Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information

Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an unseen dataset collected from different centers, vendors and disease populations. In this work, we present a random style transfer network to tackle the domain generalization problem for multi-vendor and center cardiac image segmentation. Style transfer is used to generate training data with a wider distribution/ heterogeneity, namely domain augmentation. As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains. The model can be trained in a semi-supervised manner by simultaneously optimizing a supervised segmentation and an unsupervised style translation objective. Besides, the framework incorporates the spatial information and shape prior of the target by introducing two regularization terms. We evaluated the proposed framework on 40 subjects from the M\&Ms challenge2020, and obtained promising performance in the segmentation for data from unknown vendors and centers.Comment: 11 page

Hoveyda–Grubbs II Catalyst: A Useful Catalyst for One-Pot Visible-Light-Promoted Ring Contraction and Olefin Metathesis Reactions

A one-pot reaction to synthesize functionalized 2<i>H</i>-azirines through visible-light-mediated ring contraction and olefin metathesis of isoxazoles is described. Hoveyda–Grubbs II catalyst was found to function as a photocatalyst for these transformations, allowing these processes to be carried out in a one-pot manner. This study offers a new entry for the application of Grubbs catalysts as efficient photocatalysts and the possibilities of carrying out other photoreactions and olefin metathesis in a one-pot process

Adjunctive sepsis therapy with aminophylline (STAP): a randomized controlled trial

Abstract. Background:. Sepsis is a serious disease caused by infection. Aminophylline has anti-asthma and anti-inflammatory effects. We aimed to explore the safety and effect of aminophylline in sepsis. Methods:. We conducted a clinical randomized controlled trial involving 100 patients diagnosed with sepsis within 48 h after intensive care unit (ICU) admission in two sites. All patients were randomized in a 1:1 ratio to receive standard therapy with or without aminophylline. The primary clinical outcome was all-cause mortality at 28 days. Results:. From September 27, 2018 to February 12, 2020, we screened 277 septic patients and eventually enrolled 100 patients, with 50 assigned to the aminophylline group and 50 to the usual-care group. At 28 days, 7 of 50 patients (14.0%) in the aminophylline group had died, compared with 16 of 50 (32.0%) in the usual-care group (P = 0.032). Cox regression showed that the aminophylline group had a lower hazard of death (hazard ratio = 0.312, 95% confidence interval: 0.129–0.753). Compared with the usual-care group, patients in the aminophylline group had a longer survival time (P = 0.039 by the log-rank test). The effects of aminophylline on vasopressor dose, oxygenation index, and sequential organ failure assessment score were time-dependent with treatment. There were no significant differences in total hospitalization days, ICU hospitalization days, and rates of serious adverse events (all P > 0.05). No adverse events were observed in the trial. Conclusions:. Aminophylline as an adjunct therapy could significantly reduce the risk of death and prolong the survival time of patients with sepsis. Trial registration:. ChiCTR.org.cn, ChiCTR1800019173