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.

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.

Purpose: The aim of this study is to develop and optimize an adiabatic (Formula presented.) ((Formula presented.)) mapping method for robust quantification of spin-lock (SL) relaxation in the myocardium at 3T. Methods: Adiabatic SL (aSL) preparations were optimized for resilience against (Formula presented.) and (Formula presented.) inhomogeneities using Bloch simulations. Optimized (Formula presented.) -aSL, Bal-aSL and (Formula presented.) -aSL modules, each compensating for different inhomogeneities, were first validated in phantom and human calf. Myocardial (Formula presented.) mapping was performed using a single breath-hold cardiac-triggered bSSFP-based sequence. Then, optimized (Formula presented.) preparations were compared to each other and to conventional SL-prepared (Formula presented.) maps (RefSL) in phantoms to assess repeatability, and in 13 healthy subjects to investigate image quality, precision, reproducibility and intersubject variability. Finally, aSL and RefSL sequences were tested on six patients with known or suspected cardiovascular disease and compared with LGE, (Formula presented.), and ECV mapping. Results: The highest (Formula presented.) preparation efficiency was obtained in simulations for modules comprising 2 HS pulses of 30 ms each. In vivo (Formula presented.) maps yielded significantly higher quality than RefSL maps. Average myocardial (Formula presented.) values were 183.28 (Formula presented.) 25.53 ms, compared with 38.21 (Formula presented.) 14.37 ms RefSL-prepared (Formula presented.). (Formula presented.) maps showed a significant improvement in precision (avg. 14.47 (Formula presented.) 3.71% aSL, 37.61 (Formula presented.) 19.42% RefSL, p < 0.01) and reproducibility (avg. 4.64 (Formula presented.) 2.18% aSL, 47.39 (Formula presented.) 12.06% RefSL, p < 0.0001), with decreased inter-subject variability (avg. 8.76 (Formula presented.) 3.65% aSL, 51.90 (Formula presented.) 15.27% RefSL, p < 0.0001). Among aSL preparations, (Formula presented.) -aSL achieved the better inter-subject variability. In patients, (Formula presented.) -aSL preparations showed the best artifact resilience among the adiabatic preparations. (Formula presented.) times show focal alteration colocalized with areas of hyper-enhancement in the LGE images. Conclusion: Adiabatic preparations enable robust in vivo quantification of myocardial SL relaxation times at 3T.

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its extraordinary performance, nnU-Net does not supply a measure of uncertainty to indicate its possible failure. This can be problematic for large-scale image segmentation applications, where data are heterogeneous and nnU-Net may fail without notice. In this work, we introduce a novel method to estimate nnU-Net uncertainty for medical image segmentation. We propose a highly effective scheme for posterior sampling of weight space for Bayesian uncertainty estimation. Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use. Additionally, we boost the segmentation performance over the original nnU-Net via marginalizing multi-modal posterior models. We applied our method on the public ACDC and M &M datasets of cardiac MRI and demonstrated improved uncertainty estimation over a range of baseline methods. The proposed method further strengthens nnU-Net for medical image segmentation in terms of both segmentation accuracy and quality control.

Quantitative MRI (qMRI) of the heart has become an important clinical tool for examining myocardial tissue properties. Because heart is a moving object, it is usually imaged with electrocardiogram and respiratory gating during acquisition, to “freeze” its motion. In reality, gating is more-often-than-not imperfect given the heart rate variability and nonideal breath-hold. qMRI of the heart, consequently, is characteristic of varying image contrast as well as residual motion, the latter compromising the quality of quantitative mapping. Motion correction is an important step prior to parametric mapping, however, a long-standing difficulty for registering the dynamic sequence is that the contrast across frames varies wildly: depending on the acquisition scheme some frames can have extremely poor contrast, which fails both traditional optimization-based and modern learning-based registration methods. In this work, we propose a novel framework named DisQ, which Disentangles Quantitative mapping sequences into the latent space of contrast and anatomy, fully unsupervised. The disentangled latent spaces serve for the purpose of generating a series of images with identical contrast, which enables easy and accurate registration of all frames. We applied our DisQ method to the modified Look-Locker inversion recovery (MOLLI) sequence, and demonstrated improved performance of T₁ mapping. In addition, we showed the possibility of generating a dynamic series of baseline images with exactly the same shape, strictly registered and perfectly “frozen". Our proposed DisQ methodology readily extends to other types of cardiac qMRI such as T₂ mapping and perfusion.