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Objective: 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., Materials and Methods: 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)., Results: 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., Conclusions: 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., (© 2021. European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

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.

Background: 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., Purpose: To develop an efficient free-breathing and motion-robust B 1 + mapping method that allows for the investigation of spatial homogeneity of the transmitted radiofrequency field in the myocardium at 3.0T. Three joint approaches are used to adapt the dual refocusing echo acquisition mode (DREAM) sequence for cardiac applications: (1) electrocardiograph triggering; (2) a multi-snapshot undersampling scheme, which relies on the Golden Ratio, to accelerate the acquisition; and (3) motion-compensation based on low-resolution images acquired in each snapshot., Study Type: Prospective., Phantom/subjects: Eurospin II T05 system, torso phantom, and five healthy volunteers., Field Strength/sequence: 3.0T/DREAM., Assessment: The proposed method was compared with the Bloch-Siegert shift (BSS) method and validated against the standard DREAM sequence. Cardiac B 1 + maps were obtained in free-breathing and breath-hold as a proof of concept of the in vivo performance of the proposed method., Statistical Tests: Mean 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., Results: Phantom results indicated low deviation from the BSS method (mean difference = 3%). Equivalent B 1 + distributions for free-breathing and breath-hold in vivo experiments demonstrated the motion robustness of this method with good repeatability (SD < 0.05). The amount of B 1 + variations was found to be 26% over the myocardium within a short axis slice., Data Conclusion: The feasibility of a cardiac B 1 + mapping method with high spatial resolution in a reduced scan time per trigger was demonstrated. The free-breathing characteristic could be beneficial to determine shim components for multi-channel systems, currently limited to two for a single breath-hold., Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:499-507., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: T 1 mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm., Methods: After 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 T 1 value. The algorithm was validated with cardiac T 1 mapping data from 16 volunteers acquired with saturation-recovery (SR) and inversion-recovery (IR) techniques at 3T, both pre- and post-injection of a contrast agent. Signal models of T 1 relaxation with 2 and 3 parameters were tested., Results: The 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 T 1 values in SR sequences. The region-based initialization was particularly useful in IR data to reduce the variability of the blood T 1 ., Conclusions: We have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T 1 mapping with SR sequences., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Purpose: To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T 1 mapping., Materials and Methods: This study introduces a fast and robust denoising method developed for magnetic resonance T 1 mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T 1 -weighted images. The proposed approach was assessed in simulations, ex vivo data and in vivo imaging on a cohort of 16 healthy volunteers (12 males, average age 39 ± 8 years, 62 ± 9 bpm) both in pre- and postcontrast injection. The method was evaluated in myocardial T 1 mapping at 3T with a saturation-recovery technique that is accurate but sensitive to noise. ROIs in the myocardium and left-ventricle blood pool were analyzed by an experienced reader. Mean T 1 values and standard deviation were extracted and compared in all studies., Results: Simulations 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 T 1 values was observed in the myocardium (precontrast: 1433/1426 msec, 95%CI: [-40.7, 55.9], p = 0.75, postcontrast: 766/759 msec, 95%CI: [-60.7, 77.2], p = 0.8). Meanwhile, precision was improved with standard deviations of T 1 values being significantly decreased (precontrast: 223/151 msec, 95%CI: [27.3, 116.5], p = 0.003, postcontrast: 176/135 msec, 95%CI: [5.5, 77.1], p = 0.03)., Conclusion: The proposed denoising method preserves accuracy and improves precision in myocardial T 1 mapping, with the potential to offer better map visualization and analysis., Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388., (© 2017 International Society for Magnetic Resonance in Medicine.)

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).

Objective: 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. Materials and methods: 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). Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

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 (τ ) of the signal of the Look-Locker block following next inversion pulses. After using the following equation S t = A - B × exp - t + τ / T 1 ∗ , a temporal registration of τ 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. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T1 mapping.Materials and Methods: This study introduces a fast and robust denoising method developed for magnetic resonance T1 mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T1 -weighted images. The proposed approach was assessed in simulations, ex vivo data and in vivo imaging on a cohort of 16 healthy volunteers (12 males, average age 39 ± 8 years, 62 ± 9 bpm) both in pre- and postcontrast injection. The method was evaluated in myocardial T1 mapping at 3T with a saturation-recovery technique that is accurate but sensitive to noise. ROIs in the myocardium and left-ventricle blood pool were analyzed by an experienced reader. Mean T1 values and standard deviation were extracted and compared in all studies.Results: Simulations 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 T1 values was observed in the myocardium (precontrast: 1433/1426 msec, 95%CI: [-40.7, 55.9], p = 0.75, postcontrast: 766/759 msec, 95%CI: [-60.7, 77.2], p = 0.8). Meanwhile, precision was improved with standard deviations of T1 values being significantly decreased (precontrast: 223/151 msec, 95%CI: [27.3, 116.5], p = 0.003, postcontrast: 176/135 msec, 95%CI: [5.5, 77.1], p = 0.03).Conclusion: The proposed denoising method preserves accuracy and improves precision in myocardial T1 mapping, with the potential to offer better map visualization and analysis.Level Of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388. [ABSTRACT FROM AUTHOR]