional GRAPPA acquisition with factor 2 acceleration (GRAPPA 2), a spatio-temporal TSENSE acquisition with factor 4 acceleration (TSENSE 4), and iteratively reconstructed Sparse SENSE acquisition with factor 4 acceleration (IS-SENSE 4). 14 subjects were re-analyzed and intraclass correlation coefficients (ICC) were calculated and Bland-Altman plots generated to assess agreement. Two independent reviewers qualitatively scored images. Comparison of acquisition techniques was performed using univariate analysis of variance (ANOVA). Differences in RV EF, BSA-indexed ESV (ESVi), BSA-indexed EDV (EDVi), and BSA-indexed SV (SVi) were shown to be statistically insignificant via ANOVA testing. R(2) values for linear regression of TSENSE 4 and IS-SENSE 4 versus GRAPPA 2 were 0.34 and 0.72 for RV-EF, and 0.61 and 0.76 for RV-EDVi. ICC values for intraobserver and interobserver quantification yielded excellent agreement, and Bland-Altman plots assessing agreement were generated as well. Qualitative review yielded small, but statistically significant differences in image quality and noise between TSENSE 4 and IS-SENSE 4. All three techniques were rated nearly artifact free. Segmented imaging acquisitions with IS-SENSE reconstruction and an acceleration factor of 4 accurately and reliably quantitates RV systolic function parameters, while maintaining image quality. TSENSE-4 accelerated acquisitions showed poorer correlation to standard imaging, and inferior interobserver and intraobserver agreement. IS-SENSE has the potential to shorten cine acquisition times by 50 %, improving imaging options in patients with intermittent arrhythmias or difficulties with breath holding.

Right Ventricular Imaging in 25 Seconds: Evaluating the Use of Sparse Sampling CINE With Iterative Reconstruction for Volumetric Analysis of the Right Ventricle.

OBJECTIVE: The aim of this study was to prospectively evaluate a 2-dimensional real-time CINE TrueFISP magnetic resonance sequence using sparse data sampling with iterative reconstruction (SSIR) for right ventricular (RV) volumetry in comparison to the criterion standard (CS) acquired at 3 T. MATERIALS AND METHODS: Ten healthy controls and 20 consecutive patients scheduled for cardiac magnetic resonance imaging on a 3-T system (Magnetom Skyra; Siemens Healthcare Sector, Germany) underwent undersampled SSIR sequences with a single breath-hold (BH) as well as with shallow free breathing (NBH) and a fully sampled multi-BH sequence as CS. Right ventricular volumetry was performed with dedicated cardiac magnetic resonance software (cvi42; Circle Cardiovascular Imaging Inc, Calgary, Alberta, Canada). Agreement of SSIR with and without BH and CS for RV functional parameters (end-systolic volume [RVESV], end-diastolic volume [RVEDV], stroke volume [RVSV], and ejection fraction [RVEF]) were assessed with Bland-Altman analysis and paired t test. RESULTS: Analysis of the 30 individuals (19 male; 48 ± 14 years) revealed no significant differences when comparing CS and BH measurements for RVEDV (153.7 vs 153.6 mL, P = 0.96), RVESV (71.6 vs 72.1 mL, P = 0.78), RVSV (82.0 vs 81.6 mL, P = 0.65), and RVEF (54.9% vs 54.2%, P = 0.19). Similar results were shown when comparing CS and NBH measurements for RVEDV (153.7 vs 152.2 mL, P = 0.34), RVESV (71.6 vs 72.8 mL, P = 0.30), RVSV (82.0 vs 81.0 mL, P = 0.46), and RVEF (54.9 vs 54.4, P = 0.48). Time taken for acquisition was 350 seconds for the CS, 34 seconds for BH, and 25 seconds for NBH measurements. Additional time required for iterative reconstruction was 2 minutes and 30 seconds for the sparse sampled data sets. CONCLUSIONS: Our results demonstrate that accurate RV volumetry with SSIR data at 3 T is feasible in clinical routine within 25 seconds even without BH, which is of particular importance in patients with dyspnea.

Highly accelerated cardiac MRI using iterative SENSE reconstruction: initial clinical experience.

To evaluate the qualitative and quantitative performance of an accelerated cardiovascular MRI (CMR) protocol that features iterative SENSE reconstruction and spatio-temporal L1-regularization (IS SENSE). Twenty consecutively recruited patients and 9 healthy volunteers were included. 2D steady state free precession cine images including 3-chamber, 4-chamber, and short axis slices were acquired using standard parallel imaging (GRAPPA, acceleration factor = 2), spatio-temporal undersampled TSENSE (acceleration factor = 4), and IS SENSE techniques (acceleration factor = 4). Acquisition times, quantitative cardiac functional parameters, wall motion abnormalities (WMA), and qualitative performance (scale: 1-poor to 5-excellent for overall image quality, noise, and artifact) were compared. Breath-hold times for IS SENSE (3.0 ± 0.6 s) and TSENSE (3.3 ± 0.6) were both reduced relative to GRAPPA (8.4 ± 1.7 s, p < 0.001). No difference in quantitative cardiac function was present between the three techniques (p = 0.89 for ejection fraction). GRAPPA and IS SENSE had similar image quality (4.7 ± 0.4 vs. 4.5 ± 0.6, p = 0.09) while, both techniques were superior to TSENSE (quality: 4.1 ± 0.7, p < 0.001). GRAPPA WMA agreement with IS SENSE was good (κ > 0.60, p < 0.001), while agreement with TSENSE was poor (κ < 0.40, p < 0.001). IS SENSE is a viable clinical CMR acceleration approach to reduce acquisition times while maintaining satisfactory qualitative and quantitative performance.

Highly undersampled contrast-enhanced MRA with iterative reconstruction: Integration in a clinical setting.

PURPOSE: To integrate, optimize, and evaluate a three-dimensional (3D) contrast-enhanced sparse MRA technique with iterative reconstruction on a standard clinical MR system. METHODS: Data were acquired using a highly undersampled Cartesian spiral phyllotaxis sampling pattern and reconstructed directly on the MR system with an iterative SENSE technique. Undersampling, regularization, and number of iterations of the reconstruction were optimized and validated based on phantom experiments and patient data. Sparse MRA of the whole head (field of view: 265 × 232 × 179 mm(3) ) was investigated in 10 patient examinations. RESULTS: High-quality images with 30-fold undersampling, resulting in 0.7 mm isotropic resolution within 10 s acquisition, were obtained. After optimization of the regularization factor and of the number of iterations of the reconstruction, it was possible to reconstruct images with excellent quality within six minutes per 3D volume. Initial results of sparse contrast-enhanced MRA (CEMRA) in 10 patients demonstrated high-quality whole-head first-pass MRA for both the arterial and venous contrast phases. CONCLUSION: While sparse MRI techniques have not yet reached clinical routine, this study demonstrates the technical feasibility of high-quality sparse CEMRA of the whole head in a clinical setting. Sparse CEMRA has the potential to become a viable alternative where conventional CEMRA is too slow or does not provide sufficient spatial resolution.

Compressed sensing single-breath-hold CMR for fast quantification of LV function, volumes, and mass.

OBJECTIVES: The purpose of this study was to compare a novel compressed sensing (CS)-based single-breath-hold multislice magnetic resonance cine technique with the standard multi-breath-hold technique for the assessment of left ventricular (LV) volumes and function. BACKGROUND: Cardiac magnetic resonance is generally accepted as the gold standard for LV volume and function assessment. LV function is 1 of the most important cardiac parameters for diagnosis and the monitoring of treatment effects. Recently, CS techniques have emerged as a means to accelerate data acquisition. METHODS: The prototype CS cine sequence acquires 3 long-axis and 4 short-axis cine loops in 1 single breath-hold (temporal/spatial resolution: 30 ms/1.5 × 1.5 mm(2); acceleration factor 11.0) to measure left ventricular ejection fraction (LVEF(CS)) as well as LV volumes and LV mass using LV model-based 4D software. For comparison, a conventional stack of multi-breath-hold cine images was acquired (temporal/spatial resolution 40 ms/1.2 × 1.6 mm(2)). As a reference for the left ventricular stroke volume (LVSV), aortic flow was measured by phase-contrast acquisition. RESULTS: In 94% of the 33 participants (12 volunteers: mean age 33 ± 7 years; 21 patients: mean age 63 ± 13 years with different LV pathologies), the image quality of the CS acquisitions was excellent. LVEF(CS) and LVEF(standard) were similar (48.5 ± 15.9% vs. 49.8 ± 15.8%; p = 0.11; r = 0.96; slope 0.97; p < 0.00001). Agreement of LVSV(CS) with aortic flow was superior to that of LVSV(standard) (overestimation vs. aortic flow: 5.6 ± 6.5 ml vs. 16.2 ± 11.7 ml, respectively; p = 0.012) with less variability (r = 0.91; p < 0.00001 for the CS technique vs. r = 0.71; p < 0.01 for the standard technique). The intraobserver and interobserver agreement for all CS parameters was good (slopes 0.93 to 1.06; r = 0.90 to 0.99). CONCLUSIONS: The results demonstrated the feasibility of applying the CS strategy to evaluate LV function and volumes with high accuracy in patients. The single-breath-hold CS strategy has the potential to replace the multi-breath-hold standard cardiac magnetic resonance technique.

Wavelet-based compressed sensing using a Gaussian scale mixture model.

While initial compressed sensing (CS) recovery techniques operated under the implicit assumption that the sparse domain coefficients are independently distributed, recent results have indicated that integrating a statistical or structural dependence model of sparse domain coefficients into CS enhances recovery. In this paper, we present a method for exploiting empirical dependences among wavelet coefficients during CS recovery using a Bayes least-square Gaussian-scale-mixture model. The proposed model is successfully incorporated into several recent CS algorithms, including reweighted l(1) minimization (RL1), iteratively reweighted least squares, and iterative hard thresholding. Extensive experiments including comparisons with a state-of-the-art model-based CS method demonstrate that the proposed algorithms are highly effective at reducing reconstruction error and/or the number of measurements required for a desired reconstruction quality.

View compensated compression of volume rendered images for remote visualization.

Remote visualization of volumetric images has gained importance over the past few years in medical and industrial applications. Volume visualization is a computationally intensive process, often requiring hardware acceleration to achieve a real time viewing experience. One remote visualization model that can accomplish this would transmit rendered images from a server, based on viewpoint requests from a client. For constrained server-client bandwidth, an efficient compression scheme is vital for transmitting high quality rendered images. In this paper, we present a new view compensation scheme that utilizes the geometric relationship between viewpoints to exploit the correlation between successive rendered images. The proposed method obviates motion estimation between rendered images, enabling significant reduction to the complexity of a compressor. Additionally, the view compensation scheme, in conjunction with JPEG2000 performs better than AVC, the state of the art video compression standard.

Efficient transmission of compressed data for remote volume visualization.

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.