Fast, accurate and reliable software for algebraic CT reconstruction

Related tags

Deep LearningKCT_cbct
Overview

KCT CBCT

Fast, accurate and reliable software for algebraic CT reconstruction.

This set of software tools includes OpenCL implementation of modern CT and CBCT reconstruction algorithms including unpublished algorithms by the author. Initially, the focus was on CT reconstruction using Krylov LSQR and CGLS methods. Gradually, other widely used methods such as OS-SIRT are added. Initially, the software was based on the idea of a projector that directly computes the projections of individual voxels onto pixels using the volume integrals of the voxel cuts. The author intends to publish a paper on this cutting voxel projector (CVP) in late 2021. However, the package also includes implementations of the TT projector and the Siddon projector the DD and TR projectors will be implemented in the near future. The code for the CVP projector is optimized using OpenCL local memory and is probably one of the fastest projector implementations ever for algebraic reconstruction.

The package has been tested and is compatible with the AMD Radeon VII Vega 20 GPU and NVIDIA GeForce RTX 2080 Ti GPU. Some routines have been optimized specifically for these GPU architectures. OpenCL code conforms to the OpenCL 1.2 specification and the implementation uses C++ wrappers from OpenCL 1.2. OpenCL 2.0 is not supported due to lack of support from NVidia.

Algorithms

Cutting voxel projector yet to be published.

LSQR algorithm was implemented according to https://doi.org/10.1002/nla.611

CGLS algorithm with delayed residual computation as described in the proceedings of Fully3D conference 2021 Software Implementation of the Krylov Methods Based Reconstruction for the 3D Cone Beam CT Operator Poster and extendend absract can be found in the publications directory

Repositories

The KCT package is hosted on Bitbucket and GitHub

GitHub public repository

git clone https://github.com/kulvait/KCT_cbct.git

Bitbucket public repository

git clone https://bitbucket.org/kulvait/kct_cbct.git

Submodules

Submodules lives in the submodules directory. To clone project including submodules one have to use the following commands

git submodule init
git submodule update

or use the following command when cloning repository

git clone --recurse-submodules

CTIOL

Input output routines for asynchronous thread safe reading/writing CT data. The DEN format read/write is implemented.

CTMAL

Mathematic/Algebraic algorithms for supporting CT data manipulation.

Plog logger

Logger Plog is used for logging. It is licensed under the Mozilla Public License Version 2.0.

CLI11

Comand line parser CLI11. It is licensed under 3 Clause BSD License.

Catch2

Testing framework. Licensed under Boost Software License 1.0.

CTPL

Threadpool library.

Documentation

Documentation is generated using doxygen and lives in doc directory. First the config file for doxygen was prepared runing doxygen -g. Doc files and this file can be written using Markdown syntax, JAVADOC_AUTOBRIEF is set to yes to treat first line of the doc comment as a brief description, comments are of the format

/**Brief description.
*
*Long description
*thay might span multiple lines.
*/

.

Licensing

When there is no other licensing and/or copyright information in the source files of this project, the following apply for the source files in the directories include and src and for CMakeLists.txt file:

Copyright (C) 2018-2021 Vojtěch Kulvait

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.

This licensing applies to the direct source files in the directories include and src of this project and not for submodules.

Owner
Vojtěch Kulvait
2018-2021 PostDoc at Magdeburg University, CT reconstruction
Vojtěch Kulvait
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs This is an implemetation of the paper Few-shot Relation Extraction via Baye

MilaGraph 36 Nov 22, 2022
Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022

LDL Paper | Supplementary Material Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution Jie Liang*, Hu

150 Dec 26, 2022
Image Segmentation Animation using Quadtree concepts.

QuadTree Image Segmentation Animation using QuadTree concepts. Usage usage: quad.py [-h] [-fps FPS] [-i ITERATIONS] [-ws WRITESTART] [-b] [-img] [-s S

Alex Eidt 29 Dec 25, 2022
Deep Multimodal Neural Architecture Search

MMNas: Deep Multimodal Neural Architecture Search This repository corresponds to the PyTorch implementation of the MMnas for visual question answering

Vision and Language Group@ MIL 23 Dec 21, 2022
Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

VT-UNet This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of VT-UNet. Environmen

Himashi Amanda Peiris 114 Dec 20, 2022
PyTorch implementation of Super SloMo by Jiang et al.

Super-SloMo PyTorch implementation of "Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation" by Jiang H., Sun

Avinash Paliwal 2.9k Jan 03, 2023
Neural network-based build time estimation for additive manufacturing

Neural network-based build time estimation for additive manufacturing Oh, Y., Sharp, M., Sprock, T., & Kwon, S. (2021). Neural network-based build tim

Yosep 1 Nov 15, 2021
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility

PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility Jae Yong Lee, Joseph DeGol, Chuhang Zou, Derek Hoiem Installation To install nece

31 Apr 19, 2022
PyTorch Implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedding (ORAL, MICCAIW 2021)

Small Lesion Segmentation in Brain MRIs with Subpixel Embedding PyTorch implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedd

22 Oct 21, 2022
Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi

81 Dec 15, 2022
Few-shot Neural Architecture Search

One-shot Neural Architecture Search uses a single supernet to approximate the performance each architecture. However, this performance estimation is super inaccurate because of co-adaption among oper

Yiyang Zhao 38 Oct 18, 2022
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

BossNAS This repository contains PyTorch evaluation code, retraining code and pretrained models of our paper: BossNAS: Exploring Hybrid CNN-transforme

Changlin Li 127 Dec 26, 2022
TensorFlow-based neural network library

Sonnet Documentation | Examples Sonnet is a library built on top of TensorFlow 2 designed to provide simple, composable abstractions for machine learn

DeepMind 9.5k Jan 07, 2023
RetinaFace: Deep Face Detection Library in TensorFlow for Python

RetinaFace is a deep learning based cutting-edge facial detector for Python coming with facial landmarks.

Sefik Ilkin Serengil 512 Dec 29, 2022
Dynamic Token Normalization Improves Vision Transformers

Dynamic Token Normalization Improves Vision Transformers This is the PyTorch implementation of the paper Dynamic Token Normalization Improves Vision T

Wenqi Shao 20 Oct 09, 2022
M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images This repo is the official implementation of paper "M2MRF: Man

12 Dec 14, 2022
Select, weight and analyze complex sample data

Sample Analytics In large-scale surveys, often complex random mechanisms are used to select samples. Estimates derived from such samples must reflect

samplics 37 Dec 15, 2022
PyKaldi GOP-DNN on Epa-DB

PyKaldi GOP-DNN on Epa-DB This repository has the tools to run a PyKaldi GOP-DNN algorithm on Epa-DB, a database of non-native English speech by Spani

18 Dec 14, 2022
Paper: De-rendering Stylized Texts

Paper: De-rendering Stylized Texts Wataru Shimoda1, Daichi Haraguchi2, Seiichi Uchida2, Kota Yamaguchi1 1CyberAgent.Inc, 2 Kyushu University Accepted

CyberAgent AI Lab 55 Dec 18, 2022