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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.

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).

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.

Magnetic resonance imaging (MRI) is a medical technique used in radiology to obtain anatomical images of healthy and pathological tissues. Due to hardware limitations and clinical protocols, MRI data are often acquired with low-resolution. For this reason, the scientific community has been developing super-resolution (SR) methodologies in order to enhance spatial resolution through post-processing of 2D multi-slice images. The enhancement of spatial resolution in magnetic resonance (MR) images improves clinical procedures such as tissue segmentation, registration and disease diagnosis. Several methods to perform SR-MR images have been proposed. However, they present different drawbacks: sensitivity to noise, high computational cost, and complex optimization algorithms. In this paper, we develop a supervised learning methodology to perform SR-MR images using a patch-based Gaussian process regression (GPR) method. We compare our approach with nearest-neighbor interpolation, B-splines and a SR-GPR scheme based on nearest-neighbors. We test our SR-GPR algorithm in MRI-T1 and MRI-T2 studies, evaluating the performance through error metrics and morphological validation (tissue segmentation). Results obtained with our methodology outperform the other alternatives for all validation protocols.

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.

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.

© 2017 International Society for Magnetic Resonance in Medicine Purpose: To present a method for spatiotemporal alignment of in-utero magnetic resonance imaging (MRI) time series acquired during maternal hyperoxia for enabling improved quantitative tracking of blood oxygen level-dependent (BOLD) signal changes that characterize oxygen transport through the placenta to fetal organs. Materials and Methods: The proposed pipeline for spatiotemporal alignment of images acquired with a single-shot gradient echo echo-planar imaging includes 1) signal nonuniformity correction, 2) intravolume motion correction based on nonrigid registration, 3) correction of motion and nonrigid deformations across volumes, and 4) detection of the outlier volumes to be discarded from subsequent analysis. BOLD MRI time series collected from 10 pregnant women during 3T scans were analyzed using this pipeline. To assess pipeline performance, signal fluctuations between consecutive timepoints were examined. In addition, volume overlap and distance between manual region of interest (ROI) delineations in a subset of frames and the delineations obtained through propagation of the ROIs from the reference frame were used to quantify alignment accuracy. A previously demonstrated rigid registration approach was used for comparison. Results: The proposed pipeline improved anatomical alignment of placenta and fetal organs over the state-of-the-art rigid motion correction methods. In particular, unexpected temporal signal fluctuations during the first normoxia period were significantly decreased (P < 0.01) and volume overlap and distance between region boundaries measures were significantly improved (P < 0.01). Conclusion: The proposed approach to align MRI time series enables more accurate quantitative studies of placental function by improving spatiotemporal alignment across placenta and fetal organs. Level of Evidence: 1. Technical Efficacy: Stage 1. J. MAGN. RESON. IMAGING 2017;46:403–412.

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 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.

The assessment of regional left ventricular (LV) function has been an important yet unresolved problem since the introduction of echocardiography as a diagnostic tool. Abnormal regional LV wall motion is an early finding in multiple cardiac pathologies and its diagnosis is of critical importance. In the last few years diagnostic procedures based on combined use of existing echocardiographic technologies were geared toward improving the accuracy of detection of baseline and/or induced regional wall motion abnormalities. One of the assumptions is that the combination of reduced LV wall thickening and reduced myocardial velocities can be used to accurately diagnose regional myocardial dysfunction. In this article we will discuss several new techniques for the quantification of regional LV function using Doppler echocardiography. (ECHOCARDIOGRAPHY, Volume 20, October 2003)

We have developed an experimental model to monitor inflammatory lesions in muscle and soft-tissues during the different stages of the disease by means of Magnetic Resonance Imaging (MRI). MRI of mice legs infected with Candida albicans was performed by standard two-dimensional spin echo and fast spin echo (RARE) using customized coils. The MRI findings were compared with pathologic examinations at the initial acute and established acute inflammatory stages, which provided accurate and detailed information on the evolution of the processes involved. The yeast caused inflammation within the first hours post-inoculation, appearing on T2-weighted images as an inhomogeneous mass with increased signal intensity. The presence of fungal hyphae was observed as hypointense signal areas in both T2 and T1 weighted images, with histologic confirmation. Areas of decreased signal intensity on T2 weighted images were apparent on the last experimental day and were attributed to the granulation tissue located within the capsule surrounding the abscess. The close correlation found between MRI and histopathology suggests that MRI is an ideal radiologic technique for monitoring the clinical and therapeutic follow-up of fungal infections in muscle and soft tissues.

This work describes a systematic evaluation of several autofocus functions used for analytical fluorescent image cytometry studies of counterstained nuclei. Focusing is the first step in the automatic fluorescence in situ hybridization analysis of cells. Thirteen functions have been evaluated using qualitative and quantitative procedures. For the last of these procedures a figure-of-merit (FOM) is defined and proposed. This new FOM takes into account five important features of the focusing function. Our results show that functions based on correlation measures have the best performance for this type of image. Publicado

This paper presents a new fool developed to quantify and characterize blood flow in great vessels. The aim is to quantify the flow during the cardiac cycle from sequences of magnetic resonance images of the vessel cross-section. The proposed method tracks the vessel contour to automatically quantify the maximum velocity and flow through the cardiac cycle and finally obtains the instantaneous stroke volume. The techniques proposed are based on active contours coupled with a motion estimation method. Results in ten different sequences from patients and healthy subjects have been obtained, showing values similar to other flow measurement techniques. A repeatability study was done to assess the robustness of the proposed tool.