Noncontrast Magnetic Resonance for the Diagnosis of Cardiac Amyloidosis.

OBJECTIVES: This study aimed to assess the diagnostic use of native T1 to detect cardiac amyloidosis (CA) in a large prospective cohort of patients referred for suspected systemic amyloidosis. BACKGROUND: CA is a progressive and fatal underdiagnosed cause of heart failure. Cardiovascular magnetic resonance (CMR) has emerged as an extremely useful test for the non-invasive diagnosis of CA, but administration of contrast is still required to make a diagnosis. METHODS: In this study, 868 patients with suspected CA referred between 2015 and 2017 underwent CMR with late gadolinium enhancement (LGE), T1 mapping, and an array of clinical investigations. RESULTS: The final diagnosis was cardiac light-chain (AL) amyloidosis in 222, cardiac transthyretin (ATTR) amyloidosis in 214, and no cardiac involvement in 427 cases. T1 was significantly elevated in both types of CA and this was associated with high diagnostic accuracy in the overall population (area under the curve, 0.93). A native T1 <1,036 ms was associated with 98% negative predictive value for CA whereas a native T1 >1,164 ms was associated with 98% positive predictive value for CA. We propose the use of these cut-offs to exclude or confirm CA and to restrict the administration of contrast only to patients with intermediate probability (native T1 between 1,036 and 1,164 ms), 58% of patients in this population. CONCLUSIONS: Native myocardial T1 enables diagnosis of CA to be made without need for gadolinium contrast in a large proportion of patients with suspected systemic amyloidosis. We propose a diagnostic algorithm for non-contrast CMR applicable to patients with suspected amyloidosis.

A workflow for patient-specific fluid-structure interaction analysis of the mitral valve: A proof of concept on a mitral regurgitation case.

The mechanics of the mitral valve (MV) are the result of the interaction of different anatomical structures complexly arranged within the left heart (LH), with the blood flow. MV structure abnormalities might cause valve regurgitation which in turn can lead to heart failure. Patient-specific computational models of the MV could provide a personalised understanding of MV mechanics, dysfunctions and possible interventions. In this study, we propose a semi-automatic pipeline for MV modelling based on the integration of state-of-the-art medical imaging, i.e. cardiac magnetic resonance (CMR) and 3D transoesophageal-echocardiogram (TOE) with fluid-structure interaction (FSI) simulations. An FSI model of a patient with MV regurgitation was implemented using the finite element (FE) method and smoothed particle hydrodynamics (SPH). Our study showed the feasibility of combining image information and computer simulations to reproduce patient-specific MV mechanics as seen on medical images, and the potential for efficient in-silico studies of MV disease, personalised treatments and device design.