Examinando por Autor "Torrado-Carvajal, Angel"

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Development of a Super-Resolution Scheme for Pediatric Magnetic Resonance Brain Imaging Through Convolutional Neural Networks
(Frontiers, 2022-10-25) Molina-Maza, Juan Manuel; Galiana-Bordera, Adrian; Jimenez, Mar; Malpica, Norberto; Torrado-Carvajal, Angel
Pediatric medical imaging represents a real challenge for physicians, as children who are patients often move during the examination, and it causes the appearance of different artifacts in the images. Thus, it is not possible to obtain good quality images for this target population limiting the possibility of evaluation and diagnosis in certain pathological conditions. Specifically, magnetic resonance imaging (MRI) is a technique that requires long acquisition times and, therefore, demands the use of sedation or general anesthesia to avoid the movement of the patient, which is really damaging in this specific population. Because ALARA (as low as reasonably achievable) principles should be considered for all imaging studies, one of the most important reasons for establishing novel MRI imaging protocols is to avoid the harmful effects of anesthesia/sedation. In this context, ground-breaking concepts and novel technologies, such as artificial intelligence, can help to find a solution to these challenges while helping in the search for underlying disease mechanisms. The use of new MRI protocols and new image acquisition and/or pre-processing techniques can aid in the development of neuroimaging studies for children evaluation, and their translation to pediatric populations. In this paper, a novel super-resolution method based on a convolutional neural network (CNN) in two and three dimensions to automatically increase the resolution of pediatric brain MRI acquired in a reduced time scheme is proposed. Low resolution images have been generated from an original high resolution dataset and used as the input of the CNN, while several scaling factors have been assessed separately. Apart from a healthy dataset, we also tested our model with pathological pediatric MRI, and it successfully recovers the original image quality in both visual and quantitative ways, even for available examples of dysplasia lesions. We hope then to establish the basis for developing an innovative free-sedation protocol in pediatric anatomical MRI acquisition.
Hardware Architectures for Real-Time Medical Imaging
(MDPI, 2021-12-15) Alcaín , Eduardo; Fernández, Pedro R.; Nieto , Rubén; Montemayor , Antonio S.; Vilas , Jaime; Galiana-Bordera, Adrian; Martinez-Girones, Pedro Miguel; Prieto-de-la-Lastra, Carmen; Rodriguez-Vila , Borja; Bonet , Marina; Rodriguez-Sanchez, Cristina; Yahyaoui, Imene; Malpica , Norberto; Borromeo , Susana; Machado , Felipe; Torrado-Carvajal, Angel
Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.
Implementation of ISO/IEEE 11073 PHD SpO2 and ECG Device Specializations over Bluetooth
(MDPI, 2022-07-28) Cristobal-Huerta, Cristina; Torrado-Carvajal, Angel; Rodriguez-Sanchez, Cristina; Hernandez-Tamames, Juan A; Luaces, María; Borromeo, Susana
Current m-Health scenarios in the smart living era, as the interpretation of the smart city at each person’s level, present several challenges associated with interoperability between different clinical devices and applications. The Continua Health Alliance establishes design guidelines to standardize application communication to guarantee interoperability among medical devices. In this paper, we describe the implementation of two IEEE agents for oxygen saturation level (SpO2) measurements and electrocardiogram (ECG) data acquisition, respectively, and a smartphone IEEE manager for validation. We developed both IEEE agents over the Bluetooth Health Device Profile following the Continua guidelines and they are part of a telemonitoring system. This system was evaluated in a sample composed of 10 volunteers (mean age 29.8 +- 7.1 y/o; 5 females) under supervision of an expert cardiologist. The evaluation consisted of measuring the SpO2 and ECG signal sitting and at rest, before and after exercising for 15 min. Physiological measurements were assessed and compared against commercial devices, and our expert physician did not find any relevant differences in the ECG signal. Additionally, the system was assessed when acquiring and processing different heart rate data to prove that warnings were generated when the heart rate was under/above the thresholds for bradycardia and tachycardia, respectively.