Cardiovascular Magnetic Resonance and prognosis in cardiac amyloidosis

Background: Cardiac involvement is common in amyloidosis and associated with a variably adverse outcome. We have previously shown that cardiovascular magnetic resonance (CMR) can assess deposition of amyloid protein in the myocardial interstitium. In this study we assessed the prognostic value of late gadolinium enhancement (LGE) and gadolinium kinetics in cardiac amyloidosis in a prospective longitudinal study.Materials and methods: The pre-defined study end point was all-cause mortality. We prospectively followed a cohort of 29 patients with proven cardiac amyloidosis. All patients underwent biopsy, 2D-echocardiography and Doppler studies, I-123-SAP scintigraphy, serum NT pro BNP assay, and CMR with a T-1 mapping method and late gadolinium enhancement (LGE).Results: Patients with were followed for a median of 623 days (IQ range 221, 1436), during which 17 (58%) patients died. The presence of myocardial LGE by itself was not a significant predictor of mortality. However, death was predicted by gadolinium kinetics, with the 2 minute post-gadolinium intramyocardial T1 difference between subepicardium and subendocardium predicting mortality with 85% accuracy at a threshold value of 23 ms (the lower the difference the worse the prognosis). Intramyocardial T1 gradient was a better predictor of survival than FLC response to chemotherapy (Kaplan Meier analysis P = 0.049) or diastolic function (Kaplan-Meier analysis P = 0.205).Conclusion: In cardiac amyloidosis, CMR provides unique information relating to risk of mortality based on gadolinium kinetics which reflects the severity of the cardiac amyloid burden

End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions

[EN] The correct assessment and characterization of heart anatomy and functionality is usually done through inspection of magnetic resonance image cine sequences. In the clinical setting it is especially important to determine the state of the left ventricle. This requires the measurement of its volume in the end-diastolic and end-systolic frames within the sequence trough segmentation methods. However, the first step required for this analysis before any segmentation is the detection of the end-systolic and end-diastolic frames within the image acquisition. In this work we present a fully convolutional neural network that makes use of dilated convolutions to encode and process the temporal information of the sequences in contrast to the more widespread use of recurrent networks that are usually employed for problems involving temporal information. We trained the network in two different settings employing different loss functions to train the network: the classical weighted cross-entropy, and the weighted Dice loss. We had access to a database comprising a total of 397 cases. Out of this dataset we used 98 cases as test set to validate our network performance. The final classification on the test set yielded a mean frame distance of 0 for the end-diastolic frame (i.e.: the selected frame was the correct one in all images of the test set) and 1.242 (relative frame distance of 0.036) for the end-systolic frame employing the optimum setting, which involved training the neural network with the Dice loss. Our neural network is capable of classifying each frame and enables the detection of the end-systolic and end-diastolic frames in short axis cine MRI sequences with high accuracy.Funding sources This work was partially supported by the Conselleria d'Innovació, Universitats, Ciència i Societat Digital, Generalitat Valenciana (grants AEST/2020/029 and AEST/2021/050) .Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Maceira, AM.; Bodi, V.; Moratal, D. (2022). End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions. Computerized Medical Imaging and Graphics. 99:1-8. https://doi.org/10.1016/j.compmedimag.2022.102085189

Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology

[EN] Background and objective: Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies directly affect this region. In order to characterize the left ventricle, it is necessary to extract its volume. In this work we present a neural network architecture that is capable of directly estimating the left ventricle volume in short axis cine Magnetic Resonance Imaging in the end-diastolic frame and provide a segmentation of the region which is the basis of the volume calculation, thus offering explain-ability to the estimated value. Methods: The network was designed to directly target the volumes to estimate, not requiring any labeled segmentation on the images. The network was based on a 3D U-net with extra layers defined in a scan-ning module that learned features like the circularity of the objects and the volumes to estimate in a weakly-supervised manner. The only targets defined were the left ventricle volumes and the circularity of the object detected through the estimation of the pi value derived from its shape. We had access to 397 cases corresponding to 397 different subjects. We randomly selected 98 cases to use as test set. Results: The results show a good match between the real and estimated volumes in the test set, with a mean relative error of 8% and a mean absolute error of 9.12 ml with a Pearson correlation coefficient of 0.95. The derived segmentations obtained by the network achieved Dice coefficients with a mean value of 0.79. Conclusions: The proposed method is capable of obtaining the left ventricle volume biomarker in the end-diastole and offer an explanation of how it obtains the result in the form of a segmentation mask without the need of segmentation labels to train the algorithm, making it a potentially more trustworthy method for clinicians and a way to train neural networks more easily when segmentation labels are not readily available.The authors acknowledge financial support from the Consel-leria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2019/037 and AEST/2020/029) , from the Agencia Valenciana de la Innovacion, Generalitat Valenciana (ref. INNCAD00/19/085) , and from the Centro para el Desarrollo Tecnologico Industrial (Programa Eurostars2, actuacion Interempresas Internacional) , Spanish Ministerio de Ciencia, Innovacion y Universidades (ref. CIIP-20192020) .Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Pérez-Pelegrí, L.; Maceira, AM.; Bodi, V.; Moratal, D. (2021). Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology. Computer Methods and Programs in Biomedicine. 208:1-8. https://doi.org/10.1016/j.cmpb.2021.106275S1820

Estudio paleomagnético del dique de Messejana-Plasencia

A paleomagnetic investigation of 39 sites (591 samples) across the 530 km ot the Messejana-Plasenciadike has been carried out. Rock magnetic experiments indicate PSD low Ti titanomagnetite and magnetite as the minerals carrying the NRM. The samples where mostly demagnetised by thermal demagnetisation. Most sites exhibit a characteristic remanent component of normal polarity with the exception of two sites, where samples with reversed polarities have been observed. The paleomagnetic pole derived from the sites is well defined, with values ot Plat=70.5, Plong=238.0, K= 47.8 and ags=3.5. Paleomagnetic data indicates: (i) the dike had a brief emplacement time, (ii) the age of intrusion can be constrained between 180-200 Ma, (Hi) the high grouping of the VGPs directions suggest no important tectonic perturbations of the whole structure of the dike since its intrusion to the present