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TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox
======
TIGRE is an open-source toolbox for fast and accurate 3D tomographic
reconstruction for any geometry. Its focus is on iterative algorithms
for improved image quality that have all been optimized to run on GPUs
(including multi-GPUs) for improved speed. It combines the higher level
abstraction of MATLAB or Python with the performance of CUDA at a lower level in order to make
it both fast and easy to use.
TIGRE is free to download and distribute: use it, modify it, add to it,
share it. Our aim is to provide a wide range of easy-to-use algorithms
for the tomographic community "off the shelf". We would like to build a
stronger bridge between algorithm developers and imaging
researchers/clinicians by encouraging and supporting contributions from
both sides into TIGRE.
TIGRE remains under development as we are still adding new features
(e.g., motion compensation). If you have any request for a specific
application, do not hesitate to [contact us](#contact) or open a [discussion thread](https://github.com/CERN/TIGRE/discussions)!
- [TIGRE features](#features)
- [Installation instructions](#installation)
- [FAQ](#faq)
- [Further reading](#further-reading)
- [Contact](#contact)
- [Licensing](#licensing)
## TIGRE features
TIGRE is a GPU-based CT reconstruction software repository that contains a wide variety of iterative algorithms.
- **MATLAB** and **Python** libraries for high-performance x-ray absorption tomographic reconstruction.
- State-of-the-art implementations of projection and backprojection operations on **GPUs** (including **multi-GPUs**), with a simple interface using higher level languages to facilitate the development of new methods.
- **Flexible CT geometry:** Cone Beam, Parallel Beam, Digital Tomosynthesis, C-arm CT, and any other geometry. Geometric parameters are defined per projection, not per scan.
- A wide range of reconstruction algorithms for CT.
- Filtered backprojection (FBP,FDK) and variations (different filters, Parker weights, ...)
- **Iterative algorithms**
- Gradient-based algorithms (SART, OS-SART, SIRT) with multiple tuning parameters (Nesterov acceleration, initialization, parameter reduction, ...)
- Krylov subspace algorithms (CGLS)
- Statistical reconstruction (MLEM)
- Total variation regularization based algorithms: proximal-based (FISTA, SART-TV) and POCS-based (ASD-POCS, OS-ASD-POCS, B-ASD-POCS-β, PCSD, AwPCSD, Aw-ASD-POCS)
- TV denoising for 3D images.
- Basic image loading functionality.
- A variety of plotting functions.
- Image quality metrics.
- Nikon and Varian and Phillips (DICOM) scanner data loaders.
## Installation
MATLAB and Python builds are both fully supported.
- [Installation instructions and requirements for MATLAB](Frontispiece/MATLAB_installation.md).
- [Installation instructions and requirements for Python](Frontispiece/python_installation.md).
**Advanced, not required to run TIGRE**, will change the source code. Only do if performance is critical.
- [Tune TIGRE for machine. Tricks to slightly speed up the code](Frontispiece/Tune_TIGRE.md)
## FAQ
For answers to frequently asked questions [click here](Frontispiece/FAQ.md).
If you have new question not answered in the FAQ, please [contact us](#contact), join the [Slack group](#contact) or open a [discussion thread](https://github.com/CERN/TIGRE/discussions).
## Gallery
To see a gallery of images of different CT modalities reconstructed using TIGRE [click here](Frontispiece/Gallery.md).
<img src="https://raw.githubusercontent.com/AnderBiguri/PhDThesis/master/Applications/randofull.png" height="400">
## Further Reading
If you want more information on TIGRE and its algorithms, [click here](Frontispiece/Further_reading.md).
## Contact
Contact the authors directly at:
[tigre.toolbox@gmail.com](mailto:tigre.toolbox@gmail.com) or [ander.biguri@gmail.com](mailto:ander.biguri@gmail.com)
for any questions/comments or if you want to be added to the mailing list or the Slack team.
The Slack team is a good place for chatting about development and questions about TIGRE. Please send an email to the authors and you will receive an invitation.
## Licensing
The creation of TIGRE was supported by the University of Bath and CERN. It is released under the BSD License, meaning you can use and modify the software freely. However, you **must** cite the original authors.
For more information read [the licence file][1] or the [BSD License Definition][2].
If you use TIGRE, please reference the following papers:
**TIGRE: A MATLAB-GPU toolbox for CBCT image reconstruction**
*Ander Biguri, Manjit Dosanjh, Steven Hancock and Manuchehr Soleimani*
**Biomedical Physics & Engineering Express, Volume 2, Number 5**
[Read the article (open access)][3]
And especially if you use images bigger than 512<sup>3</sup> or multiple GPUs
**Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox**
*Ander Biguri, Reuben Lindroos, Robert Bryll, Hossein Towsyfyan, Hans Deyhle, Ibrahim El khalil Harrane, Richard
Boardman, Mark Mavrogordato, Manjit Dosanjh, Steven Hancock, Thomas Blumensath*
**Journal of Parallel and Distributed Computing**
[Read the article][4],
[Preprint][5]
[1]: LICENSE.txt
[2]: http://www.linfo.org/bsdlicense.html
[3]: http://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010
[4]: https://doi.org/10.1016/j.jpdc.2020.07.004
[5]: https://arxiv.org/abs/1905.03748
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
可以直接运行。毕业设计、小论文均可采用,方便快捷, 感兴趣区域CT重建是指利用计算机断层扫描(CT)技术,对感兴趣区域(ROI)内的图像数据进行重建,以获取更高质量的图像。通常情况下,CT扫描会生成包含整个扫描区域的图像,但在某些情况下,只需对感兴趣的特定区域进行重建,可以减少辐射剂量和图像处理的复杂性。 感兴趣区域CT重建涉及以下关键技术和步骤: 感兴趣区域的确定:在进行CT扫描之前,需要确定感兴趣的区域。这可以通过临床诊断需求、病变位置等因素来确定。 扫描参数的选择:针对感兴趣区域,可以调整CT扫描的参数,如辐射剂量、扫描速度、层厚等,以获得更好的图像质量。 重建算法的选择:选择合适的重建算法对感兴趣区域内的图像进行重建。常用的算法包括滤波反投影算法、迭代重建算法等。 图像处理和优化:对重建后的图像进行处理和优化,如去噪、增强、几何校正等,以提高图像质量和准确性。 临床应用:将重建后的图像应用于临床诊断和治疗规划,为医生提供更准确的信息和依据。 感兴趣区域CT重建技术在医学影像领域具有重要意义,可以帮助医生更准确地诊断病变、评估治疗效果,同时减少辐射剂量对患者的影响。
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感兴趣区域CT重建代码,包括详细的matlab、python代码以及数据 局部CT重建,CT内重建,外重建 (514个子文件)
cycle.asv 387B
TIGRE.bib 17KB
TIGRE.bib 14KB
149.bmp 257KB
Atb_mex.cpp 13KB
Ax_mex.cpp 13KB
mexReadXim.cpp 8KB
tvDenoise.cpp 5KB
AwminTV.cpp 5KB
AddNoise.cpp 5KB
minTV.cpp 5KB
pCTCubicSpline_mex.cpp 4KB
imgTVGradMin.cpp 4KB
GpuIds.cpp 2KB
projection.cpp 1KB
getGpuName_mex.cpp 869B
getGpuCount_mex.cpp 588B
TIGRE_common.cpp 566B
improvedForwardProjections_cone.cu 54KB
voxel_backprojection.cu 46KB
voxel_backprojection2.cu 44KB
improvedForwardProjections.cu 42KB
Siddon_projection.cu 39KB
ray_interpolated_projection.cu 39KB
POCS_TV2.cu 33KB
POCS_TV.cu 33KB
tvdenoising.cu 32KB
voxel_backprojection_parallel.cu 31KB
Siddon_projection_parallel.cu 21KB
ray_interpolated_projection_parallel.cu 18KB
RandomNumberGenerator.cu 8KB
gpuUtils.cu 2KB
cufft64_90.dll 125.12MB
AstraCuda64.dll 8.04MB
cudart64_90.dll 365KB
vc_redist.x64.exe 13.9MB
astra_data_gui.fig 6KB
.flake8 477B
.gitattributes 30B
.gitignore 923B
improvedForwardProjections.hpp 4KB
types_TIGRE.hpp 3KB
voxel_backprojection2.hpp 3KB
ray_interpolated_projection.hpp 3KB
voxel_backprojection.hpp 3KB
Siddon_projection.hpp 3KB
ray_interpolated_projection_parallel.hpp 3KB
voxel_backprojection_parallel.hpp 3KB
Siddon_projection_parallel.hpp 3KB
RandomNumberGenerator.hpp 3KB
POCS_TV2.hpp 2KB
tvdenoising.hpp 2KB
POCS_TV.hpp 2KB
TIGRE_common.hpp 755B
gpuUtils.hpp 608B
XimPara.hpp 588B
GpuIds.hpp 373B
projection.hpp 205B
errors.hpp 160B
TIGRE.iml 398B
MANIFEST.in 88B
AstraCuda64.lib 972KB
LICENSE 2KB
ImageType.m 20KB
FeatureSIM.m 19KB
plasma.m 16KB
inferno.m 16KB
OS_ASD_POCS.m 16KB
B_ASD_POCS_beta.m 16KB
astra_data_gui.m 16KB
magma.m 15KB
OS_AwASD_POCS.m 15KB
IterativeTomography.m 15KB
OS_AwPCSD.m 15KB
OS_SART.m 15KB
AwASD_POCS.m 14KB
IterativeTomography3D.m 14KB
ASD_POCS.m 14KB
AwPCSD.m 13KB
SART.m 13KB
SART_TV.m 13KB
viridis.m 12KB
PCSD.m 12KB
Compile.m 11KB
SIRT.m 11KB
Interior_Reconstruction_mian_SIRT_GPU.m 11KB
Interior_Reconstruction_mian_SIRT_GPU.m 11KB
stlWrite.m 10KB
astra_create_proj_geom.m 10KB
mSTCT_DBP2_astra1_9_zaosheng.m 10KB
opTomo.m 9KB
phantom3dAniso.m 9KB
phantom3dAniso.m 9KB
phantom3dAniso.m 9KB
d09_Algorithms04.m 9KB
DARTalgorithm.m 8KB
FDK.m 8KB
CompilePCT.m 8KB
mSTCT_DBP2_astra1_9_cycle.m 8KB
plotImg.m 7KB
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