Impact of Epicardial Adipose Tissue on Infarct Size and Left Ventricular Systolic Function in Patients with Anterior ST-Segment Elevation Myocardial Infarction.

We aimed to assess the correlation of cardiovascular magnetic resonance (CMR)-derived epicardial adipose tissue (EAT) with infarct size (IS) and residual systolic function in ST-segment elevation myocardial infarction (STEMI). We enrolled patients discharged for a first anterior reperfused STEMI submitted to undergo CMR. EAT, left ventricular (LV) ejection fraction (LVEF), and IS were quantified at the 1-week (n = 221) and at 6-month CMR (n = 167). At 1-week CMR, mean EAT was 31 ± 13 mL/m2. Patients with high EAT volume (n = 72) showed larger 1-week IS. After adjustment, EAT extent was independently related to 1-week IS. In patients with large IS at 1 week (>30% of LV mass, n = 88), those with high EAT showed more preserved 6-month LVEF. This association persisted after adjustment and in a 1:1 propensity score-matched patient subset. Overall, EAT decreased at 6 months. In patients with large IS, a greater reduction of EAT was associated with more preserved 6-month LVEF. In STEMI, a higher presence of EAT was associated with a larger IS. Nevertheless, in patients with large infarctions, high EAT and greater subsequent EAT reduction were linked to more preserved LVEF in the chronic phase. This dual and paradoxical effect of EAT fuels the need for further research in this field.

Deep Learning-Based Predictive Model for Revascularization of Chronic Total Occlusions on Angiographic Imaging.

Revascularization of chronic total occlusions (CTO) is currently one of the most complex procedures in percutaneous coronary intervention (PCI), requiring the use of specific devices and a high level of experience to obtain good results. Once the clinical indication for extensive ischemia or angina uncontrolled with medical treatment has been established, the decision to perform coronary intervention is not simple, since this procedure has a higher rate of complications than non-PCI percutaneous intervention, higher ionizing radiation doses and a lower success rate. However, CTO revascularization has been shown to be helpful in symptomatic improvement of angina, reduction of ischemic burden, or improvement of ejection fraction. The aim of this work is to determine whether a model developed using deep learning techniques, and trained with angiography images, can better predict the likelihood of a successful revascularization procedure for a patient with a chronic total occlusion (CTO) lesion in their coronary artery (measured as procedure success and the duration of time during which X-ray imaging technology is used to perform a medical procedure) than the scales traditionally used. As a preliminary approach, patients with right coronary artery CTO will be included since they present standard angiographic projections that are performed in all patients and present less technical variability (duration, projection angle, image similarity) among them.The ultimate objective is to develop a predictive model to help the clinician in the decision to intervene and to analyze the performance in terms of predicting the success of the technique for the revascularization of chronic occlusions.Clinical Relevance- The development of a deep learning model based on the angiography images could potentially overcome the gold standard and help interventional cardiologists in the treatment decision for percutaneous coronary intervention, maximizing the success rate of coronary intervention.

Deep Learning Segmentation of Lower Extremities Radiographs for an Automatic Leg Length Discrepancy Measurement.

Leg length measurement is relevant for the early diagnostic and treatment of discrepancies as they are related with orthopedic and biomechanical changes. Simple radiology constitutes the gold standard on which radiologists perform manual lower limb measurements. It is a simple task but represents an inefficient use of their time, expertise and knowledge that could be spent in more complex labors. In this study, a pipeline for semantic bone segmentation in lower extremities radiographs is proposed. It uses a deep learning U-net model and performs an automatic measurement without consuming physicians' time. A total of 20 radiographs were used to test the methodology proposed obtaining a high overlap between manual and automatic masks with a Dice coefficient value of 0.963. The obtained Spearman's rank correlation coefficient between manual and automatic leg length measurements is statistically different from cero except for the angle of the left mechanical axis. Furthermore, there is no case in which the proposed automatic method makes an absolute error greater than 2 cm in the quantification of leg length discrepancies, being this value the degree of discrepancy from which medical treatment is required.Clinical Relevance- Leg length discrepancy measurements from X-ray images is of vital importance for proper treatment planning. This is a laborious task for radiologists that can be accelerated using deep learning techniques.

Breast Cancer Diagnosis Using Texture and Shape Features in MRI.

Contrast-enhanced magnetic resonance (MR) breast imaging represents a tool with great potential for the detection, evaluation and diagnosis of breast cancer (BC). Due to its high sensitivity and in combination with medical imaging biomarkers, it can overcome setbacks and limitations manifested in other diagnostic modalities such as mammography or ultrasound. In order to aid and assist clinicians in the diagnosis of BC, a methodology based on the extraction of 2D texture and 3D shape features in MR images is proposed. To categorize breast tumor malignancy, we considered its location in the coronal plane, divided into 4 quadrants (UOQ, UIQ, LOQ and LOQ), and the tumor type according to its genetic information (positive HER2 and Luminal B with negative HER2). In this regard, six different studies were conducted: one per feature type (texture and shape), as well as the combination of both features (texture + shape) for each of the two covariables (tumor type and location in the coronal plane). A dataset of 43 BC patients were considered. A radiomics approach was implemented extracting 43 texture and 17 shape features and using to train 5 different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naïve Bayes). The highest precision result for the tumor type study (74.04% in terms of AUC) was obtained with 43 texture features. Whereas for the quadrant localization study, the highest precision result (67.99% AUC) was obtained as a combination of 3 textures and shape features. Both results were achieved with the SVM with Linear Kernel classification model.Clinical Relevance- This work emphasizes the use of quantitative biomarkers as texture and shape features in combination with machine learning techniques to aid in breast tumor malignancy diagnosis on MR imaging. Moreover, considering the location of the tumor in the coronal plane and its type according to its genetic information may improve the selection of appropriate treatments, survival rate, and quality of life for breast cancer patients.

A Software Tool for the Measurement of the Aortic Annulus Area by Means of Computed Tomography Image Analysis for the Planning of Transcatheter Aortic Valve Replacement (TAVR).

Cardiovascular diseases (CVD) are the leading cause of death globally, being the heart valve complications one of the five most common heart problems. The aim of this study is the development of a MATLAB-based software tool to obtain several measurements derived from the aortic annulus for the planning of transcatheter aortic valve replacement (TAVR). The proposed software tool utilizes computed tomography (CT) images to reconstruct a volume of the patient. This virtual volume is rotated to situate the images in the plane which cuts the most basal points of the three aortic valve cusps, namely the aortic annulus, and obtain the required measurements. Nevertheless, the computer-user interaction will be entirely based on 2-dimension techniques to reduce both the complexity of the app and the computational load. The program was validated in CT images of 10 subjects with diagnosed aortic stenosis. A comparison of the results with the measurements used in the real clinical practice showed no significant differences between both methods.Clinical Relevance- The resulting computer tool provides significant information about the diseased aortic valve. This will allow clinicians to select the right prosthetic heart valve. It represents a cheaper and less complex alternative to sophisticated software tools which are currently being used to plan the intervention.

Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques.

The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 ≤ AUC ≤ 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 ≤ p ≤ 0.18) and were predictive of death for pre-frail and frail participants (0.001 ≤ p ≤ 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.

Alcohol-induced damage to the fimbria/fornix reduces hippocampal-prefrontal cortex connection during early abstinence.

INTRODUCTION: Alcohol dependence is characterized by a gradual reduction in cognitive control and inflexibility to contingency changes. The neuroadaptations underlying this aberrant behavior are poorly understood. Using an animal model of alcohol use disorders (AUD) and complementing diffusion-weighted (dw)-MRI with quantitative immunohistochemistry and electrophysiological recordings, we provide causal evidence that chronic intermittent alcohol exposure affects the microstructural integrity of the fimbria/fornix, decreasing myelin basic protein content, and reducing the effective communication from the hippocampus (HC) to the prefrontal cortex (PFC). Using a simple quantitative neural network model, we show how disturbed HC-PFC communication may impede the extinction of maladaptive memories, decreasing flexibility. Finally, combining dw-MRI and psychometric data in AUD patients, we discovered an association between the magnitude of microstructural alteration in the fimbria/fornix and the reduction in cognitive flexibility. Overall, these findings highlight the vulnerability of the fimbria/fornix microstructure in AUD and its potential contribution to alcohol pathophysiology. Fimbria vulnerability to alcohol underlies hippocampal-prefrontal cortex dysfunction and correlates with cognitive impairment.

Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks.

PURPOSE: There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors. METHODS: 66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different thermal predictors were extracted and analysed in three different moments (minutes 4, 5, and 6) along with the baseline time (just after the injection of a local anaesthetic around the sympathetic ganglia). Among them, the thermal variation of the ipsilateral foot and the thermal asymmetry variation between feet at each minute assessed and the starting time for each region of interest, were fed into 4 different machine learning classifiers: an Artificial Neuronal Network, K-Nearest Neighbours, Random Forest, and a Support Vector Machine. RESULTS: All classifiers presented an accuracy and specificity higher than 70%, sensitivity higher than 67%, and AUC higher than 0.73, and the Artificial Neuronal Network classifier performed the best with a maximum accuracy of 88%, sensitivity of 100%, specificity of 84% and AUC of 0.92, using 3 predictors. CONCLUSION: These results suggest thermal data retrieved from plantar feet combined with a machine learning-based methodology can be an effective tool to automatically classify LSBs performance.

Impact of Persistent Microvascular Obstruction Late After STEMI on Adverse LV Remodeling: A CMR Study.

BACKGROUND: Little is known about the occurrence and implications of persistent microvascular obstruction (MVO) after reperfused ST-segment elevation myocardial infarction (STEMI). OBJECTIVES: The authors used cardiac magnetic resonance (CMR) to characterize the impact of persistent MVO on adverse left ventricular remodeling (ALVR). METHODS: A prospective registry of 471 STEMI patients underwent CMR 7 (IQR: 5-10) days and 198 (IQR: 167-231) days after infarction. MVO (≥1 segment) and ALVR (relative increase >15% at follow-up CMR) of left ventricular end-diastolic index (LVEDVI) and left ventricular end-systolic volume index (LVESVI) were determined. RESULTS: One-week MVO occurred in 209 patients (44%) and persisted in 30 (6%). The extent of MVO (P = 0.026) and intramyocardial hemorrhage (P = 0.001) at 1 week were independently associated with the magnitude of MVO at follow-up CMR. Compared with patients without MVO (n = 262, 56%) or with MVO only at 1 week (n = 179, 38%), those with persistent MVO at follow-up (n = 30, 6%) showed higher rates of ALVR-LVEDVI (22%, 27%, and 50%; P = 0.003) and ALVR-LVESVI (20%, 21%, and 53%; P < 0.001). After adjustment, persistent MVO at follow-up (≥1 segment) was independently associated with ΔLVEDVI (relative increase, %) (P < 0.001) and ΔLVESVI (P < 0.001). Compared with a 1:1 propensity score-matched population on CMR variables made up of 30 patients with MVO only at 1 week, patients with persistent MVO more frequently displayed ALVR-LVEDVI (12% vs 50%; P = 0.003) and ALVR-LVESVI (12% vs 53%; P = 0.001). CONCLUSIONS: MVO persists in a small percentage of patients in chronic phase after STEMI and exerts deleterious effects in terms of LV remodeling. These findings fuel the need for further research on microvascular injury repair.

Clinical results of lumbar sympathetic blocks in lower limb complex regional pain syndrome using infrared thermography as a support tool.

AIM: To describe the clinical outcomes for a group of complex regional pain syndrome patients using infrared thermography as an intraprocedural support tool when undertaking fluoroscopy-guided lumbar sympathetic blocks. SUBJECTS: 27 patients with lower limb complex regional pain syndrome accompanied by severe pain and persistent functional impairment. METHODS: A series of three fluoroscopic-guided lumbar sympathetic blocks with local anesthetic and corticoids using infrared thermography as an intraprocedural support tool were performed. Clinical variables were collected at baseline, prior to each block, and one, three, and six months after blocks in a standardized checklist assessing each of the clinical categories of complex regional pain syndrome stipulated in the Budapest criteria. RESULTS: 23.75% of the blocks required more than one chance to achieve the desired thermal pattern and therefore to be considered as successful. A decrease in pain measured on a visual analogic scale was observed at all time points compared to pre-blockade data, but only 37% of the cases were categorized as responders, representing a ≥ 30% decrease in VAS, with the disappearance of pain at rest. An improvement of most of the clinical variables recorded was observed, such as tingling, edema, perception of thermal asymmetry, difference in coloring and sweating. There was a significant decrease of neuropathic pain and improvement of functional limitation. Logistic regression analysis showed the main variable to explain the probability of being a responder was immobilization time (odds ratio of 0.89). CONCLUSION: A series of fluoroscopy-guided lumbar sympathetic blocks controlled by infrared thermography in the treatment of lower limb CRPS showed a responder rate of 37%.

MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models.

Alcohol use disorder (AUD) is a complex condition representing a leading risk factor for death, disease and disability. Its high prevalence and severe health consequences make necessary a better understanding of the brain network alterations to improve diagnosis and treatment. The purpose of this study was to evaluate the potential of resting-state fMRI 3D texture features as a novel source of biomarkers to identify AUD brain network alterations following a radiomics approach. A longitudinal study was conducted in Marchigian Sardinian alcohol-preferring msP rats (N = 36) who underwent resting-state functional and structural MRI before and after 30 days of alcohol or water consumption. A cross-sectional human study was also conducted among 33 healthy controls and 35 AUD patients. The preprocessed functional data corresponding to control and alcohol conditions were used to perform a probabilistic independent component analysis, identifying seven independent components as resting-state networks. Forty-three radiomic features extracted from each network were compared using a Wilcoxon signed-rank test with Holm correction to identify the network most affected by alcohol consumption. Features extracted from this network were then used in the machine learning process, evaluating two feature selection methods and six predictive models within a nested cross-validation structure. The classification was evaluated by computing the area under the ROC curve. Images were quantized using different numbers of gray-levels to test their influence on the results. The influence of ageing, data preprocessing, and brain iron accumulation were also analyzed. The methodology was validated using structural scans. The striatal network in alcohol-exposed msP rats presented the most significant number of altered features. The radiomics approach supported this result achieving good classification performance in animals (AUC = 0.915 ± 0.100, with 12 features) and humans (AUC = 0.724 ± 0.117, with 9 features) using a random forest model. Using the structural scans, high accuracy was achieved with a multilayer perceptron in both species (animals: AUC > 0.95 with 2 features, humans: AUC > 0.82 with 18 features). The best results were obtained using a feature selection method based on the p-value. The proposed radiomics approach is able to identify AUD patients and alcohol-exposed rats with good accuracy, employing a subset of 3D features extracted from fMRI. Furthermore, it can help identify relevant networks in drug addiction.

Prognostic value of cardiac magnetic resonance early after ST-segment elevation myocardial infarction in older patients.

BACKGROUND: older patients with ST-segment elevation myocardial infarction (STEMI) represent a very high-risk population. Data on the prognostic value of cardiac magnetic resonance (CMR) in this scenario are scarce. METHODS: the registry comprised 247 STEMI patients over 70 years of age treated with percutaneous intervention and included in a multicenter registry. Baseline characteristics, echocardiographic parameters and CMR-derived left ventricular ejection fraction (LVEF, %), infarct size (% of left ventricular mass) and microvascular obstruction (MVO, number of segments) were prospectively collected. The additional prognostic power of CMR was assessed using adjusted C-statistic, net reclassification index (NRI) and integrated discrimination improvement index (IDI). RESULTS: during a 4.8-year mean follow-up, the number of first major adverse cardiac events (MACE) was 66 (26.7%): 27 all-cause deaths and 39 re-admissions for acute heart failure. Predictors of MACE were GRACE score (HR 1.03 [1.02-1.04], P < 0.001), CMR-LVEF (HR 0.97 [0.95-0.99] per percent increase, P = 0.006) and MVO (HR 1.24 [1.09-1.4] per segment, P = 0.001). Adding CMR data significantly improved MACE prediction compared to the model with baseline and echocardiographic characteristics (C-statistic 0.759 [0.694-0.824] vs. 0.685 [0.613-0.756], NRI = 0.6, IDI = 0.08, P < 0.001). The best cut-offs for independent variables were GRACE score > 155, LVEF < 40% and MVO ≥ 2 segments. A simple score (0, 1, 2, 3) based on the number of altered factors accurately predicted the MACE per 100 person-years: 0.78, 5.53, 11.51 and 78.79, respectively (P < 0.001). CONCLUSIONS: CMR data contribute valuable prognostic information in older patients submitted to undergo CMR soon after STEMI. The Older-STEMI-CMR score should be externally validated.

Detection and Classification of Myocardial Infarction Transmurality Using Cardiac MR Image Analysis and Machine Learning Algorithms.

The presence of abnormalities when the left ventricle is deformed is related to the patients' prognosis after a first myocardial infarction. These deformations can be detected by performing a cardiac magnetic resonance (CMR) study. Currently, late gadolinium enhancement (LGE) is considered to be the gold standard when performing CMR imaging. However, CMR with LGE overestimates infarct size and underestimates recovery of dysfunctional segments after myocardial infarction. Based on this statement, the objective is to detect, characterize, and quantify the extent of myocardial infarction in patients with cardiac pathologies, using parameters derived from CMR, in order to obtain greater precision in patients' recovery predictions than when only studying LGE images. For this purpose, we studied the infarct presence and extension from a total of 105 images from 35 patients, and calculated myocardium strain and torsion to characterize and quantify the affected tissue. A total of twenty-one parameters were selected to create predictive models. Moreover, we compared two feature extraction methods, and the performance of five machine learning algorithms. Results show that both temporal and strain parameters are the most relevant to detect and characterize the extent of myocardial infarction. The use of imaging techniques and machine learning algorithms have great potential and show promising results when it comes to detecting the presence and extent of myocardial infarction. The current study proposes a novel approach to detect, quantify, and characterize cardiac infarction by using strain and torsion parameters from different CMR images and different Machine Learning algorithms. This would potentially overcome LGE, the current state of the art technique, in estimating the extension of damaged tissue and enable an objective diagnosis and clinical decision.

Development of an Image-Based Methodology for the Evaluation of Histopathological Features in Human Meningioma.

Meningioma is the most common intracranial tumor in adulthood. With a clear female predominance and a recurrence rate that reaches 20%, it is, despite being considered a benign tumor, a pathology that greatly compromises post-diagnosis quality of life. Its prone to recur or progress to a higher degree is difficult to predict in the absence of obvious histological criteria. This project aims to develop an automatic methodology to aid in the diagnosis of meningiomas that is objective and easily reproducible. The methodology is based on histopathological image analysis using artificial intelligence and machine learning algorithms. It includes a semi-automatic process of identification and cleaning of the scanned samples, an automatic detection of the nuclei of each image and, finally, the parameterization of the samples. The obtained data together with the clinical information will be analyzed using statistical methods in order to provide a methodology to support clinical diagnosis and decision-making in patient management. The result is the development of an effective methodology that generates a set of data associated with morphological parameters with different trends according to the pathological groups studied. A tool has been developed that allows an effective semiautomatic analysis of the images to evaluate these parameters in an objective and reproducible way, helping in clinical decision-making and facilitating to undertake projects with large sample series. Clinical Relevance- The main contribution of this project is in the field of neuropathology, for the diagnosis of meningiomas, the most common brain tumor. The present project provides an objective and quantifiable prognosis methodology for the meningiomas, offering a more precise monitoring of the treatment applied to the patient, resulting in a better prognosis and better quality of life.

Synthetic Generation of Cardiac MR Images Combining Convolutional Variational Autoencoders and Style Transfer.

The number of studies in the medical field that uses machine learning and deep learning techniques has been increasing in the last years. However, these techniques require a huge amount of data that can be difficult and expensive to obtain. This specially happens with cardiac magnetic resonance (MR) images. One solution to the problem is raise the dataset size by generating synthetic data. Convolutional Variational Autoencoder (CVAe) is a deep learning technique which allows to generate synthetic images, but sometimes the synthetic images can be slightly blurred. We propose the combination of the CVAe technique combined with Style Transfer technique to generate synthetic realistic cardiac MR images. Clinical Relevance-The current work presents a tool to increase in a simple easy and fast way the cardiac magnetic resonance images dataset with which perform machine learning and deep learning studies.

Analysis of New Biomarkers for the Study of Schizophrenia Following a Radiomics Approach on MR and PET Imaging.

Traditionally, the diagnosis of schizophrenia was based on the psychiatrist's introspective diagnosis through clinical stratification factors and score-scales, which led to heterogeneity and discrepancy in the symptoms and results. However, there are many studies trying to improve and assist in how its diagnosis could be performed. To objectively classify schizophrenia patients it is required to determine quantitative biomarkers of the disease. In this contribution we propose a method based on feature extraction both in magnetic resonance (MR) and Positron Emission Tomography (PET) imaging. A dataset of 34 participants (17 patients and 17 control subjects) were analyzed and 5 different brain regions were studied (frontal cortex, posterior cingulate cortex, temporal cortex, primary auditory cortex and thalamus). Following a radiomics approach, 43 texture features were extracted using five different statistical methods. These features were used for the training of the five different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naive Bayes). The precision results were obtained classifying schizophrenia both in MR images (89% Area Under the Curve (AUC) in the posterior cingulate cortex) and with PET images (82% AUC in the frontal cortex), being Linear SVM and Naive Bayes the classification models with the highest predictive power. Clinical Relevance- The current study establishes a methodology to classify schizophrenia disease based on quantitative biomarkers using MR and PET images. This tool could assist the psychiatrist as an additional criterion for the diagnosis evaluation.

Multiclass Classification of Prostate Tumors Following an MR Image Analysis-Based Radiomics Approach.

Prostate cancer is one of the most common cancers in men, with symptoms that may be confused with those caused by benign prostatic hyperplasia. One of the key aspects of treating prostate cancer is its early detection, increasing life expectancy and improving the quality of life of those patients. However, the tests performed are often invasive, resulting in a biopsy. A non-invasive alternative is the magnetic resonance imaging (MRI)-based PI-RADS v2 classification. The aim of this work was to find objective biomarkers that allow the PI-RADS classification of prostate lesions using a radiomics approach on Multiparametric MRI. A total of 90 subjects were analyzed. From each segmented lesion, 609 different texture features were extracted using five different statistical methods. Two feature selection methods and eight multiclass predictive models were evaluated. This was a multiclass study in which the best AUC result was 0.7442 ± 0.0880, achieved with the Naïve Bayes model using a subset of 120 features. Valuable results were also obtained using the Random Forests model, obtaining an AUC of 0.7394 ± 0.0965 with a lower number of features (52). Clinical Relevance- The current study establishes a methodology for classifying prostate cancer and supporting clinical decision-making in a fast and efficient manner and avoiding additional invasive procedures using MRI.

Texture Analysis in MRI of the Knee for an Early Diagnosis of Osteoarthritis.

Osteoarthritis is one of the most disabling diseases in developed countries. Its etiology is not firmly established, and the diagnosis is made by observing radiographs, assigning a degree of severity based on the information displayed. For this reason, the diagnosis is usually late and determined by the subjectivity of the doctor, which implies a restriction of the treatment. Magnetic resonance imaging (MRI) has allowed us to see in greater detail the alterations produced in soft joint structures. In this work, biomarkers for an early diagnosis of knee osteoarthritis have been developed by means of textures analysis on MRI. For this purpose, 50 subjects underwent T1-weighted MR image acquisitions: 25 controls and 25 diagnosed with knee osteoarthritis between grades I and III. Six regions were segmented on these images, corresponding to the femorotibial cartilage, femoral condyles, and tibial plateau. 43 textures were extracted for each region of interest (ROI) employing 5 statistical methods and 5 different predictive models were trained and compared. In addition, a study of the thickness of the cartilage was carried out to make a comparison with the texture analysis. The best result has been obtained using a K-nearest neighbor model with the combination of 33 textures (maximum value of AUC = 0.7684). Furthermore, in the analysis of the cartilage thickness, no statistically significant differences were found. Finally, it is concluded that the texture analysis has great potential for the diagnosis of knee osteoarthritis. Clinical Relevance - The current study establishes a methodology for an early diagnosis of knee osteoarthritis by means of MRI-based texture analysis, in a fast and objective manner.

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

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.

Low-Power Lossless Data Compression for Wireless Brain Electrophysiology.

Wireless electrophysiology opens important possibilities for neuroscience, especially for recording brain activity in more natural contexts, where exploration and interaction are not restricted by the usual tethered devices. The limiting factor is transmission power and, by extension, battery life required for acquiring large amounts of neural electrophysiological data. We present a digital compression algorithm capable of reducing electrophysiological data to less than 65.5% of its original size without distorting the signals, which we tested in vivo in experimental animals. The algorithm is based on a combination of delta compression and Huffman codes with optimizations for neural signals, which allow it to run in small, low-power Field-Programmable Gate Arrays (FPGAs), requiring few hardware resources. With this algorithm, a hardware prototype was created for wireless data transmission using commercially available devices. The power required by the algorithm itself was less than 3 mW, negligible compared to the power saved by reducing the transmission bandwidth requirements. The compression algorithm and its implementation were designed to be device-agnostic. These developments can be used to create a variety of wired and wireless neural electrophysiology acquisition systems with low power and space requirements without the need for complex or expensive specialized hardware.