3 research outputs found
Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction
Purpose: Sparse-view computed tomography (CT) is an effective way to reduce
dose by lowering the total number of views acquired, albeit at the expense of
image quality, which, in turn, can impact the ability to detect diseases. We
explore deep learning-based artifact reduction in sparse-view cranial CT scans
and its impact on automated hemorrhage detection. Methods: We trained a U-Net
for artefact reduction on simulated sparse-view cranial CT scans from 3000
patients obtained from a public dataset and reconstructed with varying levels
of sub-sampling. Additionally, we trained a convolutional neural network on
fully sampled CT data from 17,545 patients for automated hemorrhage detection.
We evaluated the classification performance using the area under the receiver
operator characteristic curves (AUC-ROCs) with corresponding 95% confidence
intervals (CIs) and the DeLong test, along with confusion matrices. The
performance of the U-Net was compared to an analytical approach based on total
variation (TV). Results: The U-Net performed superior compared to unprocessed
and TV-processed images with respect to image quality and automated hemorrhage
diagnosis. With U-Net post-processing, the number of views can be reduced from
4096 (AUC-ROC: 0.974; 95% CI: 0.972-0.976) views to 512 views (0.973;
0.971-0.975) with minimal decrease in hemorrhage detection (P<.001) and to 256
views (0.967; 0.964-0.969) with a slight performance decrease (P<.001).
Conclusion: The results suggest that U-Net based artifact reduction
substantially enhances automated hemorrhage detection in sparse-view cranial
CTs. Our findings highlight that appropriate post-processing is crucial for
optimal image quality and diagnostic accuracy while minimizing radiation dose.Comment: 11 pages, 6 figures, 1 tabl
Improving Image Quality of Sparse-view Lung Cancer CT Images with a Convolutional Neural Network
Purpose: To improve the image quality of sparse-view computed tomography (CT)
images with a U-Net for lung cancer detection and to determine the best
trade-off between number of views, image quality, and diagnostic confidence.
Methods: CT images from 41 subjects (34 with lung cancer, seven healthy) were
retrospectively selected (01.2016-12.2018) and forward projected onto 2048-view
sinograms. Six corresponding sparse-view CT data subsets at varying levels of
undersampling were reconstructed from sinograms using filtered backprojection
with 16, 32, 64, 128, 256, and 512 views, respectively. A dual-frame U-Net was
trained and evaluated for each subsampling level on 8,658 images from 22
diseased subjects. A representative image per scan was selected from 19
subjects (12 diseased, seven healthy) for a single-blinded reader study. The
selected slices, for all levels of subsampling, with and without
post-processing by the U-Net model, were presented to three readers. Image
quality and diagnostic confidence were ranked using pre-defined scales.
Subjective nodule segmentation was evaluated utilizing sensitivity (Se) and
Dice Similarity Coefficient (DSC) with 95% confidence intervals (CI).
Results: The 64-projection sparse-view images resulted in Se = 0.89 and DSC =
0.81 [0.75,0.86] while their counterparts, post-processed with the U-Net, had
improved metrics (Se = 0.94, DSC = 0.85 [0.82,0.87]). Fewer views lead to
insufficient quality for diagnostic purposes. For increased views, no
substantial discrepancies were noted between the sparse-view and post-processed
images.
Conclusion: Projection views can be reduced from 2048 to 64 while maintaining
image quality and the confidence of the radiologists on a satisfactory level
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Purpose: To optimize the binary detection of Chronic Obstructive Pulmonary
Disease (COPD) based on emphysema presence in the lung with convolutional
neural networks (CNN) by exploring manually adjusted versus automated
window-setting optimization (WSO) on computed tomography (CT) images.
Methods: 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78
subjects (43 with COPD; 35 healthy controls) were selected retrospectively
(10.2018-12.2019) and preprocessed. For each image, intensity values were
manually clipped to the emphysema window setting and a baseline 'full-range'
window setting. Class-balanced train, validation, and test sets contained
3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing
various CNN architectures. Furthermore, automated WSO was implemented by adding
a customized layer to the model. The image-level area under the Receiver
Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] and
P-values calculated from one-sided Mann-Whitney U-test were utilized to compare
model variations.
Results: Repeated inference (n=7) on the test set showed that the DenseNet
was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85]
without WSO. Comparably, with input images manually adjusted to the emphysema
window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]
(P=0.03). By adding a customized WSO layer to the DenseNet, an optimal window
in the proximity of the emphysema window setting was learned automatically, and
a mean AUC of 0.82 [0.78, 0.86] was achieved.
Conclusion: Detection of COPD with DenseNet models was improved by WSO of CT
data to the emphysema window setting range