惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

月光博客
月光博客
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
N
Netflix TechBlog - Medium
大猫的无限游戏
大猫的无限游戏
爱范儿
爱范儿
Martin Fowler
Martin Fowler
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
The Register - Security
The Register - Security
IT之家
IT之家
博客园_首页
Microsoft Security Blog
Microsoft Security Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 三生石上(FineUI控件)
I
InfoQ
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
Jina AI
Jina AI
Apple Machine Learning Research
Apple Machine Learning Research
M
MIT News - Artificial intelligence
博客园 - Franky
C
Check Point Blog
T
The Blog of Author Tim Ferriss
V
Visual Studio Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
T
Tailwind CSS Blog
Recent Announcements
Recent Announcements
云风的 BLOG
云风的 BLOG
美团技术团队
The Cloudflare Blog
Y
Y Combinator Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
MyScale Blog
MyScale Blog
The GitHub Blog
The GitHub Blog
D
DataBreaches.Net
Google DeepMind News
Google DeepMind News
V
V2EX
aimingoo的专栏
aimingoo的专栏
GbyAI
GbyAI
G
Google Developers Blog
S
SegmentFault 最新的问题
Hugging Face - Blog
Hugging Face - Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
U
Unit 42
罗磊的独立博客
量子位
MongoDB | Blog
MongoDB | Blog
Last Week in AI
Last Week in AI
Stack Overflow Blog
Stack Overflow Blog
小众软件
小众软件
D
Docker
人人都是产品经理
人人都是产品经理

Long Luo's Life Notes

夏至日测地球:利用太阳影子计算地球半径 太阳温度是怎么计算出来的? 《大象的时间,老鼠的时间》读书笔记:生命节奏背后的数学规律 小港流到哪里去? 如何用一根棍子测出地球有多大?复刻埃拉托色尼的春分实验 2007江苏高考数学第20题解析:一道通向黄金分割数的数列压轴题 Google经典面试题: 鸡蛋应该怎么扔? 2010年江苏高考数学压轴题解析:巧用余弦定理与数学归纳法 2011年清华大学自主招生数学题解析:一道经典数列题的解法与思路 2011年清华大学自主招生数学题解析:一道经典数列题的解法与思路 2006年江西高考理科数学压轴题解析:递推、放缩与不等式结构 2006年江西高考理科数学压轴题解析:递推、放缩与不等式结构 一道初中数学极值题的多种解法:柯西不等式、几何法、函数法详解 扔几个骰子,怎么算出期望?——拼多多校招笔试算法题的数学故事 拼多多校招笔试算法题:一行公式搞定“多多的魔术盒子” 斯特林公式(Stirling's Formula):我一个阶乘表达式,怎么就和圆扯上关系了呢? 我爱做题:2010年江西高考理科数学压轴题 热机的效率上限在哪里?解析卡诺循环(Carnot Cycle) 为什么 2024 年会有 366 天? 数学之美:几何视角下的高斯积分(Gaussian Integral) 从最小二乘法到正态分布:高斯是如何找到失踪的谷神星的? 正态分布(Normal Distribution)公式为什么长这样? 高速公路编号背后的数学密码 2024阿里巴巴全球数学竞赛预选赛试题及解答 库函数 (libm) 是如何计算三角函数值的? payne hanek 归约算法 音乐背后的数学 素描背后的物理 cody waite 浮点数 Remez Algorithm 参数归约算法(Argument Range Reduction):如何在浮点数环境下计算超大数字的三角函数值? 素描背后的数学 发生在计算机内存里的进化:解密遗传算法(Genetic Algorithm) CORDIC算法:一种高效计算三角函数值的方法 墨卡托的魔术:地图是如何欺骗你的眼睛的? PID 算法到底在干什么?工程师最常用的控制方法 解密卡尔曼滤波(Kalman Filter)算法:深入解析卡尔曼滤波算法原理与在线可视化实例 从记忆到洞察:轻松掌握泰勒展开式(Taylor Series)的记忆技巧 哪个更大呢? $2^{100!}$ 还是 $2^{100}!$ ? Google经典编程竞赛题:计算 $(3 + \sqrt{5})^n$ 的小数点前三位数 手写数字识别:解码机器学习的背后的数学原理 gdb 操作指南 Linux 网络命令指南 贝塞尔曲线(Bezier Curve):优雅背后的数学原理 LeetCode 380. Insert Delete GetRandom O(1) Data Structures: Thought Process from HashMap to HashMap + Array LeetCode 2475. 数组中不等三元组的数目 2种 O(n) 时间复杂度算法 LeetCode 947. Most Stones Removed with Same Row or Column It is Literally a Graph: DFS and Union Find LeetCode 295. Find Median from Data Stream Two Heaps with the Follow Ups LeetCode 295. Find Median from Data Stream Two Heaps with the Follow Ups LeetCode 1668. 最大重复子字符串 不用API,比KMP更易理解简洁优雅的暴力解法 LeetCode 334. Increasing Triplet Subsequence Why Greedy Works? LeetCode 迷宫问题(The Maze)
The Answers of MRI Tutorial Videos
2023-02-11 · via Long Luo's Life Notes

By Frank Luo

This is my answers of the MRI Tutorial Videos How MRI Works - Part 2: The Spin Echo and How MRI Works - Part 3:Fourier Transform and K-Space .

Part 2: The Spin Echo

Questions

Part 2 Questions 1
Part 2 Question 2

Answers

Question 1:

  1. The Boltzmann Magetization \(M_0 = \frac{N {\gamma}^2 \hbar^2 B_0}{4 k T}\), then after elimination the units is \(J/T\).
  2. The Polarization is \(P = \frac{\gamma \hbar B_0}{2kT}\), then after elimination we can get that \(P\) is a special number depends on the material, no SI units.

Question 2:

  1. The polarization is \(P = \frac{51-49}{100} = 0.02\) .
  2. The magnet field strength should be \(B_0 = \frac{0.02}{0.0000034} \approx 5882T\) .
  3. The temperature should be \(T = \frac{300 \times 0.0000034}{0.02} = 0.051K\).

Question 3:

  1. Since the Boltzmann Magetization Equation is \(M = M_0(1- e^{-\frac{t}{T_1}}) e^{-\frac{t}{T_2}}\) , so we can calculate the signal.

The signal of Tissue \(A\) : \(M_A = M_0(1- e^{-\frac{150}{300}}) e^{-\frac{12.5}{20}} = 0.21\) . The signal of Tissue \(B\) : \(M_B = M_0(1- e^{-\frac{150}{200}}) e^{-\frac{12.5}{40}} = 0.38\) .

Surely Tissue \(B\) will deliver more signal.

  1. We have calculated that Tissue \(B\) will deliver more signal if both Tissue \(A\) and \(B\) has the same Boltzmann Magetization.

If Tissue \(A\) is \(85\%\) of Tissue \(B\), then the Tissue \(A\) signal will become lesser, so Tissue \(B\) deliver more signal.

  1. Let function \(f(t) = M_{0A}(1- e^{-\frac{TR}{T_{1A}}})e^{-\frac{t}{T_{2A}}} - M_{0B}(1- e^{-\frac{TR}{T_{1B}}})e^{-\frac{t}{T_{2B}}}\) reprent the signal of time \(t\).

Consider the function: \(f(t) = (1 - e^{-\frac{150}{200}}) e^{-\frac{t}{40}} - (1- e^{-\frac{150}{300}}) e^{-\frac{t}{20}}\) reaches its PEAK at about \(t = 16\), so the \(TE\) should be \(TE = 32ms\).

Question 4:

If both tissues deliver the SAME signal, which means \(M_{0A} e^{-\frac{t}{T_{2A}}} = M_{0B}e^{-\frac{t}{T_{2B}}}\).

Put the data in, then we can get \(4.1e^{-\frac{t}{30}} - 3.7e^{-\frac{t}{50}} = 0\), solve it and get \(t \approx 7.7ms\).

So the echo time is: \(TE = 2 \times t \approx 15.4ms\).

Question 5:

  1. \(e^{-\frac{t}{T_2}}S_0 = e^{-\frac{30}{50}} \approx 0.55\), so the signal is \(0.55mV\).

  2. If the magetic field of inhomogeneity of \(\Delta B = 1\) ppm, the signal can be calculated by such equation:

\[ S(t) = S_0 e^{-\frac{t}{T_2}} e^{- \gamma \Delta B t} \]

Put the data in, we can \(e^{-\frac{30}{50}} e^{-1 \times 267 \times 0.000003} \approx 0.43\), so the signal amplitude is \(0.43\).

  1. We should delivered the 180° pulses at times 20ms and times 40ms if we wish to detect echoes at times 40ms and 80ms.

The signal will be \(e^{-\frac{40}{50}} \approx 0.45\) at times 40ms and \(e^{-\frac{80}{50}} \approx 0.20\) at times 80ms.

Question 6:

From the equation \(M = M_0 e^{-\frac{t}{T_2}}\), then we can solve \(e^{-\frac{t}{60}} \le 0.1\), the answer is \(t \approx 138.55ms\).

Therefore, we can get the echoes at times 10ms, 30ms, 50ms, 70ms, 90ms, 110ms, 130ms, so we can get \(7\) echoes.

Part 3: Fourier Transform and K-Space

Questions

Part 3 Question

Answers

The amplitude of the \(\textit{FFT}\) result is \(15\). We need to times \(\frac{2}{N}\) to get the correct answer.

The reason are as follows:

  1. Both the positive and negative frequency contribute the answer, but we only use the positive, so have to multiply \(2\).

  2. Each operation we have to sum once, so we need the result to multiply \(\frac{1}{N}\) to get the final answer.