Mr. Gihun Kim Profile

Gihun Kim

at Yonsei Univ

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Publications (4)

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Proceedings Article | 2 April 2024 Poster + Paper

Feasibility study of using masked auto-encoder for a streak artifact reduction in sparse view CT

Gihun Kim, Jongduk Baek

Proceedings Volume 12926, 1292624 (2024) https://doi.org/10.1117/12.3006134

KEYWORDS: X-ray computed tomography, Computed tomography, Image restoration, Medical imaging, Image processing, Dose control

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In this study, we investigated the feasibility of using masked auto-encoder (MAE) for sinogram inpainting to reduce a streak artifact in sparse view CT, taking their successful application in image inpainting tasks. To handle the sparse view sinogram, the flattener operator, which flattened the image patch into a token, was modified to handle the 1D image patches. We compared the artifact reduction performance for trained MAE using random sampling (i.e., MAE (Random)) and periodic sparse view sampling (i.e., MAE (Sparse)). To generate the training and validation samples, a software phantom consisting of multiple simple figures was used for the CT simulation with Siddon algorithm and filtered back-projection algorithm. To evaluate the performance of MAE for streak artifact reduction in sparse view CT, we implemented RED-CNN and compared the streak artifact reduction performance of RED-CNN and MAE. SSIM and PSNR were used to quantitatively measure the performance of MAE and RED-CNN. MAE provided better performance for streak artifact reduction in sparse view CT than conventional deep-learning-based artifact reduction technique (i.e., RED-CNN). MAE with random view sampling showed better performance than with sparse view CT images. We expect that MAE for medical imaging could be applied in other fields of medical imaging study.

Proceedings Article | 15 February 2021 Presentation + Paper

Implementation of CNN-based multi-slice model observer for 3D cone beam CT

Gihun Kim, Jongduk Baek

Proceedings Volume 11599, 115990E (2021) https://doi.org/10.1117/12.2579853

KEYWORDS: 3D modeling, 3D image processing, Computed tomography, Performance modeling, Reconstruction algorithms

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In this study, we implement CNN-based multi-slice model observer for 3D CBCT images and compare it with a conventional linear model observer. To evaluate detection performance of the model observer, we considered SKE/BKS four alternative detection task for 3D CBCT images. To generate training and testing datasets, we used a power law spectrum to generate anatomical noise structure. Generated anatomical noise was reconstructed by using FDK algorithm with a CBCT geometry. We employed msCHO and vCHO with LG channels as a comparative linear model observer. We implemented CNN-based multi-slice model observer mimicked msCHOa, which was composed of multiple CNNs. Each CNN consisted of convolutional operator, the batch normalization, a Leaky-ReLU as activation function, and had the following characteristics. (1) To reduce the number of variables, we used full convolutional network and set the filter size as 3×3. (2) Since downscaling layer ignores high frequency components, we did not use any kind of downscaling layer. We used ADAM optimizer and the cross-entropy loss function to train the network. We compared the detection performance of CNN-based multislice model observer, vCHO and msCHO using 1,000 trial cases when the number of slices was three, five and seven. For all numbers of slices, CNN-based multi-slice model observer provided higher detection performance than conventional linear model observers. CNN-based multi-slice model observer required more than 50,000 signal-present and signal-absent images to provide optimized performance, while msCHO required about 5,000 image pairs. Strategy to reduce the amount of training dataset will be a future research topic.

Proceedings Article | 4 March 2019 Paper

Implementation of an ideal observer model using convolutional neural network for breast CT images

Gihun Kim, Minah Han, Hyunjung Shim, Jongduk Baek

Proceedings Volume 10952, 109520L (2019) https://doi.org/10.1117/12.2512131

KEYWORDS: Signal detection, Breast, Convolutional neural networks, Computed tomography, Performance modeling, Image quality

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In this work, we proposed a non-linear observer model based on convolutional neural network and compare its performance with LG-CHO for four alternative forced choice detection task using simulated breast CT images. In our network, each convolutional layer contained 3×3 filters and a leaky-ReLU as an activation function, but a pooling layer and a zero padding to the output of each convolutional layer were not used unlike general convolutional neural network. Network training was conducted using ADAM optimizer with two design parameters (i.e., network depth and width). The optimal value of the design parameter was found by brute force searching, which spanned up to 30 for depth and 128 for channel, respectively. To generate training and validation dataset, we generated anatomical noise images using a power law spectrum of breast anatomy. 50% volume glandular fraction was assumed, and 1 mm diameter signal was used for detection task. The generated images were recon- structed using filtered back-projection with a fan beam CT geometry, and ramp and Hanning filters were used as an apodization filter to generate different noise structures. To train our network, 125,000 signal present images and 375,000 signal absent images were reconstructed for each apodization filter. To measure detectability, we used percent correction with 4,000 images, generated independently from training and validation dataset. Our results show that the proposed network composed of 30 layers and 64 channels provides higher detectability than LG-CHO. We believe that the improved detectability is achieved by the presence of the non-linear module (i.e., leaky-ReLU) in the network.

Proceedings Article | 7 March 2018 Paper

Feasibility study of deep convolutional generative adversarial networks to generate mammography images

Gihun Kim, Hyunjung Shim, Jongduk Baek

Proceedings Volume 10577, 105771C (2018) https://doi.org/10.1117/12.2293046

KEYWORDS: Mammography, Breast, Medical imaging, Machine learning, Computer simulations

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We conducted a feasibility study to generate mammography images using a deep convolutional generative adversarial network (DCGAN), which directly produces realistic images without 3-D model passing through any complex rendering algorithm, such as ray tracing. We trained DCGAN with breast 2D mammography images, which were generated from anatomical noise. The generated X-ray mammography images were successful in that the image preserves reasonable quality and retains the visual patterns similar to training images. Especially, generated images share the distinctive structure of training images. For the quantitative evaluation, we used the mean and variance of beta values of generated images and observed that they are very similar to those of training images. Although the general distribution of generated images matches well with those of training images, there are several limitations of the DCGAN. First, checkboard pattern like artifacts are found in generated images, which is a well-known issue of deconvolution algorithm. Moreover, training GAN is often unstable so to require manual fine-tunes. To overcome such limitations, we plan to extend our idea to conditional GAN approach for improving training stability, and employ an auto-encoder for handling artifacts. To validate our idea on real data, we will apply clinical images to train the network. We believe that our framework can be easily extended to generate other medical images.

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