Magnetic Resonance Imaging in the Prognostic Evaluation of Patients with Pulmonary Arterial Hypertension

Rationale: Prognostication is important when counseling patients and defining treatment strategies in pulmonary arterial hypertension (PAH). Objectives: To determine the value of magnetic resonance imaging (MRI) metrics for prediction of mortality in PAH. Methods: Consecutive patients with PAH undergoing MRI were identified from the ASPIRE (Assessing the Spectrum of Pulmonary Hypertension Identified at a Referral Centre) pulmonary hypertension registry. Measurements and Main Results: During the follow-up period of 42 (range, 17–142) months 576 patients were studied and 221 (38%) died. A derivation cohort (n = 288; 115 deaths) and validation cohort (n = 288; 106 deaths) were identified. We used multivariate Cox regression and found two independent MRI predictors of death (P < 0.01): right ventricular end-systolic volume index adjusted for age and sex, and the relative area change of the pulmonary artery. A model of MRI and clinical data constructed from the derivation cohort predicted mortality in the validation cohort at 1 year (sensitivity, 70 [95% confidence interval (CI), 53–83]; specificity, 62 [95% CI, 62–68]; positive predictive value [PPV], 24 [95% CI, 16–32]; negative predictive value [NPV], 92 [95% CI, 87–96]) and at 3 years (sensitivity, 77 [95% CI, 67–85]; specificity, 73 [95% CI, 66–85]; PPV, 56 [95% CI, 47–65]; and NPV, 87 [95% CI, 81–92]). The model was more accurate in patients with idiopathic PAH at 3 years (sensitivity, 89 [95% CI, 65–84]; specificity, 76 [95% CI, 65–84]; PPV, 60 [95% CI, 46–74]; and NPV, 94 [95% CI, 85–98]). Conclusions: MRI measurements reflecting right ventricular structure and stiffness of the proximal pulmonary vasculature are independent predictors of outcome in PAH. In combination with clinical data MRI has moderate prognostic accuracy in the evaluation of patients with PAH

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven by increasing need for economic energy storage in the electric vehicle market. Electrode manufacture is the first main step in production and in an industry dominated by slurry casting, much of the manufacturing process is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding value to the electrode manufacturing value chain. Overcome the current barriers in the electrode manufacturing requires advances in material innovation, manufacturing technology, in-line process metrology and data analytics to improve cell performance, quality, safety and process sustainability. In this roadmap we present where fundamental research can impact advances in each stage of the electrode manufacturing process from materials synthesis to electrode calendering. We also highlight the role of new process technology such as dry processing and advanced electrode design supported through electrode level, physics-based modelling. To compliment this, the progresses in data driven models of full manufacturing processes is reviewed. For all the processes we describe, there is a growing need process metrology, not only to aid fundamental understanding but also to enable true feedback control of the manufacturing process. It is our hope this roadmap will contribute to this rapidly growing space and provide guidance and inspiration to academia and industry

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven by increasing need for economic energy storage in the electric vehicle market. Electrode manufacture is the first main step in production and in an industry dominated by slurry casting, much of the manufacturing process is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding value to the electrode manufacturing value chain. Overcome the current barriers in the electrode manufacturing requires advances in material innovation, manufacturing technology, in-line process metrology and data analytics to improve cell performance, quality, safety and process sustainability. In this roadmap we present where fundamental research can impact advances in each stage of the electrode manufacturing process from materials synthesis to electrode calendering. We also highlight the role of new process technology such as dry processing and advanced electrode design supported through electrode level, physics-based modelling. To compliment this, the progresses in data driven models of full manufacturing processes is reviewed. For all the processes we describe, there is a growing need process metrology, not only to aid fundamental understanding but also to enable true feedback control of the manufacturing process. It is our hope this roadmap will contribute to this rapidly growing space and provide guidance and inspiration to academia and industry