实例介绍
基于C++编写的CT图像重建例子,包含CT原始数据
【实例截图】
【核心代码】
CCT
└── C++CT重建
└── CT_Reconstruction
└── CT重建仿真
├── CTconv_reconstuct
│ ├── CTconv_reconstuct.cpp
│ ├── CTconv_reconstuct.sdf
│ ├── CTconv_reconstuct.sln
│ ├── CTconv_reconstuct.suo
│ ├── CTconv_reconstuct.v12.suo
│ ├── CTconv_reconstuct.vcproj
│ ├── CTconv_reconstuct.vcproj.SIMONXU.Simon Xu.user
│ ├── CTconv_reconstuct.vcxproj
│ ├── CTconv_reconstuct.vcxproj.filters
│ ├── CTconv_reconstuct.vcxproj.user
│ ├── Debug
│ │ ├── CTconv_r.C6F9329B.tlog
│ │ │ ├── cl.command.1.tlog
│ │ │ ├── CL.read.1.tlog
│ │ │ ├── CL.write.1.tlog
│ │ │ ├── CTconv_reconstuct.lastbuildstate
│ │ │ ├── link.command.1.tlog
│ │ │ ├── link.read.1.tlog
│ │ │ └── link.write.1.tlog
│ │ ├── CTconv_reconstuct.Build.CppClean.log
│ │ ├── CTconv_reconstuct.exe
│ │ ├── CTconv_reconstuct.ilk
│ │ ├── CTconv_reconstuct.log
│ │ ├── CTconv_reconstuct.obj
│ │ ├── CTconv_reconstuct.pch
│ │ ├── CTconv_reconstuct.pdb
│ │ ├── stdafx.obj
│ │ ├── vc120.idb
│ │ └── vc120.pdb
│ ├── ipch
│ │ ├── ctconv_reconstuct-91fab066
│ │ │ └── ctconv_reconstuct-e53b8102.ipch
│ │ └── ctconv_reconstuct-d9859939
│ │ └── ctconv_reconstuct-e53b8102.ipch
│ ├── ReadMe.txt
│ ├── S-L Head Model Back Projection Reconstruction.bmp
│ ├── S-L Head Model.bmp
│ ├── stdafx.cpp
│ ├── stdafx.h
│ ├── UpgradeLog.XML
│ └── _UpgradeReport_Files
│ ├── UpgradeReport.css
│ ├── UpgradeReport_Minus.gif
│ ├── UpgradeReport_Plus.gif
│ └── UpgradeReport.xslt
├── Noisy(SNR=10) S-L Head Model R-L Convolution Back Projection_1degree_Scan.bmp
├── Noisy(SNR=10) S-L Head Model S-L Convolution Back Projection_1degree_Scan.bmp
├── Noisy(SNR=25) S-L Head Model Direct Back Projection_1degree_Scan.bmp
├── Noisy(SNR=25) S-L Head Model My Convolution(121P) Back Projection_1degree_Scan.bmp
├── Noisy(SNR=25) S-L Head Model My Convolution3(121P) Back Projection_1degree_Scan.bmp
├── Noisy(SNR=25) S-L Head Model R-L Convolution Back Projection_1degree_Scan.bmp
├── Noisy(SNR=25) S-L Head Model S-L Convolution Back Projection_1degree_Scan.bmp
├── Noisy(SNR=5) S-L Head Model Direct Back Projection_1degree_Scan.bmp
├── Noisy(SNR=5) S-L Head Model My Convolution(121P) Back Projection_1degree_Scan.bmp
├── Noisy(SNR=5) S-L Head Model R-L Convolution Back Projection_1degree_Scan.bmp
├── Noisy(SNR=5) S-L Head Model S-L Convolution Back Projection_1degree_Scan.bmp
├── Readme.txt
├── S-L Head Model.bmp
├── S-L Head Model Direct Back Projection_10degree_Scan.bmp
├── S-L Head Model Direct Back Projection_1degree_Scan.bmp
├── S-L Head Model Direct Back Projection_20degree_Scan.bmp
├── S-L Head Model Direct Back Projection_5degree_Scan.bmp
├── S-L Head Model My Convolution(121P) Back Projection_10degree_Scan.bmp
├── S-L Head Model My Convolution(121P) Back Projection_1degree_Scan.bmp
├── S-L Head Model My Convolution2(121P) Back Projection_1degree_Scan.bmp
├── S-L Head Model My Convolution3(121P) Back Projection_1degree_Scan.bmp
├── S-L Head Model My Convolution(51P) Back Projection_10degree_Scan.bmp
├── S-L Head Model My Convolution(51P) Back Projection_1degree_Scan.bmp
├── S-L Head Model R-L Convolution(201P) Back Projection_1degree_Scan.bmp
├── S-L Head Model R-L Convolution(41P) Back Projection_1degree_Scan.bmp
├── S-L Head Model R-L Convolution(51P) Back Projection_10degree_Scan.bmp
├── S-L Head Model R-L Convolution Back Projection_10degree_Scan.bmp
├── S-L Head Model R-L Convolution Back Projection_1degree_Scan.bmp
├── S-L Head Model R-L Convolution Back Projection_20degree_Scan.bmp
├── S-L Head Model R-L Convolution Back Projection_5degree_Scan.bmp
├── S-L Head Model S-L Convolution(201P) Back Projection_1degree_Scan.bmp
├── S-L Head Model S-L Convolution(41P) Back Projection_1degree_Scan.bmp
├── S-L Head Model S-L Convolution(51P) Back Projection_10degree_Scan.bmp
├── S-L Head Model S-L Convolution Back Projection_10degree_Scan.bmp
├── S-L Head Model S-L Convolution Back Projection_1degree_Scan.bmp
├── S-L Head Model S-L Convolution Back Projection_20degree_Scan.bmp
├── S-L Head Model S-L Convolution Back Projection_5degree_Scan.bmp
└── Thumbs.db
10 directories, 77 files
标签:
小贴士
感谢您为本站写下的评论,您的评论对其它用户来说具有重要的参考价值,所以请认真填写。
- 类似“顶”、“沙发”之类没有营养的文字,对勤劳贡献的楼主来说是令人沮丧的反馈信息。
- 相信您也不想看到一排文字/表情墙,所以请不要反馈意义不大的重复字符,也请尽量不要纯表情的回复。
- 提问之前请再仔细看一遍楼主的说明,或许是您遗漏了。
- 请勿到处挖坑绊人、招贴广告。既占空间让人厌烦,又没人会搭理,于人于己都无利。
关于好例子网
本站旨在为广大IT学习爱好者提供一个非营利性互相学习交流分享平台。本站所有资源都可以被免费获取学习研究。本站资源来自网友分享,对搜索内容的合法性不具有预见性、识别性、控制性,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,平台无法对用户传输的作品、信息、内容的权属或合法性、安全性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论平台是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二与二十三条之规定,若资源存在侵权或相关问题请联系本站客服人员,点此联系我们。关于更多版权及免责申明参见 版权及免责申明
网友评论
我要评论