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Over the past few years, neuroimaging studies have been performed in young adults with perinatally acquired HIV (PHIV) to study the impact of HIV infection on the central nervous system (CNS), but no recent review have been published. This review aims to identify brain areas where PHIV eems to have greater impact taking into account demographic, behavioral, and clinical characteristics in PHIV infected patients. For this purpose, PubMed and Medline searches were carried out which included studies from 2010 to April 2020. We performed a systematic review and included 26 articles using structural (brain morphometry and diffusion tensor imaging) and functional magnetic resonance imaging methods involving 1182 PHIV-infected participants. Ample evidence has been provided of HIV effects on underlying brain structure. However, information recorded in the studies is commonly incomplete and results sometimes contradictory. In addition to future improvements and dissemination of tools for the developing brain MRI processing and analysis, the inclusion of data related to HIV infection itself (including clinical and immunovirological characteristics as well as detailed information about antiretroviral treatment such as age at ART initiation) may be of vital importance to the better understanding of the impact of the disease on CNS.

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.

Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, potentially introducing bias in the reconstructed PET images. The aims of this work were to develop deep learning–based methods to automatically segment air pockets and generate pseudo-CT images from CAIPIRINHA-accelerated MR Dixon images. Methods: A convolutional neural network (CNN) was trained to segment air pockets using 3-dimensional CAIPIRINHA-accelerated MR Dixon datasets from 35 subjects and was evaluated against semiautomated segmentations. A separate CNN was trained to synthesize pseudo-CT μ-maps from the Dixon images. Its accuracy was evaluated by comparing the deep learning–, model-, and CT-based μ-maps using data from 30 of the subjects. Finally, the impact of different μ-maps and air pocket segmentation methods on the PET quantification was investigated. Results: Air pockets segmented using the CNN agreed well with semiautomated segmentations, with a mean Dice similarity coefficient of 0.75. The volumetric similarity score between 2 segmentations was 0.85 ± 0.14. The mean absolute relative changes with respect to the CT-based μ-maps were 2.6% and 5.1% in the whole pelvis for the deep learning–based and model-based μ-maps, respectively. The average relative change between PET images reconstructed with deep learning–based and CT-based μ-maps was 2.6%. Conclusion: We developed a deep learning–based method to automatically segment air pockets from CAIPIRINHA-accelerated Dixon images, with accuracy comparable to that of semiautomatic segmentations. The μ-maps synthesized using a deep learning–based method from CAIPIRINHA-accelerated Dixon images were more accurate than those generated with the model-based approach available on integrated PET/MRI scanners.

The relationship between psychotic symptoms and global measures of functioning has been widely studied. No previous study has assessed so far the interplay between specific clinical symptoms and particular areas of functioning in first-episode psychosis (FEP) using network analysis methods. A total of 191 patients with FEP (age 24.45 ± 6.28 years, 64.9% male) participating in an observational and longitudinal study (AGES-CM) comprised the study sample. Functioning problems were assessed with the WHO Disability Assessment Schedule (WHODAS), whereas the Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity. Network analysis were conducted with the aim of analysing the patterns of relationships between the different dimensions of functioning and PANSS symptoms and factors at baseline. According to our results, the most important nodes were “conceptual disorganization”, “emotional withdrawal”, “lack of spontaneity and flow of conversation”, “delusions”, “unusual thought content”, “dealing with strangers” and “poor rapport”. Our findings suggest that these symptoms and functioning dimensions should be prioritized in the clinical assessment and management of patients with FEP. These areas may also become targets of future early intervention strategies, so as to improve quality of life in this population, This work was supported by the Madrid Regional Government (R&D activities in Biomedicine (grant number S2017/BMD-3740 - AGES-CM 2-CM)) and Structural Funds of the European Union. Ana Izquierdo’s work is supported by the PFIS predoctoral program (FI17/00138) from the Instituto de Salud Carlos III (Spain) and co-funded by the European Union (ERDF/ESF, "A way to make Europe”/ “Investing in your future”) and The Biomedical Research Foundation of La Princesa University Hospital. Angela Ib´a˜nez thanks the support of CIBERSAM and of the Spanish Ministry of Science, Innovation and Universities. Instituto de Salud Carlos III (PI16/00834 and PI19/01295) co-financed by ERDF Funds from the European Commission. Covadonga M. Díaz-Caneja holds a Juan Rod´es Grant from Instituto de Salud Carlos III (JR19/00024). Celso Arango was supported by the Spanish Ministry of Science and Innovation. Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/ 024), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds. European Union Seventh Framework Program under grant agreements FP7-4-HEALTH-2009-2.2.1-2-241909 (Project EU-GEI), FP7- HEALTH- 2013-2.2.1-2-603196 (Project PSYSCAN) and FP7- HEALTH-2013- 2.2.1-2-602478 (Project METSY); and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundaci´on Familia Alonso, Fundaci´on Alicia Koplowitz and Fundaci´on Mutua Madrile˜na

Social functioning is severely affected in psychotic disorders. Negative symptoms and social cognition seem to play an important role in social functioning, although the preponderance and relationship between these three domains is not clear. In this study, we sought to assess the interrelation between social cognition, social functioning, and the expressiveness and experiential factors of negative symptoms in first-episode psychosis (FEP).216 patients, participants in a multicentre study (AGES-CM), comprised our study sample. The WHO Disability Assessment Schedule (WHODAS 2.0) was used to assess functioning, whereas the Positive and Negative Schizophrenia Syndrome Scale (PANSS) was used to measure the severity of negative symptoms, and the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was applied to assess the emotional processing component of social cognition. Network analyses were conducted with the aim of analysing the patterns of relationships between social cognition, social functioning, and the expressiveness and experiential factors of negative symptoms.Our findings suggest that there is a direct relationship between social cognition and social functioning (weight = -.077), but also an indirect connection between them, mediated by the experiential (but not the expressiveness) factor of negative symptoms (weight = 0.300).The importance of the affectation of subdomains of social cognition, as well as the role of negative symptoms, specifically the experiential factor, in the functioning of patients with FEP seems to be relevant. The inclusion of these factors in prevention and treatment programs would thus allow us to reduce their impact on the social functioning of these patients.

When a seismometer network records an earthquake, operators will manually review the waveforms and identify the wave phases, a task known as phase picking. Manual phase picking is a time-consuming process that can be automated using machine learning; however, automatic methods have not yet achieved human-level performance, and open-source implementations of state-of-the-art algorithms are not always available. Convolutional networks have revolutionized the field of image processing, where the large amounts of readily available data make possible near-human performance in tasks such as classification and segmentation. Fortunately, phase picking is also an area where thousands of phases are manually picked, which makes convolutional networks a good fit for the processing of this type of data. In this paper, we describe Cospy, an open-source convolutional phase picker that uses a two-stage analysis in which the first stage segments a rough area around the phase, and the second stage regresses the precise location. Our approach was evaluated on the Northern California Earthquake Data Center (NCEDC) data set and, when targeting picks closer than 0.1 s, it achieved an $F_{1}$ -score of 93.13% for P phases and 91.07% for S phases. Our results show that convolutional networks are on track to achieve human-level performance on the task of seismic phase picking and can contribute to decreasing the need for manual analysis. An open-source implementation of the proposed approach, pretrained on the NCEDC data set, can be downloaded at https://github.com/stbnps/cospy .

Objective: The purpose of this paper is to prove that computer-vision techniques allow synthesizing water-fat separation maps for local specific absorption rate (SAR) estimation, when patient-specific water-fat images are not available. Methods: We obtained ground truth head models by using patient-specific water-fat images. We obtained two different label-fusion water-fat models generating a water-fat multiatlas and applying the STAPLE and local-MAP-STAPLE label-fusion methods. We also obtained patch-based water-fat models applying a local group-wise weighted combination of the multiatlas. Electromagnetic (EM) simulations were performed, and B1+ magnitude and 10 g averaged SAR maps were generated. Results: We found local approaches provide a high DICE overlap (72.6 ± 10.2% fat and 91.6 ± 1.5% water in local-MAP-STAPLE, and 68.8 ± 8.2% fat and 91.1 ± 1.0% water in patch-based), low Hausdorff distances (18.6 ± 7.7 mm fat and 7.4 ± 11.2 mm water in local-MAP-STAPLE, and 16.4 ± 8.5 mm fat and 7.2 ± 11.8 mm water in patch-based) and a low error in volume estimation (15.6 ± 34.4% fat and 5.6 ± 4.1% water in the local-MAP-STAPLE, and 14.0 ± 17.7% fat and 4.7 ± 2.8% water in patch-based). The positions of the peak 10 g-averaged local SAR hotspots were the same for every model. Conclusion: We have created patient-specific head models using three different computer-vision-based water-fat separation approaches and compared the predictions of B1+ field and SAR distributions generated by simulating these models. Our results prove that a computer-vision approach can be used for patient-specific water-fat separation, and utilized for local SAR estimation in high-field MRI. Significance: Computer-vision approaches can be used for patient-specific water-fat separation and for patient specific local SAR estimation, when water-fat images of the patient are not available.

Background Gadolinium-perfusion magnetic resonance (MR) identifies gray matter abnormalities in early multiple sclerosis (MS), even in the absence of structural differences. These perfusion changes could be related to the cognitive disability of these patients, especially in the working memory. Arterial spin labeling (ASL) is a relatively recent perfusion technique that does not require intravenous contrast, making the technique especially attractive for clinical research. Purpose To verify the perfusion alterations in early MS, even in the absence of cerebral volume changes. To introduce the ASL sequence as a suitable non-invasive method in the monitoring of these patients. Material and Methods Nineteen healthy controls and 28 patients were included. The neuropsychological test EDSS and SDMT were evaluated. Cerebral blood flow and bolus arrival time were collected from the ASL study. Cerebral volume and cortical thickness were obtained from the volumetric T1 sequence. Spearman’s correlation analyzed the correlation between EDSS and SDMT tests and perfusion data. Differences were considered significant at a level of P

Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports a 0.9367 ± 0.0138 (mean ± SD) and 95% confidence interval (0.9221, 0.9512), the experimental Mean Absolute Error (MAE) reports 73.9149 ± 9.2101 HU and 95% confidence interval (66.3383, 81.4915), the Structural Similarity Index Measure (SSIM) reports 0.9943 ± 0.0009 and 95% confidence interval (0.9935, 0.9951), and the experimental Dice coefficient for bone tissue reports 0.7051 ± 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield Units (m = 0.87, adjusted R2 = 0.91, p &lt, 0.001). The Bland–Altman plot shows that the average of the differences is low (−38.6471 ± 199.6100, 95% CI (−429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.

The synthesis of patient data of a certain medical image modality by applying an image processing pipeline starting from other modality is receiving a lot of interest recently, as it allows to save acquisition time and sometimes avoid radiation to the patient. An example of this is the creation of computerized tomography volumes from magnetic resonance imaging data, which can be useful for several applications such as electromagnetic simulations, cranial morphometry and attenuation correction in PET/MR systems. We present a fast patch-based algorithm for this purpose, implemented using graphics processing unit computing techniques and gaining up to $$\times$$×15.9 of speedup against a multicore CPU solution and up to about $$\times$$×75 against a single core CPU solution.

Whole-body attenuation correction (AC) is still challenging in combined PET/MR scanners. We describe Dixon-VIBE Deep Learning (DIVIDE), a deep-learning network that allows synthesizing pelvis pseudo-CT maps based only on the standard Dixon volumetric interpolated breath-hold examination (Dixon-VIBE) images currently acquired for AC in some commercial scanners. Methods: We propose a network that maps between the four 2-dimensional (2D) Dixon MR images (water, fat, in-phase, and out-of-phase) and their corresponding 2D CT image. In contrast to previous methods, we used transposed convolutions to learn the up-sampling parameters, we used whole 2D slices to provide context information, and we pretrained the network with brain images. Twenty-eight datasets obtained from 19 patients who underwent PET/CT and PET/MR examinations were used to evaluate the proposed method. We assessed the accuracy of the μ-maps and reconstructed PET images by performing voxel- and region-based analysis comparing the SUVs (in g/mL) obtained after AC using the Dixon-VIBE (PET(Dixon)), DIVIDE (PET(DIVIDE)), and CT-based (PET(CT)) methods. Additionally, the bias in quantification was estimated in synthetic lesions defined in the prostate, rectum, pelvis, and spine. Results: Absolute mean relative change values relative to CT AC were lower than 2% on average for the DIVIDE method in every region of interest except for bone tissue, where it was lower than 4% and 6.75 times smaller than the relative change of the Dixon method. There was an excellent voxel-by-voxel correlation between PET(CT) and PET(DIVIDE) (R(2) = 0.9998, P < 0.01). The Bland–Altman plot between PET(CT) and PET(DIVIDE) showed that the average of the differences and the variability were lower (mean PET(CT)–PET(DIVIDE) SUV, 0.0003; PET(CT)–PET(DIVIDE) SD, 0.0094; 95% confidence interval, [−0.0180,0.0188]) than the average of differences between PET(CT) and PET(Dixon) (mean PET(CT)–PET(Dixon) SUV, 0.0006; PET(CT)–PET(Dixon) SD, 0.0264; 95% confidence interval, [−0.0510,0.0524]). Statistically significant changes in PET data quantification were observed between the 2 methods in the synthetic lesions, with the largest improvement in femur and spine lesions. Conclusion: The DIVIDE method can accurately synthesize a pelvis pseudo-CT scan from standard Dixon-VIBE images, allowing for accurate AC in combined PET/MR scanners. Additionally, our implementation allows rapid pseudo-CT synthesis, making it suitable for routine applications and even allowing retrospective processing of Dixon-VIBE data.

PURPOSE: MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. METHODS: The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. RESULTS: The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. CONCLUSION: It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

Fetal health is critically dependent on placental function, especially placental transport of oxygen from mother to fetus. When fetal growth is compromised, placental insufficiency must be distinguished from modest genetic growth potential. If placental insufficiency is present, the physician must trade off the risk of prolonged fetal exposure to placental insufficiency against the risks of preterm delivery. Current ultrasound methods to evaluate the placenta are indirect and insensitive. We propose to use Blood-Oxygenation-Level-Dependent (BOLD) MRI with maternal hyperoxia to quantitatively assess mismatch in placental function in seven monozygotic twin pairs naturally matched for genetic growth potential. In-utero BOLD MRI time series were acquired at 29 to 34 weeks gestational age. Maps of oxygen Time-To-Plateau (TTP) were obtained in the placentas by voxel-wise fitting of the time series. Fetal brain and liver volumes were measured based on structural MR images. After delivery, birth weights were obtained and placental pathological evaluations were performed. Mean placental TTP negatively correlated with fetal liver and brain volumes at the time of MRI as well as with birth weights. Mean placental TTP positively correlated with placental pathology. This study demonstrates the potential of BOLD MRI with maternal hyperoxia to quantify regional placental function in vivo.

European and Spanish legislation set hourly limits for Nitrogen Dioxide, \(NO_2\), that are enforced with traffic restrictions. In this context it is important to warn the citizens in advance, which can only be done if the \(NO_2\) levels are forecasted. In this paper we propose a deep learning based air quality forecasting system that uses air quality and meteorological data to produce \(NO_2\) forecasts up to 24 h with a root mean squared error, RMSE, of 10.54 \(\upmu {\text {g}}/{\text {m}}^3\). We also compare our results with the model based system CALIOPE.

Reconstruction of source neural activity has an increasing importance due to its high time resolution, promoting its application in diagnosis of neurodegenerative and cognitive tasks. To improve the accuracy of reconstruction, we study the influence of anisotropic blood vessels in the EEG-based source localization solution, including several tissues. To this end, we develop a model that reflects physical properties of the head volume based on collected angiographic data. From obtained results of real data, we find that omission of the anisotropic blood vessels within the forward modeling may result in potential discrepancies larger than 35 \(\upmu \)V and dipole localization errors greater than 15 mm, especially, in deep brain areas.

Eyelid identification provides key data that can be used in several application such as controlling gaze-based HMIs (human machine interfaces), the design of new diagnostic tools for brain diseases, improving driver safety, drowsiness detection, research on advertisement, etc. We propose a novel eyetracking algorithm by learning a deep deconvolutional neural network. To train and test our method, we use several data sets with hand-labeled eye images from real-world tasks. Our method outperforms previous eye tracking methods, improving the results of the current state of the art in a 19%.

Purpose Specific absorption rate (SAR) amplification around active implantable medical devices during diagnostic MRI procedures poses a potential risk for patient safety. In this study, we present a parallel transmit (pTx) strategy that can be used to safely scan patients with deep brain stimulation (DBS) implants. Methods We performed electromagnetic simulations at 3T using a uniform phantom and a multitissue realistic head model with a generic DBS implant. Our strategy is based on using implant-friendly modes, which are defined as the modes of an array that reduce the local SAR around the DBS lead tip. These modes are used in a spokes pulse design algorithm in order to produce highly uniform magnitude least-squares flip angle excitations. Results Local SAR (1 g) at the lead tip is reduced below 0.1 W/kg compared with 31.2 W/kg, which is obtained by a simple quadrature birdcage excitation without any sort of SAR mitigation. For the multitissue realistic head model, peak 10 g local SAR and global SAR are obtained as 4.52 W/kg and 0.48 W/kg, respectively. A uniform axial flip angle is also obtained (NRMSE

Purpose Local specific absorption rate (SAR) limits many applications of parallel transmit (pTx) in ultra high-field imaging. In this Note, we introduce the use of an array element, which is intentionally inefficient at generating spin excitation (a “dark mode”) to attempt a partial cancellation of the electric field from those elements that do generate excitation. We show that adding dipole elements oriented orthogonal to their conventional orientation to a linear array of conventional loop elements can lower the local SAR hotspot in a C-spine array at 7 T. Methods We model electromagnetic fields in a head/torso model to calculate SAR and excitation B1+ patterns generated by conventional loop arrays and loop arrays with added electric dipole elements. We utilize the dark modes that are generated by the intentional and inefficient orientation of dipole elements in order to reduce peak 10g local SAR while maintaining excitation fidelity. Results For B1+ shimming in the spine, the addition of dipole elements did not significantly alter the B1+ spatial pattern but reduced local SAR by 36%. Conclusion The dipole elements provide a sufficiently complimentary B1+ and electric field pattern to the loop array that can be exploited by the radiofrequency shimming algorithm to reduce local SAR. Magn Reson Med 73:1533–1539, 2015. © 2014 Wiley Periodicals, Inc.

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called “cell astronomy” systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

The pathogenesis of orthostatic tremor (OT) remains unclear, although some evidence points to dysfunction in the brainstem or cerebellum. We used single voxel proton magnetic resonance spectroscopy (1H-MRS) (3 T) to investigate whether neurochemical changes underlie abnormal cerebellar or cortical function in OT. Fourteen OT patients and 14 healthy controls underwent 1H-MRS studies with voxels placed in midparietal gray matter and cerebellum (vermis and central white matter). Spectral analysis was analyzed using the software package LCModel (version 6.3). The absolute metabolite concentrations and ratios of total N-acetylaspartate + N-acetylaspartyl glutamate (NAA), choline-containing compounds, myoinositol, and glutamate + glutamine to creatine were calculated. In midparietal gray matter spectra, we found a significant decrease in the absolute concentration of NAA in OT patients versus healthy controls (7.76 ± 0.25 vs 8.11 ± 0.45, P = 0.017). A similar decrease in NAA was seen in the cerebellar vermis (7.33 ± 0.61 vs 8.55 ± 1.54, P = 0.014) and cerebellar white matter (8.54 ± 0.79 vs 9.95 ± 1.57, P = 0.010). No differences in the other metabolites or their ratios were observed. Reductions in both cerebral cortical and cerebellar NAA suggest that there is neuronal damage or loss in OT, raising the intriguing question as to whether OT is a neurodegenerative disease. Along with clinical history and electrophysio0logical examination, 1H-MRS could serve as a useful diagnostic aid for OT.

Cardiovascular disease is one of the leading causes of death worldwide. Epicardial adipose tissue (EAT) has emerged as an independent predictor of high cardiometabolic risk. Cardiovascular MRI has proven to be a feasible and reproducible method to assess EAT quantitatively. We present a novel approach for the automated quantification of EAT using "a priori" anatomical information. We extracted a region of interest (ROI) in the end-diastolic heart phase followed by a GVF-snake algorithm to smooth it. For the EAT and endocardial boundary detection, a Law's texture filter is applied. Left and right ventricle are localized using spatial prior information. Then, thresholding is applied to quantify the cardiac muscle. For the EAT, it is differentiated from the paracardial fat by K-cosine curvature analysis. Results for 10 morbidly obese patients show no significant differences between manual and automatic quantification with a remarkable time and effort saving between them.

MRI-based bone segmentation is a challenging task because bone tissue and air both present low signal intensity on MR images, making it difficult to accurately delimit the bone boundaries. However, estimating bone from MRI images may allow decreasing patient ionization by removing the need of patient-specific CT acquisition in several applications. In this work, we propose a fast GPU-based pseudo-CT generation from a patient-specific MRI T1-weighted image using a group-wise patch-based approach and a limited MRI and CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel with the patches of all MR images in the database, which lie in a certain anatomical neighborhood. The pseudo-CT is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a GPU. The use of patch-based techniques allows a fast and accurate estimation of the pseudo-CT from MR T1-weighted images, with a similar accuracy as the patient-specific CT. The experimental normalized cross correlation reaches 0.9324±0.0048 for an atlas with 10 datasets. The high NCC values indicate how our method can accurately approximate the patient-specific CT. The GPU implementation led to a substantial decrease in computational time making the approach suitable for real applications.

Impairment of the olfactory sensibility can be an indicator of neurode generative disorders, such as Alzheimer's disease and Parkinson's disease. The problem lies in obtaining an objective quantitative analysis of olfactory response. For this task, we will use functional magnetic resonance imaging (fMRI) and a device that will provide a selective and controlled stimulation of the olfactory system. The novel issues of our design are synchronization between the acquisition and the olfactory task, and automated control of experimental parameters, odorants sequences, and frequency. We present a review of the basic fMRI experimental design of event-related stimulus paradigms versus block design experiments, and their use in olfactory experiments. Finally, we present the preliminary results obtained on a real 3-T magnetic resonance imaging (MRI) scanner.

136 Objectives It is largely recognized that tumor microenvironment influences the efficacy of therapy and affects the outcome of different neoplasia. Therefore, we investigated the impact of metabolic information on FDG-PET in resectable non-small cell lung cancer (NSCLC), by comparing the imaging findings with the tissue expression of immune-checkpoints and other markers of tumor-related immunity. Methods All patients referred to our Institution for upfront surgical resection of NSCLC, who were investigated with FDG-PET prior to surgery (n=130) were consecutively enrolled in the study. From January 2010 to May 2014, 55 patients (stage IA-IIIB; M:F=42:13; mean age 68.9 years) were investigated. Sampled surgical tumor specimens were analyzed with immunohistochemistry (IHC) for CD68-TAMs (tumor-associated macrophages), CD8-TILs (tumor infiltrating Lymphocytes), PD-1 and PD-L1. The immunoreactivity was quantitatively evaluated and the scores compared with imaging findings. FDG-PET images were visualized on a GE Adw4.6 workstation and used to define semi-quantitative parameters (cut-off threshold 42%): i.e. SUVmax and SUVmean. Metabolic information on FDG-PET were correlated with tissue markers expression, and both with disease-free survival (DFS) considering a median follow-up of 16.2 months. Results Overall, 36 adenocarcinomas (ADK), 18 squamous cell carcinomas (SCCs), and 1 sarcomatoid carcinoma were analyzed. All investigated tumors resulted positive at FDG-PET: median SUVmax 11.3 (range: 2.3-32.5) and SUVmean 6.4 (range: 1.5-13), both significantly higher in SCCs compared to other histotypes (p=0.007 and 0.048, respectively). IHC demonstrated a median immunoreactive area covered by CD68-TAMs of 5.41% (range: 0.84-14.01%), CD8-TILs of 2.9% (range: 0.11-11.92%), PD-1 of 0.65% (range: 0.02-5.87%), and PD-L1 of 0.7% (range: 0.03-10.29%). We found a statistically significant correlation between SUVmax and SUVmean with the expression of CD68-TAMs (rho=0.31; p=0.027), CD8 TILs (rho=0.55; p>0.001), and PD-1 (rho=0.33; p=0.017 and 0.009 respectively). The other tissue markers correlated as follows: CD8 TILs and PD-1 (rho=0.45; p=0.001), CD8 TILs and PD-L1 (rho=0.41; p=0.003), CD68-TAMs and PD-L1 (rho=0.30; p=0.027), PD-1 and PD-L1 (rho=0.26; p=0.059). With respect to the outcome, only SUVmax, SUVmean and stage showed a statistically significant correlation with DFS (p=0.002, 0.004 and Conclusions The present research shows a direct association between metabolic parameters on FDG-PET and the tumor expression of immune-checkpoints and other markers of tumor-related immunity, such as PD-1 and PD-L1, CD8-TILs, and CD68-TAMs.

In each new generation of PET scanners, the axial field-of-view (FOV) has been increased as a way to improve their sensitivity and obtain better images, faster and with lower injected doses. Nevertheless, in neurological studies, the use of larger axial FOV can introduce errors in the PET attenuation correction (AC), as possible movements of the patient’s mouth are not currently considered. This effect happens when oblique PET sinograms containing both the jaw and the brain are used. In this work we have studied the bias that mouth motion can introduce in the PET AC, for two different scanners. The average effect over large regions in the brain is around 1 % for a moderate mouth motion, although some voxels show differences of up to 18 %. These results indicate that if scanners with large axial FOV and acceptance angles are going to be used in the future, possible movements of patient’s mouth should be considered in the AC procedure.

Analysis of intramyocardial perfusion by contrast echocardiography provides quantitative parameters for the assessment of ischemic disease. This analysis can be achieved by applying an ultrasound (US) burst of high mechanical index to destroy contrast bubbles, measuring various myocardial refilling parameters from the time curves obtained from regions-of-interest (ROIs) within the myocardial wall. To obtain reliable intensity curves, the position of the ROIs must be tracked to compensate for the heart motion along the sequence. In this work, we studied the use of optical flow techniques for ROI repositioning. Two block-matching and one differential technique were evaluated for this purpose. Performance was measured by comparing the result of automatic tracking with results of ROI repositioning by a human expert. This evaluation was carried out on experimental data from animals as well as on sequences from clinical studies. Results are considered to be accurate enough for clinical purposes, and computation times may allow for a real-time processing if incorporated into a US scanner.

Segmentation of image regions based on their texture is a standard problem in image analysis. Once a set of texture features is selected, several algorithms can be applied to segment the image into regions. This paper presents an extension of the watershed algorithm using a vector gradient and multivariate region merging methods. The algorithm uses a set of texture images, and it only depends on an adjustable parameter. Results are presented on a standard set of synthetic images and on textured medical ones, using different texture parameters and merging criteria.

Summary Quantification of live cells in phase contrast microscopy images allows in vivo assessment of the viability of cultured cells. An automatic screening procedure seems advisable because of the large number of cells that must be counted to achieve reasonable accuracy. This paper presents a method that quantifies necrosis in cell cultures by texture analysis of microscope images. The image is divided into regions of equal size that are classified by means of a segmentation algorithm based on texture analysis into three categories: live cells, necrotic cells and background. The classification uses three discriminant functions, built from parameters derived from the histogram and the co-occurrence matrix and calculated by performing an initial stepwise discriminant analysis on 21 sample images from a training set. The areas occupied by live and necrotic cells and number of live cells have been obtained for primary cellular cultures in intervals of 48 h during 2 weeks. The results have been compared with those obtained by an experienced observer, showing a very good correlation (Pearson's coefficient 0.95, kappa 0.87, N= 1600). A method has been developed that provides an accuracy similar to that provided by an expert, while allowing a much higher number of fields to be counted.

This paper presents a methodology and a software package developed to quantify M-mode tissue Doppler imaging (TDI), defining a number of quantitative parameters drawn from velocity and gradient curves obtained after segmenting the myocardial wall into anatomical layers. The independent clinical predictive value of these parameters to detect motion abnormalities in the presence of ischaemia was evaluated in a comparative study between a group of 17 healthy volunteers and 18 ischaemic patients. Factor analysis and stepwise logistic regression were used to assess the independent predictive value of these parameters in detecting abnormal contractility of the basal posterior segment. The statistical analysis performed has proved that any single parameter related to the gradient intensity, particularly the maximum gradient at the moment of the “e” wave, provides meaningful clinical information, achieving a rate of correct classification of 79.1% on the same data set used for the analysis. Adding additional parameters does not improve the diagnostic performance. Further testing with different settings (stress studies, other pathologies or segments) is warranted. (E-mail:desco@mce.hggm.es)

Background: Preliminary experimental data have shown a nonuniform distribution of myocardial velocities (MVs) across the myocardial wall in normal conditions. However, after ischemic damage to the myocardium, a different pattern of reduction in the myocardial layers has been reported. The aim of this study is to analyze the spatial distribution of MVs and the resultant myocardial velocity gradients (MVGs) during the systolic and diastolic time periods. Doppler tissue imaging (DTI) in color Mmode was used to evaluate 3 different myocardial layers (endocardium, mesocardium, and epicardium) and their changes as a result of ischemia. Methods: Thirty-two consecutive patients were studied with DTI color M-mode: 18 patients with a history of previous or ongoing myocardial infarction and 14 healthy subjects. Postprocessing of images was accomplished with proprietary software. MV and MVG values of all layers along both systolic and diastolic time were calculated. For temporal analysis, systole was subdivided in 3 equal periods. Early- and late-diastolic times were also identified. Results: In ischemic patients, the mean MV and maximum MV throughout systole decreased significantly in the endocardium and mesocardium, whereas only slightly in the epicardium. The mean MVG was less in ischemic patients (0.66 ± 0.11 vs 0.23 ± 0.15, P < .03). Temporal analysis showed a decrease in the maximal MV and MVG in all layers over the 3 systolic periods. This decrease was the more consistent in mesocardium. In diastole, there was a decrease in maximal MV in all layers, being more pronounced in endocardium and mesocardium. Diastolic mean MVG was shown to be different between control and ischemic groups (‐0.2 ± 0.05 vs ‐0.10 ± 0.04, P < .06). A significant decrease of the maximal MV in endocardium and mesocardium was reported in the temporal analysis during early diastole. No change was reported in the epicardium. The MVG value also showed a significant decrease (‐2.69 ± 0.29 vs ‐1.59 ± 0.89, P < .02). In ischemic patients in late diastole, the maximum MV was increased in all layers of the myocardium, and this increase was observed mainly in the endocardium. An increase in the MVG (‐0.78 ± 0.18 vs ‐1.47 ± 0.85, P = NS) was also reported during late diastole. Conclusion: There is a nonuniform distribution of velocities in the different myocardial layers under normal conditions. This distribution of velocities undergoes a significant change in patients with ischemic myocardial damage. Intramyocardial wall motion analysis could have clinical applications in both the early detection of ischemia and myocardial viability. (J Am Soc Echocardiogr 2002;15:99-108.)

MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume.The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms.The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers.It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR.

Specific absorption rate (SAR) amplification around active implantable medical devices during diagnostic MRI procedures poses a potential risk for patient safety. In this study, we present a parallel transmit (pTx) strategy that can be used to safely scan patients with deep brain stimulation (DBS) implants.We performed electromagnetic simulations at 3T using a uniform phantom and a multitissue realistic head model with a generic DBS implant. Our strategy is based on using implant-friendly modes, which are defined as the modes of an array that reduce the local SAR around the DBS lead tip. These modes are used in a spokes pulse design algorithm in order to produce highly uniform magnitude least-squares flip angle excitations.Local SAR (1 g) at the lead tip is reduced below 0.1 W/kg compared with 31.2 W/kg, which is obtained by a simple quadrature birdcage excitation without any sort of SAR mitigation. For the multitissue realistic head model, peak 10 g local SAR and global SAR are obtained as 4.52 W/kg and 0.48 W/kg, respectively. A uniform axial flip angle is also obtained (NRMSE3%).Parallel transmit arrays can be used to generate implant-friendly modes and to reduce SAR around DBS implants while constraining peak local SAR and global SAR and maximizing flip angle homogeneity.

Rectal cancer (RC) is associated with a poor prognosis because of the risk both for metastases and for local recurrence after total mesorectal excision (TME) surgery. Preoperative assessment (cTNM) for neoadjuvant therapy (radiation therapy and/or chemotherapy) is very important to minimize recurrence rates. Therefore, the challenge for preoperative imaging in RC is to determine the different risk of recurrence in different patients.

Cluster division is a critical issue in fluor escence micr oscopy-based analytical cytology when preparation protocols do not provide appropriate separation of objects. Overlooking cluster ed nuclei and analyzing only isolated nuclei may dramatically incr ease analysis time or af fect the statistical validation of the r esults. Automatic segmentation of cluster ed nuclei r equir es the implementation of specific image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1) cluster division by the detection of inter nuclei gradients; or 2) division by definition of domains of influence (geometrical approach). Both strategies lead to completely different implementations, and usually algorithms based on a single view strategy fail to corr ectly segment most cluster ed nuclei, or per for m well just for a specific type of sample. An algorithm based on morphological watersheds has been implemented and tested on the segmentation of micr oscopic nuclei clusters. This algorithm pr ovides a tool that can be used for the implementation of both gradient- and domain-based algorithms, and, mor e importantly, for the implementation of mixed (gradient- and shape-based) algorithms. Using this algorithm, almost 90% of the test clusters wer e corr ectly segmented in peripheral blood and bone marr ow pr eparations. The algorithm was valid for both types of samples, using the appr opriate markers and transfor mations. Contract grant sponsor: ARCADIM Project; Contract grant number: CICYT TIC92-0922-C02-01 (Comisión Interministerial de Ciencia y Tecnología); Contract grant sponsor: European Concerted Action CA-AMCA; Contract grant number: BMH1-CT92-1307; Contract grant sponsor: Comunidad Autónoma de Madrid (CAM); Contract grant sponsor: Universidad Politécnica de Madrid (UPM). Publicado

This paper presents a new telemedicine system currently in routine clinical usage, developed within the European Union (EU) ACTS BONAPARTE project (1). The telemedicine system is developed on an asynchronous transfer mode (ATM) multimedia hardware/software platform comprising the following set of telemedicine services: synchronous cooperative work, high-quality video conference, multimedia mail, medical image digitizing, processing, storing and printing, and local and remote transparent database access. The medical information handled by the platform conforms to the Digital Imaging and Communications in Medicine (DICOM) 3.0 medical imaging standard. The telemedicine system has been installed for clinical routines in three Spanish hospitals since November 1997 and has been used in an average of one/two clinical sessions per week. At each clinical session, a usability and clinical evaluation of the system was carried out. Evaluation is carried out through direct observation of interactions and questionnaire-based subjective data. The usability evaluation methodology and the results of the system usability study are also presented in this article. The experience gained from the design, development, and evaluation of the telemedicine system is providing an indepth knowledge of the benefits and difficulties involved in the installation and clinical usage of this type of high-usability and advanced multimedia telemedicine system in the field of teleradiology and collaborative medical imaging diagnosis.

Complex pathological brain patterns generally are found in neurodegenerative diseases which can be correlated with different clinical onsets of a particular pathology. Currently, an objective method that aids to determine such signs, in terms of global and local changes, is not available in clinical practice and the whole interpretation is dependent on the radiologist's skills. In this paper, we propose a fully automatic method that analyzes the brain structure under a multidimensional frame and highlights relevant brain patterns. An association of such patterns with the disease is herein evaluated in three classification tasks, involving probable Alzheimer's disease (AD) patients, Mild Cognitive Impairment (MCI) patients and normal subjects (NC). A set of 75 brain MR images from NC subjects (25), MCI (25) and probable AD (25) patients, split into training (15 subjects) and testing (60 subjects) sets, was used to evaluate the performance of the proposed approach. Preliminary results show that the proposed method reaches a maximum classification accuracy of 80% when discriminating AD patients from NC, of 75% for classification of MCI patients from NC.

In this paper, we propose some new box relaxation numerical schemes on staggered grids to solve the stationary system of partial differential equations arising from the dual minimization problem associated with the total variation operator. We present in detail the numerical schemes for the scalar case and its generalization to multichannel (vectorial) images. Then, we discuss their implementation in digital image denoising. The results outperform the resolution of the dual equation based on the gradient descent approach and pave the way for more advanced numerical strategies.

Resolution in Magnetic Resonance (MR) is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. This paper presents a sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated. The proposed approach includes a whole-image multi-scale edge analysis and a dimensionality reduction scheme, which results in a remarkable improvement of the computational speed and accuracy, taking nearly 26 min to generate a complete 3D high-resolution reconstruction. The method was validated by comparing interpolated and reconstructed versions of 29 MR brain volumes with the original images, acquired in a 3T scanner, obtaining a reduction of 70% in the root mean squared error, an increment of 10.3 dB in the peak signal-to-noise ratio, and an agreement of 85% in the binary gray matter segmentations. The proposed method is shown to outperform a recent state-of-the-art algorithm, suggesting a substantial impact in voxel-based morphometry studies.

Article of the IEEE Education Engineering Conference (EDUCON). Keywords: educational courses, electronic engineering education, telecommunication engineering education, electronics curriculum, project based learning activities, project-oriented integral curriculum, telecommunication engineer degree, university degree structure, Bologna process This paper describes the Electronics curriculum in the Telecommunication Engineer degree at Rey Juan Carlos University (URJC) in Spain. Telecommunication Engineering started in the 2003-2004 academic year. In these years, all the electronic courses have been set up with a main practical orientation and with Project Based Learning (PBL) activities, both compulsory and voluntary. Once these courses have been successfully implemented we have reoriented some of the practical activities to be more interlaced. In this sense, projects involving students of different courses have been developed, as well as projects involving students from different years. All these activities fit in the principles promulgated by the Declaration of Bologna, which results in the actual updating of the university degree structure in Spain. Tecnología Electrónica

Quantitative analysis on diffusion tensor imaging (DTI) has shown be useful in the study of disease-related degeneration. More and more studies perform voxel-by-voxel comparisons of fractional anisotropy (FA) values, aiming at detecting white matter alterations. Overall, there is no agreement about how the normalization stage should be performed. The purpose of this study was to evaluate the effect of the normalization strategy on voxel-based analysis of DTI images, using the performance of a classification approach as objective measure of normalization quality. This is achieved by using a Support Vector Machine (SVM) which constructs a decision surface that allows binary classification with two types of regions, generated after a statistical evaluation of the grey level values of regions detected as statistically significant in a FA analysis.

In this work, a device for generating automated olfactory stimuli in functional magnetic resonance imaging (fMRI) studies is described. The novel issues of our design are: synchronization between the acquisition and the olfactory task, automated control of experimental parameters (odorants sequences, frequency, time and concentration of stimuli). Finally, we present the preliminary results obtained on a General Electric 3 Tesla MRI scanner belong to The Alzheimer Project of the Fundacion Reina Sofia.

Article of the IEEE International Conference on Microelectronic Systems Education, 2009. MSE '09. Keywords: FPGAs, VHDL digital electronic circuit design, electronic teaching ,project-based learning methodology In this paper we present our experience in teaching digital electronic circuit and system design with FPGAs using VHDL. The course follows a project based learning methodology, in which the students learn how to design digital circuits and systems in a practical way. During the course, students design electronic circuits of incremental complexity. At the end of the course they are capable of implementing relatively complex projects, such as image processing systems and videogames. Tecnología Electrónica

The evaluation of new therapies to treat allergic asthma makes frequent use of histological studies. Some of them are based on microscope observation of stained paraffin lung sections to quantify cellular infiltrate, an effect directly related to allergic processes. Currently, there is no software tool available for doing this quantification automatically. This paper presents a methodology and a software tool for the quantification of cellular infiltrate in lung tissue images in an allergic asthma mouse model. The image is divided into regions of equal size, which are then classified by means of a segmentation algo rithm based on texture analysis. The classification uses three discriminant functions, built from parameters derived from the histogram and the co occurrence matrix. These functions were calculated by means of a stepwise discriminant analysis on 79 samples from a training set. Results provided a correct classification of 96.8% on an independ ent test set of 251 samples labeled manually. Regression analysis showed a good agree ment between automatic and manual methods. A reliable and easy to implement method has been developed to provide an automatic method for quantifying micros copy images of lung histological studies. Results showed similar accuracy to that pro vided by an expert, while allowing analyzing a much larger number of fields in a repea table way Contract grant sponsor: CD TEAM project, CENIT program, Ministerio de Industria Publicado