Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction

Abstract Background. Clinical features indicating an ischemic infarction in the territory of posterior cerebral circulation require a comprehensive radiologic examination, which is best achieved by a multi-modality imaging approach (computed tomography [CT], CT-perfusion, computed tomography angiography [CTA], magnetic resonance imaging [MRI] and diffusion weighted imaging [DWI]). The diagnosis of an acute ischemic infarction, where the damage of brain tissue may still be reversible, enables selection of appropriate treatment and contributes to a more favourable outcome. For these reasons it is essential to recognize common neurovascular variants in the territory of the posterior cerebral circulation, one of which is the artery of Percheron. Case report. A 69 year-old woman, last seen awake 10 hours earlier, presented with two typical clinical features of the artery of Percheron infarction, which were vertical gaze palsy and coma. Brain CT and CTA of neck and intracranial arteries upon arrival were interpreted as normal. A new brain CT scan performed 24 hours later revealed hypodensity in the medial parts of thalami. Other imaging modalities were not performed, due to the presumption that the window for the application of effective therapy was over. The diagnosis of an artery of Percheron infarction was therefore made retrospectively with the re-examination of the CTA of neck and intracranial arteries. Conclusions. A multi-modality imaging approach is necessary in every patient with suspicion of the posterior circulation infarction immediately after the onset of symptoms, especially in cases where primary imaging modalities are unremarkable and clinical features are severe, where follow-up examinations are indicated.

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Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

NeuroImage ◽

10.1016/j.neuroimage.2021.118606 ◽

2021 ◽

pp. 118606

Author(s):

Meera Srikrishna ◽

Joana B. Pereira ◽

Rolf A. Heckemann ◽

Giovanni Volpe ◽

Danielle van Westen ◽

...

Keyword(s):

Deep Learning ◽

Human Brain ◽

Brain Tissue ◽

Tissue Classification ◽

Brain Ct

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Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Journal of X-Ray Science and Technology ◽

10.3233/xst-211033 ◽

2021 ◽

pp. 1-8

Author(s):

Jihang Sun ◽

Haoyan Li ◽

Haiyun Li ◽

Michelle Li ◽

Yingzi Gao ◽

...

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Image Quality ◽

Ct Angiography ◽

Takayasu Arteritis ◽

Image Noise ◽

Diagnostic Confidence ◽

Ct Number ◽

Laboratory Test Results ◽

Significant Difference

BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE: To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS: hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS: There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P > 0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P < 0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION: DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

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A multi-label classification model for full slice brain computerised tomography image

BMC Bioinformatics ◽

10.1186/s12859-020-3503-0 ◽

2020 ◽

Vol 21 (S6) ◽

Author(s):

Jianqiang Li ◽

Guanghui Fu ◽

Yueda Chen ◽

Pengzhi Li ◽

Bo Liu ◽

...

Keyword(s):

Deep Learning ◽

Ct Images ◽

Image Features ◽

Classification Model ◽

Brain Diseases ◽

Ct Scans ◽

Computerised Tomography ◽

Brain Ct ◽

Proposed Model ◽

The Brain

Abstract Background Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. Results In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. Conclusion The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.

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Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2019.2953054 ◽

2020 ◽

Vol 39 (5) ◽

pp. 1545-1557 ◽

Cited By ~ 2

Author(s):

Majd Zreik ◽

Robbert W. van Hamersvelt ◽

Nadieh Khalili ◽

Jelmer M. Wolterink ◽

Michiel Voskuil ◽

...

Keyword(s):

Deep Learning ◽

Coronary Angiography ◽

Ct Angiography ◽

Coronary Arteries ◽

Cardiac Ct ◽

Invasive Coronary Angiography ◽

Cardiac Ct Angiography ◽

Learning Analysis

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P1218Deep learning survival analysis enhances the value of hybrid PET/CT for long-term cardiovascular event prediction

European Heart Journal ◽

10.1093/eurheartj/ehz748.0177 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Cited By ~ 1

Author(s):

L E Juarez-Orozco ◽

J W Benjamins ◽

T Maaniitty ◽

A Saraste ◽

P Van Der Harst ◽

...

Keyword(s):

Myocardial Infarction ◽

Survival Analysis ◽

Deep Learning ◽

Ct Angiography ◽

Time To Event ◽

Clinical Variables ◽

Pet Ct ◽

Early Revascularization

Abstract Background Deep Learning (DL) is revolutionizing cardiovascular medicine through complex data-pattern recognition. In spite of its success in the diagnosis of coronary artery disease (CAD), DL implementation for prognostic evaluation of cardiovascular events is still limited. Traditional survival models (e.g.Cox) notably incorporate the effect of time-to-event but are unable to exploit complex non-liner dependencies between large numbers of predictors. On the other hand, DL hasn't systematically incorporated time-to-event for prognostic evaluations. Long-term registries of hybrid PET/CT imaging represent a suitable substrate for DL-based survival analysis due the large amount of time-dependent structured variables that they convey. Therefore, we sought to evaluate the feasibility and performance of DL Survival Analysis in predicting the occurrence of myocardial infarction (MI) and death in a long-term registry of cardiac hybrid PET/CT. Methods Data from our PET/CT registry of symptomatic patients with intermediate CAD risk who underwent sequential CT angiography and 15O-water PET for suspected ischemia, was analyzed. The sample has been followed for a 6-year average for MI or death. Ten clinical variables were extracted from electronic records including cardiovascular risk factors, dyspnea and early revascularization. CT angiography images were evaluated segmentally for: presence of plaque, % of luminal stenosis and calcification (58 variables). Absolute stress PET myocardial perfusion data was evaluated globally and regionally across vascular territories (4 variables). Cox-Nnet (a deep survival neural network) was implemented in a 5-fold cross-validated 80:20 split for training and testing. Resulting DL-hazard ratios were operationalized and compared to the observed events developed during follow-up. The performance of Cox-Nnet evaluating structured CT, PET/CT, and PET/CT+clinical variables was compared to expert interpretation (operationalized as: normal coronaries, non-obstructive CAD, obstructive CAD) and to Calcium Score (CaSc), through the concordance (c)-index. Results There were 426 men and 525 women with a mean age of 61±9 years-old. Twenty-four MI and 49 deaths occurred during follow-up (1 month–9.6 years), while 11.5% patients underwent early revascularization. Cox-Nnet evaluation of PET/CT data (c-index=0.75) outperformed categorical expert interpretation (c-index=0.54) and CaSc (c-index=0.65), while hybrid PET/CT and PET/CT+clinical (c-index=0.75) variables demonstrated incremental performance overall independent from early revascularization. Conclusion Deep Learning Survival Analysis is feasible in the evaluation of cardiovascular prognostic data. It might enhance the value of cardiac hybrid PET/CT imaging data for predicting the long-term development of myocardial infarction and death. Further research into the implementation of Deep Learning for prognostic analyses in CAD is warranted.

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