2 research outputs found
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database
This paper newly introduces multi-modality loss function for GAN-based
super-resolution that can maintain image structure and intensity on unpaired
training dataset of clinical CT and micro CT volumes. Precise non-invasive
diagnosis of lung cancer mainly utilizes 3D multidetector computed-tomography
(CT) data. On the other hand, we can take micro CT images of resected lung
specimen in 50 micro meter or higher resolution. However, micro CT scanning
cannot be applied to living human imaging. For obtaining highly detailed
information such as cancer invasion area from pre-operative clinical CT volumes
of lung cancer patients, super-resolution (SR) of clinical CT volumes to
CT level might be one of substitutive solutions. While most SR methods
require paired low- and high-resolution images for training, it is infeasible
to obtain precisely paired clinical CT and micro CT volumes. We aim to propose
unpaired SR approaches for clincial CT using micro CT images based on unpaired
image translation methods such as CycleGAN or UNIT. Since clinical CT and micro
CT are very different in structure and intensity, direct application of
GAN-based unpaired image translation methods in super-resolution tends to
generate arbitrary images. Aiming to solve this problem, we propose new loss
function called multi-modality loss function to maintain the similarity of
input images and corresponding output images in super-resolution task.
Experimental results demonstrated that the newly proposed loss function made
CycleGAN and UNIT to successfully perform SR of clinical CT images of lung
cancer patients into micro CT level resolution, while original CycleGAN and
UNIT failed in super-resolution.Comment: 6 pages, 2 figure
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset
This paper proposes a novel, unsupervised super-resolution (SR) approach for
performing the SR of a clinical CT into the resolution level of a micro CT
(CT). The precise non-invasive diagnosis of lung cancer typically utilizes
clinical CT data. Due to the resolution limitations of clinical CT (about mm), it is difficult to obtain enough pathological
information such as the invasion area at alveoli level. On the other hand,
CT scanning allows the acquisition of volumes of lung specimens with much
higher resolution ( or higher). Thus,
super-resolution of clinical CT volume may be helpful for diagnosis of lung
cancer. Typical SR methods require aligned pairs of low-resolution (LR) and
high-resolution (HR) images for training. Unfortunately, obtaining paired
clinical CT and CT volumes of human lung tissues is infeasible.
Unsupervised SR methods are required that do not need paired LR and HR images.
In this paper, we create corresponding clinical CT-CT pairs by simulating
clinical CT images from CT images by modified CycleGAN. After this, we use
simulated clinical CT-CT image pairs to train an SR network based on
SRGAN. Finally, we use the trained SR network to perform SR of the clinical CT
images. We compare our proposed method with another unsupervised SR method for
clinical CT images named SR-CycleGAN. Experimental results demonstrate that the
proposed method can successfully perform SR of clinical CT images of lung
cancer patients with CT level resolution, and quantitatively and
qualitatively outperformed conventional method (SR-CycleGAN), improving the
SSIM (structure similarity) form 0.40 to 0.51