B<sub>0</sub> shimming of the human heart at 7T

Hock, Michael; Terekhov, Maxim; Stefanescu, Maria R.; Lohr, David; Herz, Stefan; Reiter, Theresa; Ankenbrand, Markus J.; Kosmala, Aleksander; Gassenmaier, Tobias; Juchem, Christoph; Schreiber, Laura M.

doi:10.1002/mrm.28423

Cited by 18 publications

(16 citation statements)

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“…In practice, the aorta has a small cross-section area compared to the myocardium and, thus, for the 1D flow encoding, the B0 heterogeneity can be well managed with appropriate B0-shimming. However, data from our group showed that triggered B 0 mapping offers superior results and thus would be desirable for future studies [50].…”

Section: Plos Onementioning

confidence: 92%

On the way to routine cardiac MRI at 7 Tesla - a pilot study on consecutive 84 examinations

et al. 2021

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Introduction Cardiac magnetic resonance (CMR) at ultrahigh field (UHF) offers the potential of high resolution and fast image acquisition. Both technical and physiological challenges associated with CMR at 7T require specific hardware and pulse sequences. This study aimed to assess the current status and existing, publicly available technology regarding the potential of a clinical application of 7T CMR. Methods Using a 7T MRI scanner and a commercially available radiofrequency coil, a total of 84 CMR examinations on 72 healthy volunteers (32 males, age 19–70 years, weight 50–103 kg) were obtained. Both electrocardiographic and acoustic triggering were employed. The data were analyzed regarding the diagnostic image quality and the influence of patient and hardware dependent factors. 50 complete short axis stacks and 35 four chamber CINE views were used for left ventricular (LV) and right ventricular (RV), mono-planar LV function, and RV fractional area change (FAC). Twenty-seven data sets included aortic flow measurements that were used to calculate stroke volumes. Subjective acceptance was obtained from all volunteers with a standardized questionnaire. Results Functional analysis showed good functions of LV (mean EF 56%), RV (mean EF 59%) and RV FAC (mean FAC 52%). Flow measurements showed congruent results with both ECG and ACT triggering. No significant influence of experimental parameters on the image quality of the LV was detected. Small fractions of 5.4% of LV and 2.5% of RV segments showed a non-diagnostic image quality. The nominal flip angle significantly influenced the RV image quality. Conclusion The results demonstrate that already now a commercially available 7T MRI system, without major methods developments, allows for a solid morphological and functional analysis similar to the clinically established CMR routine approach. This opens the door towards combing routine CMR in patients with development of advanced 7T technology.

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Section: Plos Onementioning

confidence: 92%

On the way to routine cardiac MRI at 7 Tesla - a pilot study on consecutive 84 examinations

et al. 2021

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“…Another challenge of FW imaging that is even observed at 1.5T or 3T is given by ΔB 0 variations across the target region. At UHFs, however, ΔB 0 , as well as its spatial and temporal variations are larger compared with lower field strengths 46,47 . In addition, potential fluctuations in ΔB 0 as a function of the respiratory phase have been demonstrated at UHFs for the human heart, 48 showing local ΔB 0 changes of up to 100 Hz between exhale and deep inhale.…”

Section: Introductionmentioning

confidence: 93%

“…At UHFs, however, ΔB 0 , as well as its spatial and temporal variations are larger compared with lower field strengths. 46,47 In addition, potential fluctuations in ΔB 0 as a function of the respiratory phase have been demonstrated at UHFs for the human heart, 48 showing local ΔB 0 changes of up to 100 Hz between exhale and deep inhale. These respiration-induced ΔB 0 changes may have an impact on free-breathing 3D FW imaging at UHFs, especially when different respiratory phases are combined.…”

Section: Introductionmentioning

confidence: 93%

Motion‐compensated fat‐water imaging for 3D cardiac MRI at ultra‐high fields

Dietrich

Aigner

Mayer

et al. 2022

Magnetic Resonance in Med

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Purpose Respiratory motion‐compensated (MC) 3D cardiac fat‐water imaging at 7T. Methods Free‐breathing bipolar 3D triple‐echo gradient‐recalled‐echo (GRE) data with radial phase‐encoding (RPE) trajectory were acquired in 11 healthy volunteers (7M\4F, 21–35 years, mean: 30 years) with a wide range of body mass index (BMI; 19.9–34.0 kg/m2) and volunteer tailored B1+ shimming. The bipolar‐corrected triple‐echo GRE‐RPE data were binned into different respiratory phases (self‐navigation) and were used for the estimation of non‐rigid motion vector fields (MF) and respiratory resolved (RR) maps of the main magnetic field deviations (ΔB0). RR ΔB0 maps and MC ΔB0 maps were compared to a reference respiratory phase to assess respiration‐induced changes. Subsequently, cardiac binned fat‐water images were obtained using a model‐based, respiratory motion‐corrected image reconstruction. Results The 3D cardiac fat‐water imaging at 7T was successfully demonstrated. Local respiration‐induced frequency shifts in MC ΔB0 maps are small compared to the chemical shifts used in the multi‐peak model. Compared to the reference exhale ΔB0 map these changes are in the order of 10 Hz on average. Cardiac binned MC fat‐water reconstruction reduced respiration induced blurring in the fat‐water images, and flow artifacts are reduced in the end‐diastolic fat‐water separated images. Conclusion This work demonstrates the feasibility of 3D fat‐water imaging at UHF for the entire human heart despite spatial and temporal B1+ and B0 variations, as well as respiratory and cardiac motion.

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“…The second case study showed how sensitivity analysis helps deep learning-based software users to evaluate a newly trained model. More precisely, a model was demanded for segmentation of the heart in transversal ultra-high field MR images to improve B 0 shimming performance [36]. A model pre-trained on short-axis cine images at 7 T [32] was fine-tuned with very little additional data (90 images from 4 subjects).…”

Section: Case Study Ii: Model Robustnessmentioning

confidence: 99%

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

et al. 2021

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Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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B₀ shimming of the human heart at 7T

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 18 publications

References 44 publications

On the way to routine cardiac MRI at 7 Tesla - a pilot study on consecutive 84 examinations

On the way to routine cardiac MRI at 7 Tesla - a pilot study on consecutive 84 examinations

Motion‐compensated fat‐water imaging for 3D cardiac MRI at ultra‐high fields

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

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B0 shimming of the human heart at 7T

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 18 publications

References 44 publications

On the way to routine cardiac MRI at 7 Tesla - a pilot study on consecutive 84 examinations

On the way to routine cardiac MRI at 7 Tesla - a pilot study on consecutive 84 examinations

Motion‐compensated fat‐water imaging for 3D cardiac MRI at ultra‐high fields

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

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B₀ shimming of the human heart at 7T