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Image post-processing corrects for cardiac and respiratory motion (MoCo) during cardiovascular magnetic resonance (CMR) stress perfusion. The study analyzed its influence on visual image evaluation. Sixty-two patients with (suspected) coronary artery disease underwent a standard CMR stress perfusion exam during free-breathing. Image post-processing was performed without (non-MoCo) and with MoCo (image intensity normalization; motion extraction with iterative non-rigid registration; motion warping with the combined displacement field). Images were evaluated regarding the perfusion pattern (perfusion deficit, dark rim artifact, uncertain signal loss, and normal perfusion), the general image quality (non-diagnostic, imperfect, good, and excellent), and the reader’s subjective confidence to assess the images (not confident, confident, very confident). Fifty-three (non-MoCo) and 52 (MoCo) myocardial segments were rated as ‘perfusion deficit’, 113 vs. 109 as ‘dark rim artifacts’, 9 vs. 7 as ‘uncertain signal loss’, and 817 vs. 824 as ‘normal’. Agreement between non-MoCo and MoCo was high with no diagnostic difference per-patient. The image quality of MoCo was rated more often as ‘good’ or ‘excellent’ (92 vs. 63%), and the diagnostic confidence more often as “very confident” (71 vs. 45%) compared to non-MoCo. The comparison of perfusion images acquired during free-breathing and post-processed with and without motion correction demonstrated that both methods led to a consistent evaluation of the perfusion pattern, while the image quality and the reader’s subjective confidence to assess the images were rated more favorably for MoCo.

Purpose Because of short signal lifetimes and respiratory motion, 3D lung MRI is still challenging today. Zero-TE (ZTE) pulse sequences offer promising solutions as they overcome the issue of short T 2 ∗ . Nevertheless, as they rely on continuous readout gradients, the trajectories they follow in k-space are not adapted to retrospective gating and inferred motion correction. Theory and methods We propose AZTEK (adaptive ZTE k-space trajectories), a set of 3D radial trajectories featuring three tuning parameters, to adapt the acquisition to any moving organ while keeping seamless transitions between consecutive spokes. Standard ZTE and AZTEK trajectories were compared for static and moving phantom acquisitions as well as for human thoracic imaging performed on 3 volunteers (1 healthy and 2 patients with lung cancer). Results For the static phantom, we observe comparable image qualities with standard and AZTEK trajectories. For the moving phantom, spatially coherent undersampling artifacts observed on gated images with the standard trajectory are alleviated with AZTEK. The same improvement in image quality is obtained in human, so details are more delineated in the lung with the use of the adaptive trajectory. Conclusion The AZTEK technique opens the possibility for 3D dynamic ZTE lung imaging with retrospective gating. It enables us to uniformly sample the k-space for any arbitrary respiratory motion gate, while preserving static image quality, improving dynamic image quality and guaranteeing continuous readout gradient transitions between spokes, which makes it appropriate to ZTE.

International audience; Purpose: To correct with post-processing effects of incomplete recovery of the longitudinal magnetization before a new inversion pulse in the Modified Look-Locker Inversion recovery sequence (MOLLI) sequence.Theory and methods: We model such effects as a temporal shift ([Formula: see text]) of the signal of the Look-Locker block following next inversion pulses. After using the following equation [Formula: see text], a temporal registration of [Formula: see text] is applied to the signal of the affected block to adjust the sampling time of the recovery signal and correct the underlying effect on quantitative T1. To test our approach, simulations, phantoms, and five volunteers' data were used while applying different MOLLI sampling schemes at different heart rates and compared to the reference three-parameter fit.Results: The temporal registration of the affected signals allows to reach higher accuracy on long T1 when compared to the reference three parameters fit (10.15 vs 22.12% for T1 = 1785 ms; 8.22 vs 14.65% for T1 = 1278 ms), and lower average variation in case of rest-period deletion (62 vs 231 ms).Conclusion: The proposed approach leads to more accurate T1 in case of incomplete recovery. It is less sensitive to parameters affecting the recovery such as the rest period or the sampling scheme; and, therefore, supports multi-center studies with different MOLLI protocols.

To evaluate the feasibility and image quality of respiratory motion–resolved 4D zero echo time (ZTE) lung MRI compared with that of 3D ZTE. Our institutional review board approved this study. Twenty-one patients underwent lung scans using 3D ZTE and 4D ZTE sequences via prospective and retrospective soft gating techniques, respectively. Image qualities of 3D ZTE and 4D ZTE at end-expiration were compared through objective and subjective assessments. The quality of end-expiratory images of 3D ZTE and 4D ZTE of the two groups with different lung functions was also compared. Images were successfully acquired in all patients without any adverse events. Signal-to-noise ratios (SNRs) of lung parenchyma and thoracic structures were significantly (all p

PURPOSE: To develop a near-silent and distortionless diffusion-weighted imaging (sd-DWI) sequence using magnetization prepared rotating ultra-fast imaging sequence (RUFIS). METHODS: A rotating ultra-fast imaging sequence was modified with driven-equilibrium diffusion preparation, including eddy-current compensation methods. To compensate for the T(1) recovery during readout, a phase-cycling method was used. Both compensation methods were validated in phantoms. The optimized sequence was compared with an echo-planar imaging (EPI) diffusion sequence for image distortion, contrast, apparent diffusion coefficient (ADC) and acoustic noise level in phantoms. The sequence was evaluated in brain volunteer, five prostate volunteers, and ten pediatric patients with joint diseases. RESULTS: Combination of several eddy-current compensation methods reduced the artifact to an acceptable level. Phase-cycling reduced T(1) recovery contamination during readout. In phantom scans, the optimized sequence generated similar image contrast to the EPI diffusion sequence and ADC maps between the sequences were comparable, sd-DWI had significantly lower acoustic noise (p < 0.05). In-vivo brain scan showed reduced image distortion in sd-DWI compared to the EPI diffusion, though residual motion artifact remains due to brain pulsation. The prostate scans showed sd-DWI can provide similar ADC compared to EPI diffusion, with no image distortion. Patient scans showed the sequence can clearly depict joint lesions. CONCLUSION: A sd-DWI sequence was developed and optimized. Compared to conventional EPI diffusion, sd-DWI provided similar diffusion contrast, accurate ADC measurement, improved image quality, and minimal ambient scanning noise. The sequence showed the ability to obtain in-vivo diffusion contrast in relatively motion-free body regions, such as prostate and joint.

Respiratory motion causes misalignments between positron emission tomography (PET) and magnetic resonance (MR)-derived attenuation maps (μ-maps) in addition to artifacts on both PET and MR images in simultaneous PET/MRI for organs such as liver that can experience motion of several centimeters. To address this problem, we developed an efficient MR-based attenuation correction (MRAC) method to generate phase-matched μ-maps for quiescent period PET (PET(Q)) in abdominal PET/MRI. MRAC data was acquired with CIRcular Cartesian UnderSampling (CIRCUS) sampling during 100 s in free-breathing as an accelerated data acquisition strategy for phase-matched MRAC (MRAC(PM-CIRCUS)). For comparison, MRAC data with raster (Default) k-space sampling was also acquired during 100 s in free-breathing (MRAC(PM-Default)), and used to evaluate MRAC(PM-CIRCUS) as well as un-matched MRAC (MRAC(UM)) that was un-gated. We purposefully oversampled the MRAC(PM) data to ensure we had enough information to capture all respiratory phases to make this comparison as robust as possible. The proposed MRAC(PM-CIRCUS) was evaluated in 17 patients with (68)Ga-DOTA-TOC PET/MRI exams, suspected of having neuroendocrine tumors or liver metastases. Effects of CIRCUS sampling for accelerating a data acquisition were evaluated by simulating the data acquisition time retrospectively in increments of 5 s. Effects of MRAC(PM-CIRCUS) on PET(Q) were evaluated using uptake differences in the liver lesions (n = 35), compared to PET(Q) with MRAC(PM-Default) and MRAC(UM). A Wilcoxon signed-rank test was performed to compare lesion uptakes between the MRAC methods. MRAC(PM-CIRCUS) showed higher image quality compared to MRAC(PM-Default) for the same acquisition times, demonstrating that a data acquisition time of 30 s was reasonable to achieve phase-matched μ-maps. Lesion update differences between MRACPM-CIRCUS (30 s) versus MRAC(PM-Default) (reference, 100 s) were 0.1% ± 1.4% (range of −2.7% to 3.2%) and not significant (P > .05); while, the differences between MRAC(UM) versus MRAC(PM-Default) were 0.6% ± 11.4% with a large variation (range of −37% to 20%) and significant (P < .05). In conclusion, we demonstrated that a data acquisition of 30 s achieved phase-matched μ-maps when using specialized CIRCUS data sampling and phase-matched μ-maps improved PET(Q) quantification significantly.

International audience; Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the through-plane direction, leading to poor image quality in that dimension. To overcome this problem, super-resolution (SR) techniques have been proposed to reconstruct a single isotropic 3D volume from multiple anisotropic acquisitions. Previously, local regularization techniques such as total variation have been applied to limit noise amplification while preserving sharp edges and small features in the images. In this paper, inspired by the recent progress in patch-based reconstruction, we propose a novel isotropic 3D reconstruction scheme that integrates non-local and self-similarity information from 3D patch neighborhoods. By grouping 3D patches with similar structures, we enforce the natural sparsity of MR images, which can be expressed by a low-rank structure, leading to robust image reconstruction with high signal-to-noise ratio efficiency. An Augmented Lagrangian formulation of the problem is proposed to efficiently decompose the optimization into a low-rank volume denoising and a SR reconstruction. Experimental results in simulations, brain imaging and clinical cardiac MRI, demonstrate that the proposed joint SR and self-similarity learning framework outperforms current state-of-the-art methods. The proposed reconstruction of isotropic 3D volumes may be particularly useful for cardiac applications, such as myocardial infarction scar assessment by late gadolinium enhancement MRI.

Field inhomogeneities in MRI caused by interactions between the radiofrequency field and the patient anatomy can lead to artifacts and contrast variations, consequently degrading the overall image quality and thereby compromising diagnostic value of the images.To develop an efficient free-breathing and motion-robustProspective.Eurospin II T05 system, torso phantom, and five healthy volunteers.3.0T/DREAM.The proposed method was compared with the Bloch-Siegert shift (BSS) method and validated against the standard DREAM sequence. CardiacMean and standard deviation (SD) values were analyzed for six standard regions of interest within the myocardium. Repeatability was assessed in terms of SD and coefficient of variation.Phantom results indicated low deviation from the BSS method (mean difference = 3%). EquivalentThe feasibility of a cardiac1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:499-507.

To introduce a novel MR pulse sequence, termed Looping Star, for fast, robust, and yet quiet, 3D radial multi-gradient echo T2* MR imaging.The Looping Star pulse sequence is based on the 3D radial Rotating Ultra-Fast Imaging Sequence (RUFIS) extended by a time-multiplexed gradient-refocusing mechanism. First, multiple magnetic coherences are excited, which are subsequently gradient-refocused in form of a looping k-space trajectory. Accordingly, Looping Star captures an initial FID image followed by gradient echo images at equidistant echo times.Looping Star was demonstrated in phantom and in vivo volunteer experiments for 3D, high resolution T2* weighted imaging, T2* mapping, and quantitative susceptibility mapping (QSM). The method is fast, quiet, and robust against imperfections including Eddy currents, motion, and geometric distortions. When applied to a motor task fMRI experiment a BOLD sensitivity of 5% was achieved at minimal acoustic noise (i.e. 2.7 dB(A) above ambient noise) and with images congruent to other anatomical scans.Looping Star imaging provides new and exciting opportunities for fast, robust and yet quiet T2* MR imaging. Potential applications include T2*-weighted imaging, T2* mapping, QSM, and fMRI.

TAfter nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps. In addition, a region-based initialization is proposed to improve the initial guess of the TThe vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of TWe have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T

To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial TThis study introduces a fast and robust denoising method developed for magnetic resonance TSimulations on synthetic phantom showed signal-to-noise ratio and sharpness improvement with the proposed method in comparison with conventional denoising. In vivo results demonstrated that our method preserves accuracy, as no difference in mean TThe proposed denoising method preserves accuracy and improves precision in myocardial T3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388.

Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial T2 mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial T2 values were in good agreement with the reference values obtained from multiple breath-holds (62.5 ± 11.1 ms against 62.2 ± 11.2 ms which was not significant with p=0.49).

The investigation of three-dimensional radial, zero-echo time (TE) imaging for high-resolution, free-breathing magnetic resonance (MR) lung imaging using prospective and retrospective motion correction. Zero-TE was implemented similarly to the rotating-ultra-fast-imaging-sequence, providing 3D, isotropic, radial imaging with proton density contrast. Respiratory motion was addressed using prospective triggering (PT), prospective gating (PG) and retrospective gating (RG) with physiological signals obtained from a respiratory belt and interleaved pencil beam and DC navigators. The methods were demonstrated on four healthy volunteers at 3T. 3D, radial zero-TE imaging with high imaging bandwidth and nominally zero echo-time enables efficient capture of short-lived signals from the lung parenchyma and the vessels. Compared to Cartesian encoding, unaccounted for free-breathing respiration resulted in only benign blurring artifacts confined to the origin of motion. Breath holding froze respiration but achieved only limited image resolution (~1.8 mm, 30 s). PT and PG obtained similar quality expiratory-phase images at 1.2 mm resolution in ~6 min scan time. RG allowed multi-phase imaging in ~15 min, derived from eight individually stored averages. Zero-TE appears to be an attractive pulse sequence for 3D isotropic lung imaging. Prospective and retrospective approaches provide high-quality, free-breathing MR lung imaging within reasonable scan time.

To investigate proton density (PD)-weighted zero TE (ZT) imaging for morphological depiction and segmentation of cranial bone structures.A rotating ultra-fast imaging sequence (RUFIS) type ZT pulse sequence was developed and optimized for 1) efficient capture of short T2 bone signals and 2) flat PD response for soft-tissues. An inverse logarithmic image scaling (i.e., -log(image)) was used to highlight bone and differentiate it from surrounding soft-tissue and air. Furthermore, a histogram-based bias-correction method was developed for subsequent threshold-based air, soft-tissue, and bone segmentation.PD-weighted ZT imaging in combination with an inverse logarithmic scaling was found to provide excellent depiction of cranial bone structures. In combination with bias correction, also excellent segmentation results were achieved. A two-dimensional histogram analysis demonstrates a strong, approximately linear correlation between inverse log-scaled ZT and low-dose CT for Hounsfield units (HU) between -300 HU and 1,500 HU (corresponding to soft-tissue and bone).PD-weighted ZT imaging provides robust and efficient depiction of bone structures in the head, with an excellent contrast between air, soft-tissue, and bone. Besides structural bone imaging, the presented method is expected to be of relevance for attenuation correction in positron emission tomography (PET)/MR and MR-based radiation therapy planning.

To validate a new index, the surface-length index (SLI) based on area change in a short-axis view and length reduction in the horizontal long-axis view, which is used to quickly (1 min) detect right ventricles with an abnormal ejection fraction (EF) during a cardiac MRI examination. SLI can be used to avoid a complete delineation of the endocardial contours of normal right ventricles.Sixty patients (group A) were retrospectively included to calibrate the SLI formula by optimisation of the area under the ROC curves and SLI thresholds were chosen to obtain 100 % sensitivity. Another 340 patients (group B) were prospectively recruited to test SLI's capacity to detect right ventricles (RVs) with an abnormal EF (0.5).The appropriate threshold to obtain 100 % sensitivity in group A was 0.58. In group B, with the 0.58 threshold, SLI yielded a sensitivity of 100 % and specificity of 51 %. SLI would have saved 35 % of the RV studies in our population, without inducing any diagnostic error. SLI and EF correlation was good (r (2) = 0.64).SLI combines two simple RV measures, and brings significant improvement in post-processing efficiency by preselecting RVs that require a complete study.• Assessment of right ventricle ejection fraction (RVEF) with cine-MRI is time consuming. • Therefore, RVEF is not always assessed during cardiac MRI. • Surface-length index (SLI) allows rapid detection of abnormal RVEF during cardiac MRI. • SLI saves one third of the operator time. • Every cardiac MRI could include RVEF assessment by means of SLI.

The ability to manipulate image contrast and thus to obtain complementary information is one of the main advantages of MRI. Motion consistency within the whole data set is a key point in the context of multi contrast imaging. In cardiac and abdominal MRI, the acquisition strategy uses multiple breath-holds and often relies on acceleration methods that inherently suffer from a signal to-noise ratio loss. The aim of this work is to propose a free-breathing multi-contrast acquisition and reconstruction workflow to improve image quality and the subsequent data analysis.We extended a previously proposed motion-compensated image reconstruction method for multi-contrast imaging. Shared information throughout the imaging protocol is now exploited by the image reconstruction in the form of an additional constraint based on image gradient sparsity. This constraint helps to minimize the amount of data needed for efficient non-rigid motion correction. T₁and T₂weighted images were reconstructed from free-breathing acquisitions in 4 healthy volunteers and in a phantom. The impact of multi-contrast motion correction was evaluated in a phantom in terms of precision and accuracy of T₁and T₂quantification.In the phantom, the proposed method achieved an accuracy of 97.5 % on the quantified parameters against 88.0 % before motion correction. In volunteers, motion inconsistency in T₁and T₂quantification were noticeably reduced within 5 min of free-breathing acquisition.An efficient, free-breathing, multi-contrast imaging method has been demonstrated that does not require prior assumptions about contrast and that is applicable to a wide range of examinations.

Magnetic resonance images are affected by motion artefacts due to breathing and cardiac beating that occur during the acquisition. Methods for joint reconstruction of image and motion have been proposed recently. Such optimization problems are ill-conditioned, therefore regularization methods are required such as motion smoothness constraints using the Tikhonov method. However with Tikhonov methods the solution often relies on a good choice of the regularization parameter micron, especially in large parameter search spaces (e.g., in 3D reconstructions). In this paper, we propose an adaptive, implicit regularization method which results in subject-specific, spatially varying smoothness constraints on the motion model. It is based on the idea of solving for motion only in certain key points that form a mesh. A practical algorithm is proposed for generating this mesh automatically. The proposed method is shown to have a better convergence rate than the Tikhonov method, both in silico and in vivo. The accuracy of the reconstructed image and motion is also improved.