https://github.com/fitushar/study-of-low-dose-to-high-dose-ct-using-supervised-learning-with-gan-and-virtual-imaging-trials

Computed tomography (CT) is one of the most widely used radiography exams worldwide for different diagnostic applications. However, CT scans involve ioniz- ing radiational exposure, which raises health concerns. Counter-intuitively, low- ering the adequate CT dose level introduces noise and reduces the image quality, which may impact clinical diagnosis. This study analyzed the feasibility of using a conditional generative adversarial network (cGAN) called pix2pix to learn the mapping from low dose to high dose CT images under different conditions. This study included 270 three-dimensional (3D) CT scan images (85,050 slices) from 90 unique patients imaged virtually using virtual imaging trials platform for model development and testing. Performance was reported as peak signal-to-noise ra- tio (PSNR) and structural similarity index measure (SSIM). Experimental results demonstrated that mapping a single low-dose CT to high-dose CT and weighted two low-dose CTs to high-dose CT have comparable performances using pix2pix CGAN and applicability of using VITs
https://github.com/fitushar/study-of-low-dose-to-high-dose-ct-using-supervised-learning-with-gan-and-virtual-imaging-trials

ct deep-learning denoising gan generative-adversarial-network pix2pix pix2pix-tensorflow

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Host: GitHub
URL: https://github.com/fitushar/study-of-low-dose-to-high-dose-ct-using-supervised-learning-with-gan-and-virtual-imaging-trials
Owner: fitushar
Created: 2022-04-26T10:21:57.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2022-04-26T19:41:01.000Z (about 3 years ago)
Last Synced: 2024-11-08T17:42:32.792Z (8 months ago)
Topics: ct, deep-learning, denoising, gan, generative-adversarial-network, pix2pix, pix2pix-tensorflow
Language: Python
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Forks: 1
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Metadata Files:
- Readme: README.md

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README

# Study-of-Low-dose-to-High-dose-CT-using-Supervised-Learning-with-GAN-and-Virtual-Imaging-Trials
This study analyzed the feasibility of utilizing virtually generated CT images to generate high-dose
CT from low-dose CT applying pix2pix cGANs. Different experiments were performed using single
and multiple low-dose to high-dose CT mapping using 4 different pix2pix cGANs. Performance of
all the model are comparable.

### Abstract
Computed tomography (CT) is one of the most widely used radiography exams worldwide for different diagnostic applications. However, CT scans involve ioniz- ing radiational exposure, which raises health concerns. Counter-intuitively, low- ering the adequate CT dose level introduces noise and reduces the image quality, which may impact clinical diagnosis. This study analyzed the feasibility of using a conditional generative adversarial network (cGAN) called pix2pix to learn the mapping from low dose to high dose CT images under different conditions. This study included 270 three-dimensional (3D) CT scan images (85,050 slices) from 90 unique patients imaged virtually using virtual imaging trials platform for model development and testing. Performance was reported as peak signal-to-noise ra- tio (PSNR) and structural similarity index measure (SSIM). Experimental results demonstrated that mapping a single low-dose CT to high-dose CT and weighted two low-dose CTs to high-dose CT have comparable performances using pix2pix CGAN and applicability of using VITs.

### Results

## Study over-view
* **1.** pix2pix cGAN was adoped for model development.
* **2.** Generating Simulated CT scans utilizing VITs platform.
* **3.** Quantitative and qualitative performance analysis.

### Key refrences:
```ruby
@inproceedings{isola2017image,
title={Image-to-image translation with conditional adversarial networks},
author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={1125--1134},
year={2017}
}

@article{abadi2021virtual,
title={Virtual imaging trials for coronavirus disease (COVID-19)},
author={Abadi, Ehsan and Segars, W Paul and Chalian, Hamid and Samei, Ehsan},
journal={AJR. American journal of roentgenology},
volume={216},
number={2},
pages={362},
year={2021},
publisher={NIH Public Access}
}

@inproceedings{tushar2022virtual,
title={Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography},
author={Tushar, Fakrul Islam and Abadi, Ehsan and Sotoudeh-Paima, Saman and Fricks, Rafael B and Mazurowski, Maciej A and Segars, W Paul and Samei, Ehsan and Lo, Joseph Y},
booktitle={Medical Imaging 2022: Computer-Aided Diagnosis},
volume={12033},
pages={18--24},
year={2022},
organization={SPIE}
}
```

## Data and Codes

* **1.** Dataset used in this study is not publicly avaible but can be consider access upon request.
* **1.** All the codes related to this project has been shared publicly within this repo, we will further discussed the code sctructure and in the How to run section.

# How to Run

* **1.** Model were trained using tfrecords and the tfrecords were generated using the coding with the folders

## Genegrate tfrecords for model cGAN-SD, cGAN-Dy codes- https://github.com/fitushar/Study-of-Low-dose-to-High-dose-CT-using-Supervised-Learning-with-GAN-and-Virtual-Imaging-Trials/tree/main/tfrecords_cGAN_SD_and_cGAN_Dy

### Files
* (1) Generate_tfrecords-|-> Generate tfrecords for training, testing and validation
```ruby
a) config_generate_denoising_tfrecords.py |-- Configuration tfrecords generation hyperparameter.
b) Preprocessing_utlities.py |-- Preprocessing function such as Resampling,Hu cliping, and patch extraction functions
c) main_generate_denoising_tfrecords.py |-- Generate tfrecords, Main File
d) CVIT_CT_data.csv |-- Data path info
```
* (2) All you need to do is to modify the config_generate_denoising_tfrecords.py for wich data you want generate tfrecors-|->
for cGAN_SD and cGAN_Dy ,USE_THE_DATASET=='CVIT_CT_5p7_28p5_57', and the path of the tfrecords

#### config_generate_denoising_tfrecords.py

```ruby
import tensorflow as tf

#-----User input if there were multiple dataset options
USE_THE_DATASET ='CVIT_CT_5p7_28p5_57'
starting_end_range = False
PATCH_PARAMS = { 'example_size': [256,256], #[hight x width]}
'resampling':[2.0,2.0,1.0],# resampling CT
'padding_value':-1}

STARTING_INDEX=0
END_INDEX =1

if USE_THE_DATASET=='CVIT_CT_57_100_200':
RAW_DATA_DIRECTORY_lower ='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/RAW_DATA/57mAs_noTCM_nifti_reorient/'
RAW_DATA_DIRECTORY_medium ='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/RAW_DATA/100mAs_noTCM_nifti_reorient/'
RAW_DATA_DIRECTORY_high ='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/RAW_DATA/200mAs_noTCM_nifti_reorient/'

NAMEOF_TF='DN_57_100_200'

IMAGE_PATH_INDEX_NAME_lower ='57mAs' #index of the column having the data path of nifti.gz (nii.gz)
IMAGE_PATH_INDEX_NAME_medium ='100mAs' #index of the column having the data path of nifti.gz (nii.gz)
IMAGE_PATH_INDEX_NAME_high ='200mAs' #index of the column having the data path of nifti.gz (nii.gz)

IMAGE_PATIENT_ID_INDEX_NAME = 'Patient_ID'
IMAGE_PHANTOM_ID_INDEX_NAME = 'Phantom_ID'
IMAGE_DISEAE_LABEL_INDEX_NAME = 'Diseased_label'

FLIP_CT='False'
DATA_CSV='CVIT_CT_data.csv'
PATH_TO_SAVE_TFRECORDS='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_57_100_200/DN_57_100_200_tfrecords/'
SAVING_PNG_OF_THE_PATCH_PNG='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_57_100_200/DN_57_100_200_tfrecords_png/'
NAME_OF_PATH_CSV='Denoising_57_100_200_CVIT-Duke_patch1x160x160_spc5x2x2.csv'

if USE_THE_DATASET=='CVIT_CT_5p7_28p5_57':
RAW_DATA_DIRECTORY_lower ='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/RAW_DATA/5p7mAs_noTCM_nifti_reorient/'
RAW_DATA_DIRECTORY_medium ='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/RAW_DATA/28p5mAs_noTCM_nifti_reorient/'
RAW_DATA_DIRECTORY_high ='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/RAW_DATA/57mAs_noTCM_nifti_reorient/'

NAMEOF_TF='DN_5p7_28p5_57'

IMAGE_PATH_INDEX_NAME_lower ='5p7mAs' #index of the column having the data path of nifti.gz (nii.gz)
IMAGE_PATH_INDEX_NAME_medium ='28p5mAs' #index of the column having the data path of nifti.gz (nii.gz)
IMAGE_PATH_INDEX_NAME_high ='57mAs' #index of the column having the data path of nifti.gz (nii.gz)

IMAGE_PATIENT_ID_INDEX_NAME = 'Patient_ID'
IMAGE_PHANTOM_ID_INDEX_NAME = 'Phantom_ID'
IMAGE_DISEAE_LABEL_INDEX_NAME = 'Diseased_label'

FLIP_CT='False'
DATA_CSV='CVIT_CT_data.csv'
PATH_TO_SAVE_TFRECORDS='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7_28p5_57/5p7_28p5_57_tfrecords/'
SAVING_PNG_OF_THE_PATCH_PNG='/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7_28p5_57/5p7_28p5_57_tfrecords_png/'
NAME_OF_PATH_CSV='Denoising_5p7_28p5_57_CVIT-Duke_patch1x160x160_spc5x2x2.csv'

```

## Similarly to Genegrate tfrecords for model cGAN-DD codes- https://github.com/fitushar/Study-of-Low-dose-to-High-dose-CT-using-Supervised-Learning-with-GAN-and-Virtual-Imaging-Trials/tree/main/tfrecords_cGAN_DD

and use the USE_THE_DATASET=='CVIT_CT_5p7_57' and USE_THE_DATASET=='CVIT_CT_28p5_57'.

## To train, Test and generated predicted images for cGAN-SD, cGAN-DD use codes with in the folder: https://github.com/fitushar/Study-of-Low-dose-to-High-dose-CT-using-Supervised-Learning-with-GAN-and-Virtual-Imaging-Trials/tree/main/Denoising_pix2pix_model

* Configure the model configuration with in the *config_pix2pix_2D_denoising.py* file

```ruby

import tensorflow as tf
import math
import numpy as np
import pandas as pd
import glob
import os

# The facade training set consist of 400 images

# The batch size of 1 produced better results for the U-Net in the original pix2pix experiment
BATCH_SIZE = 1
# Each image is 192x192x1 in size
IMG_WIDTH = 256
IMG_HEIGHT = 256

NUMBER_OF_PARALLEL_CALL = 8
PARSHING = 8
MODEL_TO_RUN ='Model_28p5mAs_to57mAs' # pick which model you want to run
# for cGAN-SD5.7 MODEL_TO_RUN=='Model_5p7mAs_to57mAs'
# for cGAN-SD28.5 MODEL_TO_RUN=='Model_28p5mAs_to57mAs'
# for cGAN-DD MODEL_TO_RUN=='Model_5P7and28p5mAs_to57mAs'

LAMBDA = 100

ROOT_SAVING_PATHS='/data/usr/ft42/CVIT_Denoising/Denoising_pix2pix_model/'

if MODEL_TO_RUN=='Model_5p7mAs_to57mAs':
BUFFER_SIZE = 15750
TRAINING_DATASET_SIZE = 15750
VALIDATION_DATASET_SIZE = 6300
TEST_DATASET_SIZE = 6300

LOGDIR = ROOT_SAVING_PATHS+MODEL_TO_RUN+'/logs/'
CHECKPOINT_DIR = ROOT_SAVING_PATHS+MODEL_TO_RUN+'/training_checkpoints'
GENERATE_IMAGE_DIR = ROOT_SAVING_PATHS+MODEL_TO_RUN+'/generated_denoising_img/'

TRAIN_TFRECORDS_CSV = '5p7_28p5_57mAs_train_tfrecords.csv'
TRAIN_TFRECORDS_PATH = '/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7_28p5_57/5p7_28p5_57_tfrecords/'

VALIDATION_TFRECORDS_CSV = '5p7_28p5_57mAs_val_tfrecords.csv'
VALIDATION_TFRECORDS_PATH = '/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7_28p5_57/5p7_28p5_57_tfrecords/'

TEST_TFRECORDS_CSV = '5p7_28p5_57mAs_test_tfrecords.csv'
TEST_TFRECORDS_PATH = '/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7_28p5_57/5p7_28p5_57_tfrecords/'

DATA_CACHE_TR = ROOT_SAVING_PATHS+MODEL_TO_RUN+"_train"
DATA_CACHE_VAL = ROOT_SAVING_PATHS+MODEL_TO_RUN+"_val"
VALIDATION_PSNR_SSIM_CSV_PATH = ROOT_SAVING_PATHS+MODEL_TO_RUN+'/'+MODEL_TO_RUN+'_validation_df.csv'
GENERATE_IMAGE_CSV= '5p7_28p5_57mAs_Generate_tfrecords.csv'

if MODEL_TO_RUN=='Model_28p5mAs_to57mAs':

BUFFER_SIZE = 15750
TRAINING_DATASET_SIZE = 15750
VALIDATION_DATASET_SIZE = 6300
TEST_DATASET_SIZE = 6300

if MODEL_TO_RUN=='Model_5P7and28p5mAs_to57mAs':

BUFFER_SIZE = 15750*2
TRAINING_DATASET_SIZE = 15750*2
VALIDATION_DATASET_SIZE = 6300*2
TEST_DATASET_SIZE = 6300*2

TRAIN_TFRECORDS_CSV = '5p7And28p5_57mAs_train_tfrecords.csv'
TRAIN_TFRECORDS_PATH = '/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7and28p5_57/5p7and28p5_57_tfrecords/'

VALIDATION_TFRECORDS_CSV = '5p7And28p5_57mAs_val_tfrecords.csv'
VALIDATION_TFRECORDS_PATH = '/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7and28p5_57/5p7and28p5_57_tfrecords/'

TEST_TFRECORDS_CSV = '5p7And28p5_57mAs_test_tfrecords.csv'
TEST_TFRECORDS_PATH = '/data/usr/ft42/nobackup/denoising_data_and_tfrecords/denoising_tfrecords/tfrecords_DN_5p7and28p5_57/5p7and28p5_57_tfrecords/'

GENERATE_IMAGE_CSV = '5p7And28p5_57mAs_Generate_tfrecords.csv'

```

* to train: python train_pix2pix_2D_denoising.py
* to test : python test_pix2pix_2D_denoising.py
* to generate predicted denoised image : python generate_image_numpy.py

## similarly to train, Test and generated predicted images for cGAN-Dy use codes with in the folder: https://github.com/fitushar/Study-of-Low-dose-to-High-dose-CT-using-Supervised-Learning-with-GAN-and-Virtual-Imaging-Trials/tree/main/cGAN_Dy

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