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Quantitative CEST imaging of amide proton transfer in acute ischaemic stroke.
Msayib, Y, Harston, GWJ, Tee, YK, Sheerin, F, Blockley, NP, Okell, TW, Jezzard, P, Kennedy, J, Chappell, MA
NeuroImage. Clinical. 2019;:101833
Abstract
BACKGROUND Amide proton transfer (APT) imaging may help identify the ischaemic penumbra in stroke patients, the classical definition of which is a region of tissue around the ischaemic core that is hypoperfused and metabolically stressed. Given the potential of APT imaging to complement existing imaging techniques to provide clinically-relevant information, there is a need to develop analysis techniques that deliver a robust and repeatable APT metric. The challenge to accurate quantification of an APT metric has been the heterogeneous in-vivo environment of human tissue, which exhibits several confounding magnetisation transfer effects including spectrally-asymmetric nuclear Overhauser effects (NOEs). The recent literature has introduced various model-free and model-based approaches to analysis that seek to overcome these limitations. OBJECTIVES The objective of this work was to compare quantification techniques for CEST imaging that specifically separate APT and NOE effects for application in the clinical setting. Towards this end a methodological comparison of different CEST quantification techniques was undertaken in healthy subjects, and around clinical endpoints in a cohort of acute stroke patients. METHODS MRI data from 12 patients presenting with ischaemic stroke were retrospectively analysed. Six APT quantification techniques, comprising model-based and model-free techniques, were compared for repeatability and ability for APT to distinguish pathological tissue in acute stroke. RESULTS Robustness analysis of six quantification techniques indicated that the multi-pool model-based technique had the smallest contrast between grey and white matter (2%), whereas model-free techniques exhibited the highest contrast (>30%). Model-based techniques also exhibited the lowest spatial variability, of which 4-pool APTR∗ was by far the most uniform (10% coefficient of variation, CoV), followed by 3-pool analysis (20%). Four-pool analysis yielded the highest ischaemic core contrast-to-noise ratio (0.74). Four-pool modelling of APT effects was more repeatable (3.2% CoV) than 3-pool modelling (4.6% CoV), but this appears to come at the cost of reduced contrast between infarct growth tissue and normal tissue. CONCLUSION The multi-pool measures performed best across the analyses of repeatability, spatial variability, contrast-to-noise ratio, and grey matter-white matter contrast, and might therefore be more suitable for use in clinical imaging of acute stroke. Addition of a fourth pool that separates NOEs and semisolid effects appeared to be more biophysically accurate and provided better separation of the APT signal compared to the 3-pool equivalent, but this improvement appeared be accompanied by reduced contrast between infarct growth tissue and normal tissue.
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Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.
Arcadu, F, Benmansour, F, Maunz, A, Michon, J, Haskova, Z, McClintock, D, Adamis, AP, Willis, JR, Prunotto, M
Investigative ophthalmology & visual science. 2019;(4):852-857
Abstract
PURPOSE To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs). METHODS Retrospective analysis on 17,997 CFPs and their associated OCT measurements from the phase 3 RIDE/RISE diabetic macular edema (DME) studies. DL with transfer-learning cascade was applied on CFPs to predict time-domain OCT (TD-OCT)-equivalent measures of MT, including central subfield thickness (CST) and central foveal thickness (CFT). MT was defined by using two OCT cutoff points: 250 μm and 400 μm. A DL regression model was developed to directly quantify the actual CFT and CST from CFPs. RESULTS The best DL model was able to predict CST ≥ 250 μm and CFT ≥ 250 μm with an area under the curve (AUC) of 0.97 (95% confidence interval [CI], 0.89-1.00) and 0.91 (95% CI, 0.76-0.99), respectively. To predict CST ≥ 400 μm and CFT ≥ 400 μm, the best DL model had an AUC of 0.94 (95% CI, 0.82-1.00) and 0.96 (95% CI, 0.88-1.00), respectively. The best deep convolutional neural network regression model to quantify CST and CFT had an R2 of 0.74 (95% CI, 0.49-0.91) and 0.54 (95% CI, 0.20-0.87), respectively. The performance of the DL models declined when the CFPs were of poor quality or contained laser scars. CONCLUSIONS DL is capable of predicting key quantitative TD-OCT measurements related to MT from CFPs. The DL models presented here could enhance the efficiency of DME diagnosis in tele-ophthalmology programs, promoting better visual outcomes. Future research is needed to validate DL algorithms for MT in the real-world.
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Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.
Nazir, T, Irtaza, A, Shabbir, Z, Javed, A, Akram, U, Mahmood, MT
Artificial intelligence in medicine. 2019;:101695
Abstract
Diabetic retinopathy (DR) is an eye disease that victimize the people suffering from diabetes from many years. The severe form of DR results in form of the blindness that can initially be controlled by the DR-screening oriented treatment. The effective screening programs require the trained human resource that manually grade the fundus images to understand the severity of the disease. But due to the complexity of this process, and the insufficient number of the trained workers, the precise manual grading is an expensive process. The CAD-based solutions try to address these limitations but most of the existing DR detection systems are as evaluated over small sets and become ineffective when applied in real scenarios. Therefore, in this paper we proposed a novel technique to precisely detect the various stages of the DR by extending the research of the content-based image retrieval domain. To achieve the human-level performance over the large-scale DR-datasets (i.e. Kaggle-DR), the fundus images are represented by the novel tetragonal local octa pattern (T-LOP) features, that are then classified through the extreme learning machine (ELM). To justify the significance of the method, the proposed scheme is compared against several state-of-the-art methods including the deep learning-based methods over four DR-datasets of variational lengths (i.e. Kaggle-DR, DRIVE, Review-DB, STARE). The experimental results confirm the significance of the DR-detection scheme to serve as a stand-alone solution for providing the precise information of the severity of the DR in an efficient manner.
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A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans.
Militello, C, Rundo, L, Toia, P, Conti, V, Russo, G, Filorizzo, C, Maffei, E, Cademartiri, F, La Grutta, L, Midiri, M, et al
Computers in biology and medicine. 2019;:103424
Abstract
Many studies have shown that epicardial fat is associated with a higher risk of heart diseases. Accurate epicardial adipose tissue quantification is still an open research issue. Considering that manual approaches are generally user-dependent and time-consuming, computer-assisted tools can considerably improve the result repeatability as well as reduce the time required for performing an accurate segmentation. Unfortunately, fully automatic strategies might not always identify the Region of Interest (ROI) correctly. Moreover, they could require user interaction for handling unexpected events. This paper proposes a semi-automatic method for Epicardial Fat Volume (EFV) segmentation and quantification. Unlike supervised Machine Learning approaches, the method does not require any initial training or modeling phase to set up the system. As a further key novelty, the method also yields a subdivision into quartiles of the adipose tissue density. Quartile-based analysis conveys information about fat densities distribution, enabling an in-depth study towards a possible correlation between fat amounts, fat distribution, and heart diseases. Experimental tests were performed on 50 Calcium Score (CaSc) series and 95 Coronary Computed Tomography Angiography (CorCTA) series. Area-based and distance-based metrics were used to evaluate the segmentation accuracy, by obtaining Dice Similarity Coefficient (DSC) = 93.74% and Mean Absolute Distance (MAD) = 2.18 for CaSc, as well as DSC = 92.48% and MAD = 2.87 for CorCTA. Moreover, the Pearson and Spearman coefficients were computed for quantifying the correlation between the ground-truth EFV and the corresponding automated measurement, by obtaining 0.9591 and 0.9490 for CaSc, and 0.9513 and 0.9319 for CorCTA, respectively. In conclusion, the proposed EFV quantification and analysis method represents a clinically useable tool assisting the cardiologist to gain insights into a specific clinical scenario and leading towards personalized diagnosis and therapy.
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Traumatic bone marrow edema of the calcaneus: Evaluation of color-coded virtual non-calcium dual-energy CT in a multi-reader diagnostic accuracy study.
Booz, C, Nöske, J, Albrecht, MH, Lenga, L, Martin, SS, Wichmann, JL, Huizinga, NA, Eichler, K, Nour-Eldin, NA, Vogl, TJ, et al
European journal of radiology. 2019;:207-214
Abstract
PURPOSE To investigate the diagnostic accuracy of dual-energy computed tomography (CT) virtual non-calcium (VNCa) reconstructions for the depiction of traumatic bone marrow edema of the calcaneus. METHOD Data from 62 patients (33 women, 29 men; mean age: 41 years, range: 19-84 years) with acute tarsal trauma who had undergone third-generation dual-source dual-energy CT and 3-T magnetic resonance imaging (MRI) within seven days between January 2017 and July 2018 were retrospectively analyzed. Five radiologists, blinded to clinical and MRI information, independently assessed conventional grayscale dual-energy CT series for the presence of fractures; after at least eight weeks, readers re-evaluated all cases using color-coded VNCa reconstructions for the presence of bone marrow edema. Quantitative analysis of CT numbers on VNCa reconstructions was performed by a sixth radiologist. Two additional experienced radiologists, blinded to clinical and CT information, assessed MRI series in consensus to define the reference standard. Sensitivity, specificity and the area under the curve (AUC) were the primary indices for diagnostic accuracy. RESULTS MRI revealed 62 areas with bone marrow edema in 39 patients. In the qualitative analysis, VNCa showed high overall sensitivity (286/310 [92%]) and specificity (899/930 [97%]) for the depiction of bone marrow edema. A cut-off value of -53 Hounsfield units (HU) provided a sensitivity of 82% (51/62) and specificity of 95% (176/186]) for differentiating bone marrow edema. The overall AUC was 0.98. CONCLUSIONS In both quantitative and qualitative analyses, dual-energy CT VNCa reconstructions show excellent diagnostic accuracy for the visualization of traumatic calcaneal bone marrow edema compared to MRI.
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APT-weighted MRI: Techniques, current neuro applications, and challenging issues.
Zhou, J, Heo, HY, Knutsson, L, van Zijl, PCM, Jiang, S
Journal of magnetic resonance imaging : JMRI. 2019;(2):347-364
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Abstract
Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.
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Delineation of Human Carotid Plaque Features In Vivo by Exploiting Displacement Variance.
Torres, G, Czernuszewicz, TJ, Homeister, JW, Caughey, MC, Huang, BY, Lee, ER, Zamora, CA, Farber, MA, Marston, WA, Huang, DY, et al
IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2019;(3):481-492
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Abstract
While in vivo acoustic radiation force impulse (ARFI)-induced peak displacement (PD) has been demonstrated to have high sensitivity and specificity for differentiating soft from stiff plaque components in patients with carotid plaque, the parameter exhibits poorer performance for distinguishing between plaque features with similar stiffness. To improve discrimination of carotid plaque features relative to PD, we hypothesize that signal correlation and signal-to-noise ratio (SNR) can be combined, outright or via displacement variance. Plaque feature detection by displacement variance, evaluated as the decadic logarithm of the variance of acceleration and termed "log(VoA)," was compared to that achieved by exploiting SNR, cross correlation coefficient, and ARFI-induced PD outcome metrics. Parametric images were rendered for 25 patients undergoing carotid endarterectomy, with spatially matched histology confirming plaque composition and structure. On average, across all plaques, log(VoA) was the only outcome metric with values that statistically differed between regions of lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), collagen (COL), and calcium (CAL). Further, log(VoA) achieved the highest contrast-to-noise ratio (CNR) for discriminating between LRNC and IPH, COL and CAL, and grouped soft (LRNC and IPH) and stiff (COL and CAL) plaque components. More specifically, relative to the previously demonstrated ARFI PD parameter, log(VoA) achieved 73% higher CNR between LRNC and IPH and 59% higher CNR between COL and CAL. These results suggest that log(VoA) enhances the differentiation of LRNC, IPH, COL, and CAL in human carotid plaques, in vivo, which is clinically relevant to improving stroke risk prediction and medical management.
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Retinal image assessment using bi-level adaptive morphological component analysis.
Javidi, M, Harati, A, Pourreza, H
Artificial intelligence in medicine. 2019;:101702
Abstract
The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as features of these structures are directly related to the diagnosis and treatment process of diabetic retinopathy. The complexity of the retinal image contents especially in images with severe diabetic retinopathy makes detection of vascular structure and lesions difficult. In this paper, a novel framework based on morphological component analysis (MCA) is presented which benefits from the adaptive representations obtained via dictionary learning. In the proposed Bi-level Adaptive MCA (BAMCA), MCA is extended to locally deal with sparse representation of the retinal images at patch level whereas the decomposition process occurs globally at the image level. BAMCA method with appropriately offline learnt dictionaries is adopted to work on retinal images with severe diabetic retinopathy in order to simultaneously separate vessels and exudate lesions as diagnostically useful morphological components. To obtain the appropriate dictionaries, K-SVD dictionary learning algorithm is modified to use a gated error which guides the process toward learning the main structures of the retinal images using vessel or lesion maps. Computational efficiency of the proposed framework is also increased significantly through some improvement leading to noticeable reduction in run time. We experimentally show how effective dictionaries can be learnt which help BAMCA to successfully separate exudate and vessel components from retinal images even in severe cases of diabetic retinopathy. In this paper, in addition to visual qualitative assessment, the performance of the proposed method is quantitatively measured in the framework of vessel and exudate segmentation. The reported experimental results on public datasets demonstrate that the obtained components can be used to achieve competitive results with regard to the state-of-the-art vessel and exudate segmentation methods.
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Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review.
Tang, A, Bashir, MR, Corwin, MT, Cruite, I, Dietrich, CF, Do, RKG, Ehman, EC, Fowler, KJ, Hussain, HK, Jha, RC, et al
Radiology. 2018;(1):29-48
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The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS. © RSNA, 2017 Online supplemental material is available for this article.
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Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning.
Ren, F, Cao, P, Zhao, D, Wan, C
Technology and health care : official journal of the European Society for Engineering and Medicine. 2018;(S1):389-397
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Abstract
BACKGROUND Diabetic macular edema (DME) is one of the severe complication of diabetic retinopathy causing severe vision loss and leads to blindness in severe cases if left untreated. OBJECTIVE To grade the severity of DME in retinal images. METHODS Firstly, the macular is localized using its anatomical features and the information of the macula location with respect to the optic disc. Secondly, a novel method for the exudates detection is proposed. The possible exudate regions are segmented using vector quantization technique and formulated using a set of feature vectors. A semi-supervised learning with graph based classifier is employed to identify the true exudates. Thirdly, the disease severity is graded into different stages based on the location of exudates and the macula coordinates. RESULTS The results are obtained with the mean value of 0.975 and 0.942 for accuracy and F1-scrore, respectively. CONCLUSION The present work contributes to macula localization, exudate candidate identification with vector quantization and exudate candidate classification with semi-supervised learning. The proposed method and the state-of-the-art approaches are compared in terms of performance, and experimental results show the proposed system overcomes the challenge of the DME grading and demonstrate a promising effectiveness.