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.

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.

While some studies have observed the beneficial impact of mergers and acquisitions (M&As) on a firm's innovation performance in developed countries, others have found the consequences to be neutral or even negative. This article develops an integrated framework to elucidate how the combination of technological relatedness and product relatedness between acquiring and target firms affects post-innovation performance of technology-driven M&As. This performance is investigated by using a set of parameters, namely R&D input, patent and product activity, and the financial results from commercialisation. We conducted case studies on China's high-tech firms derived from three diverse industry sectors, and the empirical results indicate that both types of relatedness between the partners of technology-driven M&As are conducive to the intensification of R&D expenditures. The acquisition of similar technologies and products has more significant effects on R and D input and output, and M&As without technology relatedness have better financial performance, since they lead acquirers to new technology sectors or sub-sectors. In comparison, M&As with technological complementarity and product complementarity have negative effects on related innovation processes in the short term.