3D dynamic diuretic renal scintigraphy using a hybrid whole body CZT SPECT/CT camera protocol in the evaluation of acute ureteric obstruction caused by ureteric stone.

PURPOSE: To investigate the performance of dynamic 3D diuretic renal scintigraphy using a hybrid whole body CZT SPECT/CT for the evaluation of acute ureteric obstruction in patients with urinary stone disease. METHODS: 20 patients who presented to the Emergency Department with acute renal colic due to urinary stone disease confirmed by means of CT were prospectively included. Three observers evaluated and graded hydronephrosis, hydroureter, perirenal stranding, and thickening of the renal fascia from the CT as well as the renal scintigraphy curves from the dynamic SPECT study. The normalized residual activity from dynamic SPECT was analysed at 16 min in all patients and at 20 min in suspected obstruction. RESULTS: Renal scintigraphy curves showed a sensitivity of 100%, specificity of 93%, PPV 83% and a NPV 100% for obstruction, while normalized residual activity showed a sensitivity of 100%, specificity of 73%, PPV 56% and a NPV 100%. All patients presented at least 2 secondary signs of obstruction on the CT, showing a PPV of only 25% for obstruction. CONCLUSION: Dynamic 3D diuretic renal scintigraphy CZT SPECT/CT provides valuable functional and anatomical information from one single examination. The combination of pathological renogram curves and high normalized residual activity values provide the most valuable imaging information to determine the presence of acute ureteric obstruction. The secondary signs of obstruction observed on CT are not specific and should not be used to confirm or discard obstruction in patients with urinary stone disease. TRIAL REGISTRATION: ISRCTN15338358. Registration date 03/01/2024. Retrospectively registered. https://www.isrctn.com/ISRCTN15338358?q=miguel%20ochoa%20figueroa&filters=&sort=&offset=1&totalResults=2&page=1&pageSize=10.

Effectiveness of Bacillus subtilis ANT01 and Rhizobium sp. 11B on the control of fusarium wilt in pineapple (Ananas comosus).

Pineapple (Ananas comosus) is commonly infected by Fusarium oxysporum, causal agent of the fusarium wilt disease. Conventionally, growers use synthetic fungicides to control the disease, which lead to environmental pollution, hazardous effects on non-target organisms and risks on human health. The aim of this work was to assess the effectiveness of Bacillus subtilis ANT01 and Rhizobium sp. 11B to control fusarium wilt on pineapple plants. Four treatments derived from a complete factorial design were tested under field conditions. Treatments composed of B. subtilis ANT01 and the combination B. subtilis ANT01-Rhizobium sp. 11B decreased disease severity by 94.4% and 86.1%, respectively. On the other hand, the treatment prepared with Rhizobium sp. 11B alone showed a reduction of 75.0%. Size of leaves and nutritional condition (SPAD units) of the biocontrol agents-treated plants showed no statistical differences. Moreover, B. subtilis ANT01 decreased by 46% the initial soil population of F. oxysporum, while Rhizobium sp. 11B, B. subtilis ANT01 plus Rhizobium sp. 11B and control, showed a population reduction of 12.5%, 24.2% and 23.0%, respectively. These results make evident the potential of B. subtilis ANT01 as biocontrol agent of the pathogen under field conditions.

Diagnostic performance of a novel deep learning attenuation correction software for MPI using a cardio dedicated CZT camera. Experience in the clinical practice.

PURPOSE: To evaluate the diagnostic performance of a novel deep learning attenuation correction software (DLACS) for myocardial perfusion imaging (MPI) using a cadmium-zinc-telluride (CZT) cardio dedicated camera with invasive coronary angiography (ICA) correlation for the diagnosis of coronary artery disease (CAD) in a high-risk population. METHODS: Retrospective study of 300 patients (196 males [65%], mean age 68 years) from September 2014 to October 2019 undergoing MPI, followed by ICA and evaluated by means of quantitative angiography software, within six months after the MPI. The mean pre-test probability score for coronary disease according to the European Society of Cardiology criteria was 37% for the whole cohort. The MPI was performed in a dedicated CZT cardio camera (D-SPECT Spectrum Dynamics) with a two-day protocol, according to the European Association of Nuclear Medicine guidelines. MPI was retrospectively evaluated with and without the DLACS. RESULTS: The overall diagnostic accuracy of MPI without DLACS to identify patients with any obstructive CAD at ICA was 87%, sensitivity 94%, specificity 57%, Positive Predictive Value 91% and Negative Predictive Value 64%. Using DLACS the overall diagnostic accuracy was 90%, sensitivity 91%, specificity 86%, Positive Predictive Value 97% and Negative Predictive Value 66%. CONCLUSION: Use of the novel DLACS enhances performance of the MPI using the CZT D-SPECT camera and achieves improved results, especially avoiding artefacts and reducing the number of false positive results.

Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission.

PURPOSE: Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). METHODS: FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. RESULTS: Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). CONCLUSION: Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.

Transmission of optical communication signals through ring core fiber using perfect vortex beams.

Orbital angular momentum can be used to implement high capacity data transmission systems that can be applied for classical and quantum communications. Here we experimentally study the generation and transmission properties of the so-called perfect vortex beams and the Laguerre-Gaussian beams in ring-core optical fibers. Our results show that when using a single preparation stage, the perfect vortex beams present less ring-radius variation that allows coupling of higher optical power into a ring core fiber. These results lead to lower power requirements to establish fiber-based communications links using orbital angular momentum and set the stage for future implementations of high-dimensional quantum communication over space division multiplexing fibers.

Nicotinic acid attenuates experimental non-alcoholic steatohepatitis by inhibiting the NLRP3 inflammasome/pyroptosis pathway.

Non-alcoholic steatohepatitis (NASH) is a global public health concern that may progress into fibrosis, cirrhosis, and liver cancer, with limited curative treatment options. While the nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome is closely linked to NASH progression, nicotinic acid (NA), a vitamin used for the treatment of dyslipidemia, is an emerging pharmaceutical treatment for hepatic steatosis and fibrosis. Here, we investigated pharmacological effects of NA on experimental NASH and whether NLRP3 inflammasome/pyroptosis inhibition is an associated mechanism of action. Rats were fed a high-fat sucrose diet supplemented with cholesterol and a low dose of CCl4. NA significantly reduced inflammation by decreasing the protein levels of tumor necrosis factor-alpha and nuclear factor kappa B. Moreover, NA inhibited the formation of NLRP3- apoptosis-associated speck-like protein containing caspase recruitment domain-Caspase-1, decreasing interleukin-1beta, interleukin-18, and gasdermin D protein. In addition, NA reduced tumor growth factor-beta, alpha-smooth muscle actin, and hepatic levels of collagen-1, consequently decreasing extracellular matrix synthesis. Our results indicate that NA can inhibit NASH progression and encourage further basic and clinical studies on the use of NA for the treatment of human NASH.

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction.

Images produced by CMOS sensors may contain defective pixels due to noise, manufacturing errors, or device malfunction, which must be detected and corrected at early processing stages in order to produce images that are useful to human users and image-processing or machine-vision algorithms. This paper proposes a defective pixel detection and correction algorithm and its implementation using CMOS analog circuits, which are integrated with the image sensor at the pixel and column levels. During photocurrent integration, the circuit detects defective values in parallel at each pixel using simple arithmetic operations within a neighborhood. At the image-column level, the circuit replaces the defective pixels with the median value of their neighborhood. To validate our approach, we designed a 128×128-pixel imager in a 0.35µm CMOS process, which integrates our defective-pixel detection/correction circuits and processes images at 694 frames per second, according to post-layout simulations. Operating at that frame rate, our proposed algorithm and its CMOS implementation produce better results than current state-of-the-art algorithms: it achieves a Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) of 45 dB and 198.4, respectively, in images with 0.5% random defective pixels, and a PSNR of 44.4 dB and IEF of 194.2, respectively, in images with 1.0% random defective pixels.

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera.

PURPOSE: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning. METHODS: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well. RESULTS: Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%). CONCLUSION: Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.

Diagnostic performance of different cardiac stress protocols for myocardial perfusion imaging for the diagnosis of coronary artery disease using a cadmium-zinc-telluride camera with invasive coronary angiography correlation.

PURPOSE: To evaluate the diagnostic performance of three different cardiac stress protocols for myocardial perfusion imaging (MPI) using a cadmium-zinc-telluride (CZT) camera with invasive coronary angiography (ICA) correlation for the diagnosis of coronary artery disease in a high risk population. METHODS: Retrospective study of 263 patients (96 women and 167 males, mean age 68 years) from which 119 patients performed a bicycle stress test (BST), 113 pharmacological stress test (PST) and 31 a combination of the two (CST) between September 2014 and December 2018. The patients then underwent myocardial perfusion imaging (MPI), followed by ICA and evaluated by means of quantitative angiography software, within six months after the MPI. The mean pre-test probability score for coronary disease according to the European Society of Cardiology criteria was 36% for the whole population. The MPI was performed in a dedicated CZT cardio camera (D-SPECT Spectrum Dynamics) with a two-day protocol, according to the European Association of Nuclear Medicine guidelines. RESULTS: No significant difference was observed between the three stress protocols in terms of diagnostic accuracy (BST 85%, PST 88%, CST 84%). The overall diagnostic accuracy of MPI to identify patients with any obstructive CAD at ICA was 86%, Sensitivity 93%, Specificity 54%, PPV 90% and NPV 63%. CONCLUSION: The CZT D-SPECT camera achieves overall satisfactory results in the diagnosis of CAD, observing no significant differences in the diagnostic performance when the stress test was performed as a BST, PST or CST.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

Motion-Based Object Location on a Smart Image Sensor Using On-Pixel Memory.

Object location is a crucial computer vision method often used as a previous stage to object classification. Object-location algorithms require high computational and memory resources, which poses a difficult challenge for portable and low-power devices, even when the algorithm is implemented using dedicated digital hardware. Moving part of the computation to the imager may reduce the memory requirements of the digital post-processor and exploit the parallelism available in the algorithm. This paper presents the architecture of a Smart Imaging Sensor (SIS) that performs object location using pixel-level parallelism. The SIS is based on a custom smart pixel, capable of computing frame differences in the analog domain, and a digital coprocessor that performs morphological operations and connected components to determine the bounding boxes of the detected objects. The smart-pixel array implements on-pixel temporal difference computation using analog memories to detect motion between consecutive frames. Our SIS can operate in two modes: (1) as a conventional image sensor and (2) as a smart sensor which delivers a binary image that highlights the pixels in which movement is detected between consecutive frames and the object bounding boxes. In this paper, we present the design of the smart pixel and evaluate its performance using post-parasitic extraction on a 0.35 µm mixed-signal CMOS process. With a pixel-pitch of 32 µm × 32 µm, we achieved a fill factor of 28%. To evaluate the scalability of the design, we ported the layout to a 0.18 µm process, achieving a fill factor of 74%. On an array of 320×240 smart pixels, the circuit operates at a maximum frame rate of 3846 frames per second. The digital coprocessor was implemented and validated on a Xilinx Artix-7 XC7A35T field-programmable gate array that runs at 125 MHz, locates objects in a video frame in 0.614 µs, and has a power consumption of 58 mW.

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data.

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision.

The design of cooperative advanced driver assistance systems (C-ADAS) involves a holistic and systemic vision that considers the bidirectional interaction among three main elements: the driver, the vehicle, and the surrounding environment. The evolution of these systems reflects this need. In this work, we present a survey of C-ADAS and describe a conceptual architecture that includes the driver, vehicle, and environment and their bidirectional interactions. We address the remote operation of this C-ADAS based on the Internet of vehicles (IoV) paradigm, as well as the involved enabling technologies. We describe the state of the art and the research challenges present in the development of C-ADAS. Finally, to quantify the performance of C-ADAS, we describe the principal evaluation mechanisms and performance metrics employed in these systems.

A new germacranolide from Ageratina vernalis.

The aerial parts of Ageratina vernalis provided the new germacranolide 1,10-epoxydeltoidin A (3), together with the known pentacyclic triterpenoid hopane-6α,22-diol (1), and the also known germacranolides deltoidin A (2) and 15-hydroxydeltoidin A (4). In addition, pTsOH catalyzed cyclization of 2 afforded the new guaianolide 5. The absolute configuration of 2, 4, and 5 was assigned by vibrational circular dichroism spectroscopy, while the complete 1H and 13C NMR data assignments of 2-5 followed from 1 D- and 2 D-NMR experiments.

MRI of nigrosome-1: A potential triage tool for patients with suspected parkinsonism.

BACKGROUND AND PURPOSE: Susceptibility-weighted imaging (SWI) of nigrosome-1 is an emerging and clinically applicable imaging marker for parkinsonism, which can be derived from routinely performed brain MRI. The purpose of the study was to assess whether SWI can be used as a triage tool for more efficient selection of subsequent Dopamine Transporter Scan (DaTSCAN) single-photon emission computed tomography (SPECT). METHODS: We examined 72 consecutive patients with suspected parkinsonism with both DaTSCAN SPECT and SWI (48 in Philips Ingenia, 24 in GE Signa). Additionally, we examined 24 healthy controls with SWI (14 in Philips Ingenia, 10 in GE Signa). Diagnostic performance of SWI and DaTSCAN SPECT was assessed on the basis of clinical diagnosis, in terms of sensitivity, specificity, and diagnostic accuracy. RESULTS: A total of 54 parkinsonism patients (69 years ± 9, 32 men), 18 nonparkinsonism patients (69.4 years ± 9, 10 men), and 24 healthy controls (62 years ± 8, 10 men) were recruited. SWI had a specificity of 92% and a sensitivity of 74%, whereas DaTSCAN SPECT had 83% and 94%, respectively. By preselecting patients with abnormal or inconclusive SWI, the diagnostic performance of DaTSCAN SPECT improved (specificity 100%, sensitivity 95%). Scans from Philips were associated with significantly lower image quality compared to GE (p < .001). The experienced rater outperformed the less experienced one in diagnostic accuracy (82% vs. 68%). CONCLUSIONS: SWI can be used as triage tool because normal SWI can in most cases rule out parkinsonism. However, the performance of SWI depends on acquisition parameters and rater's experience.

Absolute configuration assignment of stigmasterol oxiranes.

Diastereoisomeric stigmasterol oxiranes 4, 5, 8, and 9 are known phytosterol oxidation products (POPs) that have been evaluated for their cytotoxicity, although the results are of limited significance since, in most cases, they were evaluated as mixtures. Consequently, to establish biological activity hierarchy of these oxides, it is critical to evaluate individual pure POPs. Therefore, we now describe the obtention of individual molecules and their absolute configuration (AC) determination. The two acetylated C-5-C-6 oxiranes 6 and 7; the two acetylated C-22-C-23 oxides 10 and 11, obtained by means of Δ5 double bond protection-deprotection; and the four C-5-C-6, C-22-C-23 diepoxystigmasteryl acetates 19-22 were now individually gained and their AC determined by vibrational circular dichroism. Vibrational modes associated with the C-5-C-6 and the C-22-C-23 bonds were identified in dioxiranes 19-22 and used to assign the AC of monoepoxides 6, 7, 10, and 11. The AC of biological active non-acetylated molecules follows immediately. Due to the scarce spectroscopic information available for these POPs, the 1 H and 13 C NMR chemical shifts of 3-22 were assigned using 1D- and 2D-NMR experiments.

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. MATERIALS AND METHODS: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. RESULTS: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. CONCLUSION: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.

Neoehrlichia mikurensis Causing Thrombosis and Relapsing Fever in a Lymphoma Patient Receiving Rituximab.

Neoehrlichia (N.) mikurensis, an intracellular tick-borne bacterium not detected by routine blood culture, is prevalent in ticks in Scandinavia, Central Europe and Northern Asia, and may cause long-standing fever, nightly sweats, migrating pain, skin rashes and thromboembolism, especially in patients treated with rituximab. The multiple symptoms may raise suspicion of both infection, inflammation and malignancy, and lead in most cases to extensive medical investigations across many medical specialist areas and a delay of diagnosis. We describe a complex, albeit typical, case of neoehrlichiosis in a middle-aged splenectomised male patient with a malignant lymphoma, receiving treatment with rituximab. The multifaceted clinical picture associated with this tick-borne disease is addressed, and longitudinal clinical and laboratory data, as well as imaging, are provided. Longstanding relapsing fever in combination with thrombosis in superficial and deep veins in an immunocompromised patient living in a tick-endemic region should raise the suspicion of the emerging tick-borne disease neoehrlichiosis. Given the varied clinical presentation and the risk of delay in diagnosis and treatment, we believe it is important to raise clinicians' awareness of this emerging infection, which is successfully treated with doxycycline.

18Fluorodeoxyglucose uptake in relation to fat fraction and R2* in atherosclerotic plaques, using PET/MRI: a pilot study.

Inflammation inside Atherosclerotic plaques represents a major pathophysiological process driving plaques towards rupture. Pre-clinical studies suggest a relationship between lipid rich necrotic core, intraplaque hemorrhage and inflammation, not previously explored in patients. Therefore, we designed a pilot study to investigate the feasibility of assessing the relationship between these plaque features in a quantitative manner using PET/MRI. In 12 patients with high-grade carotid stenosis the extent of lipid rich necrotic core and intraplaque hemorrhage was quantified from fat and R2* maps acquired with a previously validated 4-point Dixon MRI sequence in a stand-alone MRI. PET/MRI was used to measure 18F-FDG uptake. T1-weighted images from both scanners were used for registration of the quantitative Dixon data with the PET images. The plaques were heterogenous with respect to their volumes and composition. The mean values for the group were as follows: fat fraction (FF) 0.17% (± 0.07), R2* 47.6 s-1 (± 10.9) and target-to-blood pool ratio (TBR) 1.49 (± 0.48). At group level the correlation between TBR and FFmean was - 0.406, p 0.19 and for TBR and R2*mean 0.259, p 0.42. The lack of correlation persisted when analysed on a patient-by-patient basis but the study was not powered to draw definitive conclusions. We show the feasibility of analysing the quantitative relationship between lipid rich necrotic cores, intraplaque haemorrhage and plaque inflammation. The 18F-FDG uptake for most patients was low. This may reflect the biological complexity of the plaques and technical aspects inherent to 18F-FDG measurements.Trial registration: ISRCTN, ISRCTN30673005. Registered 05 January 2021, retrospectively registered.

A Heterogeneous Hardware Accelerator for Image Classification in Embedded Systems.

Convolutional neural networks (CNN) have been extensively employed for image classification due to their high accuracy. However, inference is a computationally-intensive process that often requires hardware acceleration to operate in real time. For mobile devices, the power consumption of graphics processors (GPUs) is frequently prohibitive, and field-programmable gate arrays (FPGA) become a solution to perform inference at high speed. Although previous works have implemented CNN inference on FPGAs, their high utilization of on-chip memory and arithmetic resources complicate their application on resource-constrained edge devices. In this paper, we present a scalable, low power, low resource-utilization accelerator architecture for inference on the MobileNet V2 CNN. The architecture uses a heterogeneous system with an embedded processor as the main controller, external memory to store network data, and dedicated hardware implemented on reconfigurable logic with a scalable number of processing elements (PE). Implemented on a XCZU7EV FPGA running at 200 MHz and using four PEs, the accelerator infers with 87% top-5 accuracy and processes an image of 224×224 pixels in 220 ms. It consumes 7.35 W of power and uses less than 30% of the logic and arithmetic resources used by other MobileNet FPGA accelerators.