Deformable medical image registration is traditionally formulated as an optimization problem. While classical methods solve this problem iteratively, recent learning-based approaches use weight-tied neural networks to mimic this process by unrolling the prediction of deformation fields in a fixed number of steps. However, classical methods typically converge after sufficient iterations, but learning-based unrolling methods lack a theoretical convergence guarantee and show instability empirically. In addition, unrolling methods have a practical bottleneck at training time: GPU memory usage grows linearly with the unrolling steps due to backpropagation through time (BPTT). To address both theoretical and practical challenges, we propose DEQReg, a novel registration framework based on Deep Equilibrium Models (DEQ), which formulates registration as an equilibrium-seeking problem, establishing a natural connection between classical optimization and learning-based unrolling methods. DEQReg maintains constant memory usage, enabling theoretically unlimited iteration steps. Through extensive evaluation on the public brain MRI and lung CT datasets, we show that DEQReg can achieve competitive registration performance, while substantially reducing memory consumption compared to state-of-the-art unrolling methods. We also reveal an intriguing phenomenon: the performance of existing unrolling methods first increases slightly then degrades irreversibly when the inference steps go beyond the training configuration. In contrast, DEQReg achieves stable convergence with its inbuilt equilibrium-seeking mechanism, bridging the gap between classical optimization-based and modern learning-based registration methods.

The low degree of labeling and limited photon count of fluorescent emitters in single molecule localization microscopy results in poor quality images of macro-molecular complexes. Particle fusion provides a single reconstruction with high signal-to-noise ratio by combining many single molecule localization microscopy images of the same structure. The underlying assumption of homogeneity is not always valid, heterogeneity can arise due to geometrical shape variations or distinct conformational states. We introduce a Point Cloud Variational Auto-Encoder that works directly on 2D and 3D localization data, to detect multiple modes of variation in such datasets. The computing time is on the order of a few minutes, enabled by the linear scaling with dataset size, and fast network training in just four epochs. The use of lists of localization data instead of pixelated images leads to just minor differences in computational burden between 2D and 3D cases. With the proposed method, we detected radius variation in 2D Nuclear Pore Complex data, height variations in 3D DNA origami tetrahedron data, and both radius and height variations in 3D Nuclear Pore Complex data. In all cases, the detected variations were on the few nanometer scale.

Background: As a quantitative magnetic resonance imaging (MRI) technique, myocardial T1 mapping plays a crucial role in the diagnosis and treatment of cardiovascular diseases. In practice, involuntary cardiac and respiratory motion often results in reduced accuracy and precision in T1 estimation. Therefore, image registration remains crucial for accurate and precise myocardial T1 mapping. Compared with pairwise registration that warps each baseline image to a predefined template, groupwise registration aligns all images from one sequence simultaneously without the need for a template. However, a persistent challenge is the difficulty of extracting the structural representation of T1 mapping data that contains vastly varying contrast, which severely undermines the performance of image registration. Purpose: The purpose of this study is to incorporate the learning capabilities of the diffusion model to tackle the main challenge encountered in the registration of myocardial T1 mapping. Our goal is to align all images within an image series simultaneously in a groupwise manner. Methods: In this article, we propose a novel template-free groupwise registration framework that can align one T1-weighted image series through a single forward propagation. Notably, we introduce the diffusion process to effectively boost the structural information extraction under the drastic contrast changes for reliable image registration. Furthermore, we design a Hybrid Attention Feature Fusion (HAFF) module to promote the multi-scale feature fusion from diffusion to registration. To evaluate the registration performance of the proposed model, experiments are conducted on a publicly available myocardial T1 mapping dataset comprising 210 consecutive patients, using an independent test set for comparison experiments and ablation studies. Results: Experimental results demonstrated the great superiority of our proposed method in the registration of myocardial T1 mapping. Quantitatively, the proposed method resulted in a Dice score of 0.839, groupwise Dice score of 0.601, Hausdorff distance of 10.389 mm, and T1 mapping error of 11.372 ms, surpassing the current state-of-the-art approaches. Conclusions: Our proposed framework realizes robust groupwise registration for myocardial T1 mapping by leveraging the state-of-the-art diffusion model, demonstrating its strong feature extraction capacity for image registration, beyond image generation.

In this work, we aimed to develop and evaluate multi-band outer volume suppression pulses for increased acceleration rates in simultaneous multi-slice accelerated cardiac MRI. MB-OVS pulses were constructed from a multi-band combination of two slab-selective saturation pulses and tested for various pulse shapes using Bloch simulation and phantom experiment. The MB-OVS pulses were interleaved between imaging pulses to ensure homogeneous suppression throughout the cardiac cycle/imaging window in vivo. Simultaneous multi-slice (SMS) CINE and first-pass myocardial perfusion scans with and without the proposed MB-OVS pulses were compared in terms of residual artifacts at high acceleration rates. Among the tested pulses, both Bloch simulation and phantom experiments showed that amplitude-optimized sinc pulses provided the best trade-off in suppression efficiency, the required B+ 1 , SAR, and slab profile. CINE imaging with 5-fold SMS-OVS acceleration significantly outperformed imaging without MB-OVS, maintaining leakage-free image quality, even when adding 2-fold in-plane acceleration. SMS-OVS also enabled perfusion imaging in 9 slices with 1.7 × 1.7 mm2 resolution, achieving a 16-fold spatial-only acceleration while ensuring accurate contrast dynamics without leakage artifacts. Interleaved MB-OVS modules enabled thorough leakage artifact suppression in cardiac SMS-accelerated CINE and perfusion imaging, particularly at high acceleration rates. The proposed approach may be promising for unlocking further acceleration potential of SMS in cardiac imaging.

Spin-lattice relaxation time (T1) is an important biomarker in cardiac parametric mapping for characterizing myocardial tissue and diagnosing cardiomyopathies. Conventional Modified Look-Locker Inversion Recovery (MOLLI) acquires 11 breath-hold baseline images with interleaved rest periods to ensure mapping accuracy. However, prolonged scanning can be challenging for patients with poor breathholds, often leading to motion artifacts that degrade image quality. In addition, T1 mapping requires a voxel-wise nonlinear fitting to a signal recovery model involving an iterative estimation process. Recent studies have proposed deep-learning approaches for rapid T1 mapping using shortened sequences to reduce acquisition time for patient comfort. Nevertheless, existing methods overlook important physics constraints, limiting interpretability and generalization. In this work, we present an accelerated, end-to-end T1 mapping framework leveraging Physics-Informed Neural Ordinary Differential Equations (ODEs) to model temporal dynamics and address these challenges. Our method achieves high-accuracy T1 estimation from a sparse subset of baseline images and ensures efficient null index estimation at the test time. Specifically, we develop a continuous-time LSTM-ODE model to enable selective Look-Locker (LL) data acquisition with arbitrary time lags. Experimental results show superior performance in T1 estimation for both native and post-contrast sequences and demonstrate the strong benefit of our physics-based formulation over direct data-driven T1 priors.

Pretrained segmentation models for cardiac magnetic resonance imaging (MRI) struggle to generalize across different imaging sequences due to significant variations in image contrast. These variations arise from changes in imaging protocols, yet the same fundamental spin properties, including proton density, T₁, and T₂ values, govern all acquired images. With this core principle, we introduce Reverse Imaging, a novel physics-driven method for cardiac MRI data augmentation and domain adaptation to fundamentally solve the generalization problem. Our method reversely infers the underlying spin properties from observed cardiac MRI images, by solving ill-posed nonlinear inverse problems regularized by the prior distribution of spin properties. We acquire this “spin prior” by learning a generative diffusion model from the multiparametric SAturation-recovery single-SHot acquisition sequence (mSASHA) dataset, which offers joint cardiac T₁ and T₂ maps. Our method enables approximate but meaningful spin-property estimates from MR images, which provide an interpretable “latent variable” that lead to highly flexible image synthesis of arbitrary novel sequences. We show that Reverse Imaging enables highly accurate segmentation across vastly different image contrasts and imaging protocols, realizing wide-spectrum generalization of cardiac MRI segmentation.

Cardiac T1 mapping by magnetic resonance imaging (MRI) is an important clinical tool for the diagnosis and treatment of cardiovascular diseases. In practice, involuntary cardiac and respiratory motion often results in reduced accuracy and precision in T1 estimation. Motion correction is an essential preprocessing step, however, with intensive contrast changes among baseline images, both optimization-based and deep-learning (DL)-based registration methods still struggle to estimate structural similarity between images, especially when image contrast is poor and displacement is large. In this work, we propose a novel registration metric that is highly insensitive to large contrast changes, based on modified modality independent neighborhood descriptor (mo-MIND). To accommodate severe motions, we further propose pre-deformation as an augmentation strategy at the training stage. We combine the proposed mo-MIND-based metric and the augmentation strategy in a U-Net architecture to tackle the challenges of motion correction for cardiac T1 mapping. Experimental results and ablation studies demonstrated that our method achieved improved registration performance compared to several established baselines, leading to significantly reduced T1 mapping error and improved landmark stability.

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial–temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.

Objectives: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. Materials and methods: In this multicenter retrospective study, T1 and T1ce of vestibular schwannoma (VS) patients were used to simulate low-dose T1ce with varying reductions of contrast agent dose. DL models were trained to restore standard-dose T1ce from the low-dose simulation. The image quality and segmentation performance of the DL-restored T1ce were evaluated. A head and neck radiologist was asked to rate DL-restored images in multiple aspects, including image quality and diagnostic characterization. Results: 203 MRI studies from 72 VS patients (mean age, 58.51 ± 14.73, 39 men) were evaluated. As the input dose increased, the structural similarity index measure of the restored T1ce increased from 0.639 ± 0.113 to 0.993 ± 0.009, and the peak signal-to-noise ratio increased from 21.6 ± 3.73 dB to 41.4 ± 4.84 dB. At a 10% input dose, using DL-restored T1ce for segmentation improved the Dice from 0.673 to 0.734, the 95% Hausdorff distance from 2.38 mm to 2.07 mm, and the average surface distance from 1.00 mm to 0.59 mm. Both DL-restored T1ce from 10% and 30% input doses showed excellent image quality (3, interquartile range (IQR) [Q3-Q1] = 3–3 and 3, IQR [Q3-Q1] = 4–3), with the latter being considered more informative (4, IQR [Q3-Q1] = 4–3). Conclusion: The DL model improved the image quality of low-dose MRI of the CPA cistern, which makes lesion detection and diagnostic characterization possible with 10–30% of the standard dose. Key Points: Question Deep learning models that aid in the reduction of contrast agent dose are not extensively evaluated for MRI of the cerebellopontine angle cistern. Findings Deep learning models restored the low-dose MRI of the cerebellopontine angle cistern, yielding images sufficient for vestibular schwannoma diagnosis and management. Clinical relevance Deep learning models make it possible to reduce the use of gadolinium-based contrast agents for contrast-enhanced MRI of the cerebellopontine angle cistern.

Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advances in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modeling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver for the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input.

We extensively evaluated RIIR on brain MRI, lung CT, and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only 5% of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.

Purpose: To introduce Double Inversion Recovery (DIR) preparations for myocardial Arterial Spin Labeling (myoASL) for mitigation of heart rate (HR) variability induced physiological noise (PN).

Methods: DIR-labeling was implemented for double ECG-gated myoASLsequences and compared with conventional Flow-sensitive Alternating Inversion Recovery (FAIR) labeling using single inversions. In DIR-preparations, the FAIR-inversion pulses were immediately followed by an identical reinversion pulse, applied either slice-selectively or nonselectively. Bloch-equation-based simulation and phantom experiments were performed to evaluate the PN and SNR across a range of HR variabilities. Data from six healthy subjects were acquired to evaluate myocardial blood flow (MBF), PN, and SNR in vivo.

Results: Simulation experiments showed that the averageMBFvalues remained nearly constant across the range of HR variabilities and were comparable across all three sequences. However, DIR-labeling allowed for greater recovery of the myocardial background signal, which mitigates the sensitivity to HR-dependent changes in the inversion time. Consequently, PN in the presence of HR variability was substantially reduced with DIR-labeling. For HR variabilities corresponding to the mean value observed in vivo, this resulted in a simulated SNR gain of 1.79 ± 0.90 for selective and 1.55 ± 0.77 for nonselective DIR-labeling. In vivo, DIR-labeling showed reduced PN, with 53% (p < 0.05)/44% (p = 0.16) less PN compared with conventional FAIR-myoASL, leading to an average SNR gain of 1.47 ± 0.63 (p = 0.09)/1.32 ± 0.57 (p = 0.84) with selective/nonselective reinversions.

Conclusion: The proposed DIR-preparations reduce sensitivity to HR variations and alleviate PN in double ECG-gated myoASL, improving the precision of myoASL-based perfusion quantification.

Purpose of Review: This review explores the advancements in deep learning (DL)-based cardiac magnetic resonance (CMR) reconstruction, focusing on its role in accelerating imaging, denoising, super-resolution, motion artifact correction, and quantitative mapping. It highlights the transition from parallel imaging and compressed sensing to artificial intelligence (AI)-driven approaches that enhance image quality and diagnostic accuracy. Recent Findings: Supervised and self-supervised DL models can significantly reduce scan times, enabling high-fidelity reconstructions from undersampled data. Generative adversarial network (GAN)-based super-resolution techniques enhance spatial resolution, while denoising networks improve signal-to-noise ratio. Motion correction strategies, including spatiotemporal learning, have enhanced free-breathing acquisitions. Physics-guided models incorporate MRI signal constraints for improved T1/T2 mapping and myocardial tissue characterization. Summary: DL-driven CMR reconstruction optimizes imaging speed, quality, and artifact suppression. Despite challenges in dataset standardization and clinical validation, AI is advancing real-time, high-fidelity CMR, facilitating broader clinical adoption.

This study presents a novel image-based machine learning (ML) method for automating I–V parameter extraction in gallium nitride (GaN) devices. Using Ampleon’s GEAR model, a dataset of 100000 simulated I–V curves are converted into I–V images through specifically designed transfer functions to train a convolutional neural network. The proposed method outperforms the existing ML method based on a fully connected neural network, particularly for I–V curves in the subthreshold region. Validation with measured pulse I–V data shows its superior accuracy, achieving a normalized mean square error (NMSE) of −30 dB compared with −24 dB with the existing ML method. The proposed method demonstrates a strong potential to accelerate the extraction and enhance the accuracy of GaN device modeling.

Dynamic brain data are becoming increasingly accessible, providing a gateway to understanding the inner workings of the brain in living participants. However, the size and complexity of the data pose a challenge in extracting meaningful information across various data sources. Here we introduce a generalizable unsupervised deep manifold learning for exploration of neurocognitive and behavioral patterns. Unlike existing methods that extract patterns directly from the input data, the proposed brain-dynamic convolutional-network-based embedding (BCNE) captures brain-state trajectories by analyzing temporospatial correlations within the data and applying manifold learning. The results demonstrate that BCNE effectively delineates scene transitions, underscores the involvement of different brain regions in memory and narrative processing, distinguishes dynamic learning processes and identifies differences between active and passive behaviors. BCNE provides an effective tool for exploring general neuroscience inquiries or individual-specific patterns.

Purpose: Evaluate the feasibility of quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame (RAFF2) relaxation times in the human myocardium at 3 T.

Methods: TRAFF2 mapping was performed using a breath-held ECG-gated acquisition of five images: one without preparation, three preceded by RAFF2 trains of varying duration, and one preceded by a saturation prepulse. Pixel-wise TRAFF2 maps were obtained after three-parameter exponential fitting. The repeatability of TRAFF2, T1, and T2 was assessed in phantom via the coefficient of variation (CV) across three repetitions. In seven healthy subjects, TRAFF2 was tested for precision, reproducibility, inter-subject variability, and image quality (IQ) on a Likert scale (1 = Nondiagnostic, 5 = Excellent). Additionally, TRAFF2 mapping was performed in three patients with suspected cardiovascular disease, comparing it to late gadolinium enhancement (LGE), native T1, T2, and ECV mapping.

Results: In phantom, TRAFF2 showed good repeatability (CV < 1.5%) while showing no (R2=0.09) and high (R2=0.99) correlation with T1 and T2, respectively. Myocardial TRAFF2 maps exhibited overall acceptable image quality (IQ = 3.0±1.0) with moderate artifact levels, stemming from off-resonances near the coronary sinus. Average TRAFF2 time across subjects and repetitions was 79.1 ± 7.3 ms. Good precision (7.6 ± 1.4%), reproducibility (1.0 ± 0.6%), and low inter-subject variability (10.0 ± 1.8%) were obtained. In patients, visual agreement of the infarcted area was observed in the TRAFF2 map and LGE.

Conclusion: Myocardial TRAFF2 quantification at 3 T was successfully achieved in a single breath-hold with acceptable image quality, albeit with residual off-resonance artifacts. Nonetheless, preliminary clinical data indicate potential sensitivity of TRAFF2 mapping to myocardial infarction detection without the need for contrast agents, but off-resonance artifacts mitigation warrants further investigation.

Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks to learn the image prior as the regularization term. In quantitative MRI, the physical model of nuclear magnetic resonance relaxometry is known, providing additional prior knowledge for image reconstruction. However, traditional reconstruction networks are limited to learning the spatial domain prior knowledge, ignoring the relaxometry prior. Therefore, we propose a relaxometry-guided quantitative MRI reconstruction framework to learn the spatial prior from data and the relaxometry prior from MRI physics. Additionally, we also evaluated the performance of two popular reconstruction backbones, namely, recurrent variational networks (RVN) and variational networks (VN) with U-Net. Experiments demonstrate that the proposed method achieves highly promising results in quantitative MRI reconstruction.

Background
Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The Society for Cardiovascular Magnetic Resonance (SCMR) Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine images. The goal of this study is to summarize the status of the SCMR Registry at 150,000 exams.

Methods
The processes for data security, data submission, and research access are outlined. We interrogated the Registry and presented a summary of its contents.

Results
Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (∼100 terabytes of storage). Across reported values, the human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% (63,070/145,275) female, 72% (69,766/98,008) Caucasian, and had a mortality rate of 8% (9,962/132,979). The most common indication was cardiomyopathy (35,369/131,581, 27%), and most frequently used current procedural terminology code was 75561 (57,195/162,901, 35%). Macrocyclic gadolinium-based contrast agents represented 89% (83,089/93,884) of contrast utilization after 2015. Short-axis cines were performed in 99% (76,859/77,871) of tagged scans, short-axis late gadolinium enhancement (LGE) in 66% (51,591/77,871), and stress perfusion sequences in 30% (23,241/77,871). Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction <35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct LGE, compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years.

Conclusion
The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility.

Quantitative cardiac magnetic resonance imaging (MRI) is an increasingly important diagnostic tool for cardiovascular diseases. Yet, co-registration of all baseline images within the quantitative MRI sequence is essential for the accuracy and precision of quantitative maps. However, co-registering all baseline images from a quantitative cardiac MRI sequence remains a nontrivial task because of the simultaneous changes in intensity and contrast, in combination with cardiac and respiratory motion. To address the challenge, we propose a novel motion correction framework based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, and we integrate the groupwise CNN-based registration backbone within the rPCA framework. The low-rank component of rPCA corresponds to the quantitative mapping (i.e. limited degree of freedom in variation), while the sparse component corresponds to the residual motion, making it easier to formulate and solve the groupwise registration problem. We evaluated our proposed method on cardiac T1 mapping by the modified Look-Locker inversion recovery (MOLLI) sequence, both before and after the Gadolinium contrast agent administration. Our experiments showed that our method effectively improved registration performance over baseline methods without introducing rPCA, and reduced quantitative mapping error in both in-domain (pre-contrast MOLLI) and out-of-domain (post-contrast MOLLI) inference. The proposed rPCA framework is generic and can be integrated with other registration backbones.

Background: Voltage mapping to detect ventricular scar is important for guiding catheter ablation, but the field-of-view of unipolar, bipolar, conventional, and microelectrodes as it relates to the extent of viable myocardium (VM) is not well defined. Objectives: The purpose of this study was to evaluate electroanatomic voltage-mapping (EAVM) with different-size electrodes for identifying VM, validated against high-resolution ex-vivo cardiac magnetic resonance (HR-LGE-CMR). Methods: A total of 9 swine with early-reperfusion myocardial infarction were mapped with the QDOT microcatheter. HR-LGE-CMR (0.3-mm slices) were merged with EAVM. At each EAVM point, the underlying VM in multisize transmural cylinders and spheres was quantified from ex vivo CMR and related to unipolar and bipolar voltages recorded from conventional and microelectrodes. Results: In each swine, 220 mapping points (Q1, Q3: 216, 260 mapping points) were collected. Infarcts were heterogeneous and nontransmural. Unipolar and bipolar voltage increased with VM volumes from >175 mm³ up to >525 mm³ (equivalent to a 5-mm radius cylinder with height >6.69 mm). VM volumes in subendocardial cylinders with 1- or 3-mm depth correlated poorly with all voltages. Unipolar voltages recorded with conventional and microelectrodes were similar (difference 0.17 ± 2.66 mV) and correlated best to VM within a sphere of radius 10 and 8 mm, respectively. Distance-weighting did not improve the correlation. Conclusions: Voltage increases with transmural volume of VM but correlates poorly with small amounts of VM, which limits EAVM in defining heterogeneous scar. Microelectrodes cannot distinguish thin from thick areas of subendocardial VM. The field-of-view for unipolar recordings for microelectrodes and conventional electrodes appears to be 8 to 10 mm, respectively, and unexpectedly similar.

Purpose: To develop and evaluate a robust cardiac B+1 mapping sequence at 3 T, using Bloch–Siegert shift (BSS)-based preparations.

Methods: A longitudinal magnetization preparation module was designed to encode |B+1 |. After magnetization tip-down, off-resonant Fermi pulses, placed symmetrically around two refocusing pulses, induced BSS, followed by tipping back of the magnetization. Bloch simulations were used to optimize refocusing pulse parameters and to assess the mapping sensitivity. Relaxation-induced B+1 error was simulated for various T 1 /T 2 times. The effective mapping range was determined in phantom experiments, and |B+1 | maps were compared to the conventional BSS method and subadiabatic hyperbolic-secant 8 (HS8) pulse-sensitized method. Cardiac B+1 maps were acquired in healthy subjects, and evaluated for repeatability and imaging plane intersection consistency. The technique was modified for three-dimensional (3D) acquisition of the whole heart in a single breath-hold, and compared to two-dimensional (2D) acquisition.

Results: Simulations indicate that the proposed preparation can be tailored to achieve high mapping sensitivity across various B+1 ranges, with maximum sensitivity at the upper B+1 range. T 1 /T 2 -induced bias did not exceed 5.2%. Experimentally reproduced B+1 sensitization closely matched simulations for B+1 ≥ 0.3B+1, max (mean difference 0.031±0.022, compared to 0.018±0.025 in the HS8-sensitized method), and showed 20-fold reduction in the standard deviation of repeated scans, compared with conventional BSS B+1 mapping, and an equivalent 2-fold reduction compared with HS8-sensitization. Robust cardiac B+1 map quality was obtained, with an average test-retest variability of 0.027±0.043 relative to normalized B+1 magnitude, and plane intersection bias of 0.052±0.031. 3D acquisitions showed good agreement with2D scans (mean absolute deviation 0.055±0.061).

Conclusion: BSS-based preparations enable robust and tailorable 2D/3D cardiac B+1 mapping at 3 T in a single breath-hold.