-
1.
Cardiorenal sodium MRI at 7.0 Tesla using a 4/4 channel 1 H/23 Na radiofrequency antenna array.
Boehmert, L, Kuehne, A, Waiczies, H, Wenz, D, Eigentler, TW, Funk, S, von Knobelsdorff-Brenkenhoff, F, Schulz-Menger, J, Nagel, AM, Seeliger, E, et al
Magnetic resonance in medicine. 2019;(6):2343-2356
Abstract
PURPOSE Cardiorenal syndrome describes disorders of the heart and the kidneys in which a dysfunction of 1 organ induces a dysfunction in the other. This work describes the design, evaluation, and application of a 4/4-channel hydrogen-1/sodium (1 H/23 Na) RF array tailored for cardiorenal MRI at 7.0 Tesla (T) for a better physiometabolic understanding of cardiorenal syndrome. METHODS The dual-frequency RF array is composed of a planar posterior section and a modestly curved anterior section, each section consisting of 2 loop elements tailored for 23 Na MR and 2 loopole-type elements customized for 1 H MR. Numerical electromagnetic field and specific absorption rate simulations were carried out. Transmission field ( B1+ ) uniformity was optimized and benchmarked against electromagnetic field simulations. An in vivo feasibility study was performed. RESULTS The proposed array exhibits sufficient RF characteristics, B1+ homogeneity, and penetration depth to perform 23 Na MRI of the heart and kidney at 7.0 T. The mean B1+ field for sodium in the heart is 7.7 ± 0.8 µT/√kW and in the kidney is 6.9 ± 2.3 µT/√kW. The suitability of the RF array for 23 Na MRI was demonstrated in healthy subjects (acquisition time for 23 Na MRI: 18 min; nominal isotropic spatial resolution: 5 mm [kidney] and 6 mm [heart]). CONCLUSION This work provides encouragement for further explorations into densely packed multichannel transceiver arrays tailored for 23 Na MRI of the heart and kidney. Equipped with this technology, the ability to probe sodium concentration in the heart and kidney in vivo using 23 Na MRI stands to make a critical contribution to deciphering the complex interactions between both organs.
-
2.
Amide proton transfer imaging of tumors: theory, clinical applications, pitfalls, and future directions.
Kamimura, K, Nakajo, M, Yoneyama, T, Takumi, K, Kumagae, Y, Fukukura, Y, Yoshiura, T
Japanese journal of radiology. 2019;(2):109-116
Abstract
Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging technique based on chemical exchange saturation transfer (CEST). APT imaging has shown promise in oncologic imaging, especially in the imaging of brain tumors. This review article illustrates the theory of CEST/APT imaging and describes the clinical utility, pitfalls, and potential for future development of APT imaging.
-
3.
Motor cortex metabolite alterations in amyotrophic lateral sclerosis assessed in vivo using edited and non-edited magnetic resonance spectroscopy.
Weerasekera, A, Peeters, R, Sima, D, Dresselaers, T, Sunaert, S, De Vocht, J, Claeys, K, Van Huffel, S, Van Damme, P, Himmelreich, U
Brain research. 2019;:22-31
Abstract
Previous MRI and proton spectroscopy (1H-MRS) studies have revealed impaired neuronal integrity and altered neurometabolite concentrations in the motor cortex of patients with amyotrophic lateral sclerosis (ALS). Here, we aim to use MRI with conventional and novel MRS sequences to further investigate neurometabolic changes in the motor cortex of ALS patients and their relation to clinical parameters. We utilized the novel HERMES (Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy) MRS sequence to simultaneously quantify the inhibitory neurotransmitter GABA and antioxidant glutathione in ALS patients (n = 7) and healthy controls (n = 7). In addition, we have also quantified other MRS observable neurometabolites using a conventional point-resolved MR spectroscopy (PRESS) sequence in ALS patients (n = 20) and healthy controls (n = 20). We observed a trend towards decreasing glutathione concentrations in the motor cortex of ALS patients (p = 0.0842). In addition, we detected a 11% decrease in N-acetylaspartate (NAA) (p = 0.025), a 15% increase in glutamate + glutamine (Glx) (p = 0.0084) and a 21% increase in myo-inositol (mIns) (p = 0.0051) concentrations for ALS patients compared to healthy controls. Furthermore, significant positive correlations were found between GABA-NAA (p = 0.0480; Rρ = 0.7875) and NAA-mIns (p = 0.0448; Rρ = -0.4651) levels among the patients. NAA levels in the bulbar-onset patient group were found to be significantly (p = 0.0097) lower compared to the limb-onset group. A strong correlation (p < 0.0001; Rρ = -0,8801) for mIns and a weak correlation (p = 0.0066; Rρ = -0,6673) for Glx was found for the disease progression, measured by declining of the ALS Functional Rating Scale-Revised criteria (ALSFRS-R). Concentrations of mIns and Glx also correlated with disease severity measured by forced vital capacity (FVC). Results suggest that mean neurometabolite concentrations detected in the motor cortex may indicate clinical and pathological changes in ALS.
-
4.
Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.
Hua, CH, Huynh-The, T, Kim, K, Yu, SY, Le-Tien, T, Park, GH, Bang, J, Khan, WA, Bae, SH, Lee, S
International journal of medical informatics. 2019;:103926
Abstract
BACKGROUND Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors. OBJECTIVE In this paper, an approach of deep bimodal learning is introduced to leverage the performance of DR risk progression identification. METHODS In particular, we further involve valuable clinical information of fundus photography in addition to the aforementioned systemic attributes. Accordingly, a Trilogy of Skip-connection Deep Networks, namely Tri-SDN, is proposed to exhaustively exploit underlying relationships between the baseline and follow-up information of the fundus images and EMR-based attributes. Besides that, we adopt Skip-Connection Blocks as basis components of the Tri-SDN for making the end-to-end flow of signals more efficient during feedforward and backpropagation processes. RESULTS Through a 10-fold cross validation strategy on a private dataset of 96 diabetic mellitus patients, the proposed method attains superior performance over the conventional EMR-modality learning approach in terms of Accuracy (90.6%), Sensitivity (96.5%), Precision (88.7%), Specificity (82.1%), and Area Under Receiver Operating Characteristics (88.8%). CONCLUSIONS The experimental results show that the proposed Tri-SDN can combine features of different modalities (i.e., fundus images and EMR-based numerical risk factors) smoothly and effectively during training and testing processes, respectively. As a consequence, with impressive performance of DR risk progression recognition, the proposed approach is able to help the ophthalmologists properly decide follow-up schedule and subsequent treatment plans.
-
5.
11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier.
Hotta, M, Minamimoto, R, Miwa, K
Scientific reports. 2019;(1):15666
Abstract
Differentiating recurrent brain tumor from radiation necrosis is often difficult. This study aims to investigate the efficacy of 11C-methionine (MET)-PET radiomics for distinguishing recurrent brain tumor from radiation necrosis, as compared with conventional tumor-to-normal cortex (T/N) ratio evaluation. We enrolled 41 patients with metastatic brain tumor or glioma treated using radiation therapy who underwent MET-PET. The area with a standardized uptake value > 1.3 times that of the normal brain cortex was contoured. Forty-two PET features were extracted and used in a random forest classifier and the diagnostic performance was evaluated using a 10-fold cross-validation scheme. Gini index was measured to identify relevant PET parameters for classification. The reference standard was surgical histopathological analysis or more than 6 months of follow-up with MRI. Forty-four lesions were used for the analysis. Thirty-three and 11 lesions were confirmed as recurrent brain tumor and radiation necrosis, respectively. Radiomics and T/N ratio evaluation showed sensitivities of 90.1% and 60.6%, and specificities of 93.9% and 72.7% with areas under the curve of 0.98 and 0.73, respectively. Gray level co-occurrence matrix dissimilarity was the most pertinent feature for diagnosis. MET-PET radiomics yielded excellent outcome for differentiating recurrent brain tumor from radiation necrosis, which outperformed T/N ratio evaluation.
-
6.
Harnessing the new emerging imaging technologies to uncover the role of Ca2+ signalling in plant nutrient homeostasis.
Vigani, G, Costa, A
Plant, cell & environment. 2019;(10):2885-2901
Abstract
Increasing crop yields by using ecofriendly practices is of high priority to tackle problems regarding food security and malnutrition worldwide. A sustainable crop production requires a limited use of fertilizer and the employment of plant varieties with improved ability to acquire nutrients from soil. To reach these goals, the scientific community aims to understand plant nutrients homeostasis by deciphering the nutrient sensing and signalling mechanisms of plants. Several lines of evidence about the involvement of Ca2+ as the signal of an impaired nutrient availability have been reported. Ca2+ signalling is a tightly regulated process that requires specific protein toolkits to perceive external stimuli and to induce the specific responses in the plant needed to survive. Here, we summarize both older and recent findings concerning the involvement of Ca2+ signalling in the homeostasis of nutrients. In this review, we present new emerging technologies, based on the use of genetically encoded Ca2+ sensors and advanced microscopy, which offer the chance to perform in planta analyses of Ca2+ dynamics at cellular resolution. The harnessing of these technologies with different genetic backgrounds and subjected to different nutritional stresses will provide important insights to the still little-known mechanisms of nutrient sensing in plants.
-
7.
Automatic segmentation of the foveal avascular zone in ophthalmological OCT-A images.
Díaz, M, Novo, J, Cutrín, P, Gómez-Ulla, F, Penedo, MG, Ortega, M
PloS one. 2019;(2):e0212364
Abstract
Angiography by Optical Coherence Tomography (OCT-A) is a non-invasive retinal imaging modality of recent appearance that allows the visualization of the vascular structure at predefined depths based on the detection of the blood movement through the retinal vasculature. In this way, OCT-A images constitute a suitable scenario to analyze the retinal vascular properties of regions of interest as is the case of the macular area, measuring the characteristics of the foveal vascular and avascular zones. Extracted parameters of this region can be used as prognostic factors that determine if the patient suffers from certain pathologies (such as diabetic retinopathy or retinal vein occlusion, among others), indicating the associated pathological degree. The manual extraction of these biomedical parameters is a long, tedious and subjective process, introducing a significant intra and inter-expert variability, which penalizes the utility of the measurements. In addition, the absence of tools that automatically facilitate these calculations encourages the creation of computer-aided diagnosis frameworks that ease the doctor's work, increasing their productivity and making viable the use of this type of vascular biomarkers. In this work we propose a fully automatic system that identifies and precisely segments the region of the foveal avascular zone (FAZ) using a novel ophthalmological image modality as is OCT-A. The system combines different image processing techniques to firstly identify the region where the FAZ is contained and, secondly, proceed with the extraction of its precise contour. The system was validated using a representative set of 213 healthy and diabetic OCT-A images, providing accurate results with the best correlation with the manual measurements of two experts clinician of 0.93 as well as a Jaccard's index of 0.82 of the best experimental case in the experiments with healthy OCT-A images. The method also provided satisfactory results in diabetic OCT-A images, with a best correlation coefficient with the manual labeling of an expert clinician of 0.93 and a Jaccard's index of 0.83. This tool provides an accurate FAZ measurement with the desired objectivity and reproducibility, being very useful for the analysis of relevant vascular diseases through the study of the retinal micro-circulation.
-
8.
Automated retinal lesion detection via image saliency analysis.
Yan, Q, Zhao, Y, Zheng, Y, Liu, Y, Zhou, K, Frangi, A, Liu, J
Medical physics. 2019;(10):4531-4544
-
-
Free full text
-
Abstract
BACKGROUND AND OBJECTIVE The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. METHODS Retinal images are first segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low-rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disk, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at the pixel level from different modalities of retinal images, without the need to tune parameters. RESULTS To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at the pixel level, lesion level, or image level according to ground truth availability in these datasets. CONCLUSIONS The experimental results show that the proposed method outperforms existing state-of-the-art ones in applicability, effectiveness, and accuracy.
-
9.
Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks.
Yu, C, Xie, S, Niu, S, Ji, Z, Fan, W, Yuan, S, Liu, Q, Chen, Q
Medical physics. 2019;(10):4502-4519
Abstract
PURPOSE The purpose of this study was to automatically and accurately segment hyper-reflective foci (HRF) in spectral domain optical coherence tomography (SD-OCT) images with diabetic retinopathy (DR) using deep convolutional neural networks. METHODS An automatic HRF segmentation model for SD-OCT images based on deep networks was constructed. The model segmented small lesions through pixel-wise predictions based on small image patches. We used an approach for discriminative features extraction for small patches by introducing small kernels and strides in convolutional and pooling layers, which was applied on the state-of-the-art deep classification networks (GoogLeNet and ResNet). The features extracted by the adapted deep networks were fed into a softmax layer to produce the probabilities of HRF. We trained different models on a dataset with 16 HRF eyes by using different sizes of patches, and then, we fused these models to generate optimal results. RESULTS Experimental results on 18 eyes demonstrated that our method is effective for the HRF segmentation. The dice similarity coefficient (DSC) for the foci area in B-scan, projection images, and foci amount in B-scan images reaches 67.81%, 74.09%, and 72.45%, respectively. CONCLUSIONS The proposed segmentation model can accurately segment HRF in SD-OCT images with DR and outperforms traditional methods. Our model may provide reliable segmentations for small lesions in SD-OCT images and may be helpful in the clinical diagnosis of diseases.
-
10.
Simultaneous metabolic and functional imaging of the brain using SPICE.
Guo, R, Zhao, Y, Li, Y, Li, Y, Liang, ZP
Magnetic resonance in medicine. 2019;(6):1993-2002
-
-
Free full text
-
Abstract
PURPOSE To enable simultaneous high-resolution mapping of brain function and metabolism. METHODS An encoding scheme was designed for interleaved acquisition of functional MRI (fMRI) data in echo volume imaging trajectories and MR spectroscopic imaging (MRSI) data in echo-planar spectroscopic imaging trajectories. The scheme eliminates water and lipid suppression and utilizes free induction decay signals to encode both functional and metabolic information with ultrashort TE, short TR, and sparse sampling of k,t -space. A subspace-based image reconstruction method was introduced for processing both the fMRI and MRSI data. The complementary information in the fMRI and MRSI data sets was also utilized to improve image reconstruction in the presence of intrascan head motion, field drift, and tissue susceptibility changes. RESULTS In-vivo experimental results were obtained from healthy human subjects in resting-state fMRI/MRSI experiments. In these experiments, the proposed method was able to simultaneously acquire metabolic and functional information from the brain in high resolution. For scans of 6.5 minutes, we achieved 3.0 × 3.0 × 1.8 mm3 spatial resolution for fMRI, 1.9 × 2.5 × 3.0 mm3 nominal spatial resolution for MRSI, and 1.9 × 1.9 × 1.8 mm3 nominal spatial resolution for quantitative susceptibility maps. CONCLUSION This work demonstrates the feasibility of simultaneous high-resolution mapping of brain function and metabolism with improved spatial resolution and synergistic image reconstruction.