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        <description>Medical Imaging course exam topics

This is a version for summer 2022. Besides the topics mentioned below, you should also know the topics explained in the laboratory sessions. I might also ask you to do simple calculations using mathematical formulas that we talked about.</description>
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        <description>Cvičení / Lab organization

This is the main wiki page for labs in Medical Imaging Systems (codes BAM33ZSL, BEAM33ZSL, A6M33ZSL).

Course tutors:

	*  Sumit Kaushik &lt;kaushsum@fel.cvut.cz&gt;
	*  Jiří Anýž &lt;anyzjiri@fel.cvut.cz&gt;
	*  Jan Šanda &lt;jan.sanda@lfmotol.cuni.cz&gt;

Plán cvičení (Timetable and topics)
No.   Date</description>
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        <dc:date>2022-02-10T13:25:53+0200</dc:date>
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        <title>courses:zsl:labs2019_01_matlab</title>
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        <description>Lab 01 : Introduction / MATLAB

Topics:

HW01 Assignment

Homework HW01 has two sub-tasks, follow the emphasized links to get to the detailed description.
[2 pts][3 pts]
Upload your report in PDF format to BRUTE to the assignment L03-Matlab:Statistics. Please name the PDF file as $y = -3 \cdot x + 1$$Y = A \cdot X$$A$$B = X \backslash  Y$$z = \frac{\sin(\sqrt(x^2 + y^2))}{\sqrt(x^2 + y^2))}$$x, y \in (-15, 15)$$z = \sin(5*x) + \cos(4*y)$$x, y \in (-3, 3)$</description>
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        <dc:date>2022-02-07T16:19:55+0200</dc:date>
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        <title>courses:zsl:labs2020_02_microscopy</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_02_microscopy?rev=1644247195&amp;do=diff</link>
        <description>Lab 02 : Microscopy

Topics:
ExercisesStain normalization


HW02 Assignment

Homework HW02 has two parts, detailed description follows:
[2 pts][3 pts]
(A) Excercises

Important equations
optical distance$d = 250 mm$magnification$Z = \frac{y'}{y} = -\frac{a'}{a}$lense maker's formula$\frac{1}{a} + \frac{1}{a'} = \frac{1}{f}$$a, a'$magnification of a microscope$$ Z = \frac{\Delta}{f_{ob}} \frac{d}{f_{oc}} $$$d = 1.22 \cdot \frac{\lambda}{2 \cdot NA}$$f=0.1~m, y=0.06~m, a=0.12~m$$Z = \frac{y'}{y} =…</description>
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        <dc:date>2022-04-06T10:48:30+0200</dc:date>
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        <title>courses:zsl:labs2020_03_ctimages</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_03_ctimages?rev=1649234910&amp;do=diff</link>
        <description>Lab 08 : CT Images

Topics:
VisualizationClassification
HW03 Homework
[3 pts][2 pts]
(A) CT Data Processing

Download the [ MITK Workbench] application. Load the testing image . Optionally you can try data lowdose_CT.nii.gz from &lt;https://cmp.felk.cvut.cz/~herinjan/dt45knp3/&gt;, if the data are too large for your PC to process, you can try the smaller $[W_l, W_u]$$W_l$$W_u$$[W_l, W_u]$$(W_u + W_l) / 2$$W_u - W_l$$I(x,y)$$\theta_l \leq I(x, y) \leq \theta_u$</description>
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        <dc:date>2022-05-03T13:43:18+0200</dc:date>
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        <title>courses:zsl:labs2020_04_fwdproj</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_04_fwdproj?rev=1651578198&amp;do=diff</link>
        <description>Lab 09 : CT Reconstruction, forward projection, backprojection

Topics -- Math of CT images

slides [ lab09_presentation.pdf]

HW09 Assignment

1. Forward projection

The homework has four consecutive parts. When completed, you will have your own implementation of the forward projection ($\theta$$x, y$$p$$q$$p$$q$$\theta$\begin{align}
p &amp;=&amp; x \cdot \cos \theta + y \cdot \sin \theta \\
q &amp;=&amp; -x \cdot \sin \theta + y \cdot \cos \theta  
\end{align}$[-p_m, p_m] \times [-q_m, q_m]$$(x, y) \in [-x_m,…</description>
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        <dc:date>2022-04-28T15:36:28+0200</dc:date>
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        <title>courses:zsl:labs2020_06_iterrecon</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_06_iterrecon?rev=1651152988&amp;do=diff</link>
        <description>Lab 10: Algebraic reconstruction technique



Radon transform

Radon transform is a transform that is used for example in computed tomography. Revise the equations describing the Radon transform in Radon transform equations. 

In the previous lab we used the Radon transform in a continuous space and we discritized the equation in order to apply it to digital images. Another form of the Radon transform for digital images is in Equation 1. This form is more suitable for the reconstruction of a sin…</description>
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        <dc:date>2022-05-13T10:05:07+0200</dc:date>
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        <title>courses:zsl:labs2020_07_ultrasound</title>
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        <description>Lab 11 : Ultrasound theory

Homework
[1 pt][4 pts]
US exercises

Used formulas:
$\lambda = \frac{c}{f}$$c = \frac{1}{\sqrt{\rho K}}$$K$$E$$Z_t = \rho c$$R = \frac{(Z_1 - Z_2)^2}{(Z_1 + Z_2)^2}$
 1. Signal propagation

1.1 The time delay between transmission and the arrival of the reflected wave of a signal using ultrasound travelling through a piece of fat tissue was $0.13 ms$$1450 m/s$$2 \cdot d$$0.13 ms$$$d = \frac {c_{fat} \cdot t_d}  {2} =  \frac{1450 m/s \cdot 0.00013 s} {2} = 0.09425 m$$$0…</description>
    </item>
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        <dc:date>2022-05-18T08:51:32+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2020_08_registrace</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_08_registrace?rev=1652856692&amp;do=diff</link>
        <description>Lab 08 : Image Registration



Homework</description>
    </item>
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        <dc:date>2022-02-07T16:19:55+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2020_09_mribloch</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_09_mribloch?rev=1644247195&amp;do=diff</link>
        <description>Lab 9 : MRI Basics

V dnešním cvičení se budeme zabývat základními principy magnetické rezonance - Magnetic Resonance Imaging (MRI). Projděte si slajdy/prezentaci z přednášek MRI 1 (8.4.) a MRI 2 (15.4.). Dále doporučuji velmi bohatý soubor informací k MRI, který obsahuje i velké množství ilustrací a animací, dostuná na stránkách $^\circ$$T_1, T_2$$M_{xy}$$M_z$$90^{\circ}$$T_1$$T_2$$T_2$$T_1$$$U = \rho \cdot (1 - e^{-\frac{TR}{T_1}}) \cdot e^{-\frac{TE}{T_2}}$$$f = 42.58\cdot 10^6 \cdot B(x)$$B_…</description>
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        <dc:date>2022-02-07T16:19:54+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2020_10_mrikspace</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_10_mrikspace?rev=1644247194&amp;do=diff</link>
        <description>V předchozím cvičení jsme se zabývali závislostí signálu jednoduché sekvence s opakovými RF pulsy na parameterech TR/TE. Průběh signálu jsme uvažovali pro jednu látku, bez prostorového umístění. Ostatně při této sekvenci bychom měřili $G_f$$B_0$$x$$B(x)$$x$$f = \gamma B(x)$$x$$G_\phi$$\omega_1$$\gamma \cdot (B_0 + B_1) $$\omega_0$$G_y$$y$$(k_x, k_y)$$k_x = -\gamma G_f t$$k_y = -\gamma G_\phi t$$x$$k_x$$y$$k_y$$\rho$$T_1$$T_2$$T_1$$T_2$$\rho$$TE, TR$$K$$K$$T_1$$T_2$$\rho$$TR$$TE$$\rho, T_1, T_2$$…</description>
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        <dc:date>2022-02-09T11:52:43+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2020_11_mriartifacts</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_11_mriartifacts?rev=1644403963&amp;do=diff</link>
        <description>Lab 11 : Diffusion weighted MRI

Today we look at Diffusion-weighted Imaging - one specific MR modality. With dMRI, we can use gradient direction to measure the movement of molecules in that direction. If there is no obstacle at the measuring point, then the signals at different gradients will not differ. This is not the case when we have obstacles in our way, such as axons of nerve connections. The diffusion takes place along the unrestricted direction and gets restricted in transverse directio…</description>
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        <dc:format>text/html</dc:format>
        <dc:date>2022-02-07T16:19:54+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2020_12_fmrieval</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_12_fmrieval?rev=1644247194&amp;do=diff</link>
        <description>Lab 12 - fMRI

V dnešním cvičení se podíváme na data z funkční magnetické rezonance (fMRI) a bude nás zajímat detekce regionů v mozku, které jsou aktivovány při poslouchání dvouslabičných slov. Aktivita mozku je měřena pomocí fMRI, konkrétněji měříme BOLD signál při resting-state fMRI sekvenci. Statisticky významné rozdíly mezi běžnou aktivitou ($t$$\bar{x}_a, \bar{x}_r$$\bar{x}$$S^2_a$$S^2_r$$\bar{x}$$\alpha=0.01$$53 \times 63 \times 52$$3~mm$$T$$Y$$Y(:,:,:,1:6)$$Y(:,:,:,7:12)$$\alpha$$$\mathca…</description>
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    <item rdf:about="https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_13_petct?rev=1644247194&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2022-02-07T16:19:54+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2020_13_petct</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_13_petct?rev=1644247194&amp;do=diff</link>
        <description>Lab 13 Pozitronová emisní tomografie (PET) - měření a rekonstrukce dát

Téma cvičení je měření a rekonstrukce dat pozitronové emisní tomografii (PET). Jádrem zobrazování PET je detekce dvojice fotonů, které vzniknou následkem anihilace pozitronu vzniklého při rozpadu radiofarmaka v pacientově těle. Fotony letí od sebe po přímce a v ideálním případě je jejich průlet $N$$T_i$$T_b$$T_e$$D$$r$$\sigma^2$$n$$\tau_r$$\Delta N$$\Delta N$$t=0$$T_i$$N_0 =n \cdot N_A$$N_A$$N_A=6.0221415\cdot 10^{23}$$T_i$$…</description>
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        <dc:format>text/html</dc:format>
        <dc:date>2022-05-30T09:08:03+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2020_14_prikaldy_qa</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2020_14_prikaldy_qa?rev=1653894483&amp;do=diff</link>
        <description>Lab 14 : Exercises, Questions &amp; Answers

We have already practiced some math problems, see  Lab 7: Ultrasound theory and there are some examples in the microscopy  Lab 2 : Microscopy (optics), which was not part of the sylabus this year, but you might still find the exercises there useful. We will continue with some more problems today.$E = hf = h \frac{c}{\lambda} \, ,$$\frac{1}{2} = \textrm{e}^{-\mu d}$$100~nm$$12~eV$$2~nm$$10~keV$$E$$10~W/cm^2$$10~cm$$2~cm$$\mu_w = 0.22~cm^{-1}$$\mu_t$$1/2 = …</description>
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        <dc:date>2022-02-10T14:43:16+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2022_01_matlab</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2022_01_matlab?rev=1644500596&amp;do=diff</link>
        <description>Lab 01 : Introduction / MATLAB

Topics:

HW01 Assignment

For the homework, HW01  follow the emphasized links to get to the detailed description.
[5 pts]
Upload your report in PDF format to BRUTE to the assignment L01-Matlab introduction. Please name the PDF file as $y = -3 \cdot x + 1$$XA = Y $$A$$2\times 1 $$X$$51\times2$$x \in [0, 5]$$Y$$51\times1$$y$$A = X \backslash Y$$z = \frac{\sin(\sqrt(x^2 + y^2))}{\sqrt(x^2 + y^2))}$$x, y \in (-15, 15)$$z = \sin(5*x) + \cos(4*y)$$x, y \in (-3, 3)$</description>
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        <dc:format>text/html</dc:format>
        <dc:date>2022-02-10T10:01:15+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2022_02_matlabcellseg</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2022_02_matlabcellseg?rev=1644483675&amp;do=diff</link>
        <description>Lab 02 : MATLAB: Segmentation

Topics:

HW02 Assignment

For the homework (HW02), follow the emphasized links to get to the detailed description.
[5 pts]
Upload your report in PDF format to BRUTE to the assignment L02-segmentation. Please name the PDF file as</description>
    </item>
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        <dc:date>2022-02-24T13:40:41+0200</dc:date>
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        <title>courses:zsl:labs2022_03_matlab_statistics</title>
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        <description>Lab 03 : Statistical significance tests with MATLAB

Topics:

HW03 Assignment

For the homework HW03 tasks, follow the emphasized links to get to the detailed description.
[5.0 pts][2.0 pts]
Upload your report in PDF format and code zipped in a folder to $x$$y$$\alpha$$\alpha=0.01$$n$$m$$s$$n=30$$m$$m=30$$m=30$$m=20$$H0$$\alpha$$\mu_{B}-\mu_{A}\leq15$$$ t=\frac{(x_B-x_A)-(\mu_B-\mu_A)}{\sqrt{(\frac{s_B^2}{n_B} + \frac{s_A^2}{n_A})}}$$$x$$s$$\mu$$n$</description>
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        <dc:date>2022-02-16T11:17:54+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:labs2022_04_microscopy</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2022_04_microscopy?rev=1645006674&amp;do=diff</link>
        <description>Lab 04 : Microscopy

Topics:
ExercisesStain normalization


HW04 Assignment

Homework has two parts, detailed description follows:
[2 pts][3 pts]
(A) Excercises

Important equations
optical distance$d = 250 mm$magnification$Z = \frac{y'}{y} = -\frac{a'}{a}$lense maker's formula$\frac{1}{a} + \frac{1}{a'} = \frac{1}{f}$$a, a'$magnification of a microscope$$ Z = \frac{\Delta}{f_{ob}} \frac{d}{f_{oc}} $$$d = 1.22 \cdot \frac{\lambda}{2 \cdot NA}$$f=0.1~m, y=0.06~m, a=0.12~m$$Z = \frac{y'}{y} = \fra…</description>
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        <dc:date>2022-03-21T09:00:17+0200</dc:date>
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        <title>courses:zsl:labs2022_07_dtmri</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/labs2022_07_dtmri?rev=1647849617&amp;do=diff</link>
        <description>Lab 07 : Diffusion weighted MRI

Today we look at Diffusion-weighted Imaging - one specific MR modality. With dMRI, we can use gradient direction to measure the movement of molecules in that direction. If there is no obstacle at the measuring point, then the signals at different gradients will not differ. This is not the case when we have obstacles in our way, such as axons of nerve connections. The diffusion takes place along the unrestricted direction and gets restricted in transverse directio…</description>
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        <dc:date>2022-05-18T12:12:51+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:lessons</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/lessons?rev=1652868771&amp;do=diff</link>
        <description>ZSL Zobrazovací systémy v lékařství / Medical Imaging Systems

Přednášky  / Lectures


 Wednesday (and exceptionally also Friday), 9:15-10:45, room KN:E-126, Charles square. 

Přednášející/Lecturer(s): Jan Kybic,
Jan Petr (JP),
Petr Páta (PP)

The schedule is preliminary and may change. Study materials will appear here before the lecture.</description>
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        <title>courses:zsl:start</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/start?rev=1644851930&amp;do=diff</link>
        <description>BAM33ZSL / A6M33ZSL - Zobrazovací systémy v lékařství / Medical Imaging Systems

Obsahem předmětu je koncepce, vlastnosti a struktura zobrazovacích systémů užívaných v současné době v lékařství. Jedná se 2D mikroskopické, rentgenové a ultrazvukové zobrazovací systémy včetně dopplerovského ultrazvuku. Dále se budeme zabývat tomografickými (3D) systémy: počítačovou tomografií (CT), magnetickou rezonancí (MRI) včetně funkční MR a nukleárními zobrazovacími metodami (PET,SPECT).…</description>
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        <dc:date>2022-05-18T15:31:36+0200</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>courses:zsl:zkouskazsl</title>
        <link>https://cw.fel.cvut.cz/b212/courses/zsl/zkouskazsl?rev=1652880696&amp;do=diff</link>
        <description>Zkouška z předmětu Zobrazovací systémy v lékařství / Medical Imaging Systems course exam

 Termíny zkoušek budou  oznámeny.    The exam dates are:
27.5.10.6.22.6.


Podrobnosti

	*  Zkoušku tvoří písemný test, který lze psát česky i anglicky.</description>
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