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This set of software tools includes OpenCL implementation of modern CT and CBCT reconstruction algorithms including unpublished algorithms by the author.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
本步态识别系统主要基于GaitSet模型进行实现

本步态识别系统主要基于GaitSet模型进行实现。在尝试部署本系统之前,建立理解GaitSet模型的网络结构、训练和推理方法。 系统的实现效果如视频所示: 演示视频 由于模型较大,部分模型文件存储在百度云盘。 链接提取码:33mb 具体部署过程 1.下载代码 2.安装requirements.txt

16 Oct 22, 2022
I tried to apply the CAM algorithm to YOLOv4 and it worked.

YOLOV4:You Only Look Once目标检测模型在pytorch当中的实现 2021年2月7日更新: 加入letterbox_image的选项,关闭letterbox_image后网络的map得到大幅度提升。 目录 性能情况 Performance 实现的内容 Achievement

55 Dec 05, 2022
Replication Code for "Self-Supervised Bug Detection and Repair" NeurIPS 2021

Self-Supervised Bug Detection and Repair This is the reference code to replicate the research in Self-Supervised Bug Detection and Repair in NeurIPS 2

Microsoft 85 Dec 24, 2022
FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation

FCN_via_Keras FCN FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This

Kento Watanabe 48 Aug 30, 2022
ConvMixer unofficial implementation

ConvMixer ConvMixer 非官方实现 pytorch 版本已经实现。 nets 是重构版本 ,test 是官方代码 感兴趣小伙伴可以对照看一下。 keras 已经实现 tf2.x 中 是tensorflow 2 版本 gelu 激活函数要求 tf=2.4 否则使用入下代码代替gelu

Jian Tengfei 8 Jul 11, 2022
Diverse graph algorithms implemented using JGraphT library.

# 1. Installing Maven & Pandas First, please install Java (JDK11) and Python 3 if they are not already. Next, make sure that Maven (for importing J

See Woo Lee 3 Dec 17, 2022
This repo. is an implementation of ACFFNet, which is accepted for in Image and Vision Computing.

Attention-Guided-Contextual-Feature-Fusion-Network-for-Salient-Object-Detection This repo. is an implementation of ACFFNet, which is accepted for in I

5 Nov 21, 2022
Eth brownie struct encoding example

eth-brownie struct encoding example Overview This repository contains an example of encoding a struct, so that it can be used in a function call, usin

Ittai Svidler 2 Mar 04, 2022
A Unified Generative Framework for Various NER Subtasks.

This is the code for ACL-ICJNLP2021 paper A Unified Generative Framework for Various NER Subtasks. Install the package in the requirements.txt, then u

177 Jan 05, 2023
An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

Rugby score prediction An end-to-end machine learning web app to predict rugby scores Overview An demo project to provide a high-level overview of the

34 May 24, 2022
[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations

VirTex: Learning Visual Representations from Textual Annotations Karan Desai and Justin Johnson University of Michigan CVPR 2021 arxiv.org/abs/2006.06

Karan Desai 533 Dec 24, 2022
Convert human motion from video to .bvh

video_to_bvh Convert human motion from video to .bvh with Google Colab Usage 1. Open video_to_bvh.ipynb in Google Colab Go to https://colab.research.g

Dene 306 Dec 10, 2022
SynNet - synthetic tree generation using neural networks

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

Wenhao Gao 60 Dec 29, 2022
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:

Ravi Garg 227 Nov 28, 2022
Code for our WACV 2022 paper "Hyper-Convolution Networks for Biomedical Image Segmentation"

Hyper-Convolution Networks for Biomedical Image Segmentation Code for our WACV 2022 paper "Hyper-Convolution Networks for Biomedical Image Segmentatio

Tianyu Ma 17 Nov 02, 2022
The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue. How do I cite D-REX? For now, cite

Alon Albalak 6 Mar 31, 2022
Reaction SMILES-AA mapping via language modelling

rxn-aa-mapper Reactions SMILES-AA sequence mapping setup conda env create -f conda.yml conda activate rxn_aa_mapper In the following we consider on ex

16 Dec 13, 2022
QT Py Media Knob using rotary encoder & neopixel ring

QTPy-Knob QT Py USB Media Knob using rotary encoder & neopixel ring The QTPy-Knob features: Media knob for volume up/down/mute with "qtpy-knob.py" Cir

Tod E. Kurt 56 Dec 30, 2022
A PyTorch implementation of the continual learning experiments with deep neural networks

Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: Brain

182 Dec 27, 2022
Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Gra

32 Dec 26, 2022