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and. Digital Image Processing. D igital S ubtraction A ngiography. วัตถุประสงค์. อธิบายขบวนการประมวลผลภาพดิจิตอลได้ อธิบายวิธีการปรับคอนทราสของภาพดิจิตอลได้ อธิบายการทำงานและควบคุม window ของภาพรังสีดิจิตอลได้ อธิบายวิธีการทำ Subtraction ภาพด้วยวิธีต่างๆ ได้. 1. 2. LUT Curve.

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Digital image processing

and

Digital Image Processing

Digital

Subtraction Angiography


วัตถุประสงค์

  • อธิบายขบวนการประมวลผลภาพดิจิตอลได้

  • อธิบายวิธีการปรับคอนทราสของภาพดิจิตอลได้

  • อธิบายการทำงานและควบคุม window ของภาพรังสีดิจิตอลได้

  • อธิบายวิธีการทำ Subtraction ภาพด้วยวิธีต่างๆ ได้


1

2



Curve

Selection of



D igital s ubtraction a ngiography

Digital Subtraction Angiography

DSA





Mask image

Live image

Mask-Live

(original)

(original+ contrast media)


Mask image

Live image

Live-Mask


Image processing with java
Image processing with Java

ให้นักศึกษาใช้ โปรแกรมนี้ในการทำ Digital Subtraction

ftp://rsbweb.nih.gov/pub/image-j/win32/


Subtraction methods
Subtraction methods

1. Depth

2. Energy

3. Time


Temporal subtraction time dependent
Temporal subtraction(Time-dependent)


Temporal subtraction
Temporal subtraction

1. Pre-contrast images (mask images)

2. Post-contrast images (live images)

3. Subtraction of mask from live images


2 energy subtraction
2. Energy subtraction

Energy dependence of x-ray attenuation of difference tissue



Dual energy subtraction1
Dual energy subtraction

Compton/Photoelectric decomposition


Advantage disadvantage

1. Provide selective cancellation

2. Fast , in millisecond, minimized motion interference

1. More complex

2. More sensitive to scatter radiation

3.Impossible to remove soft-tissue and bone simultaneously

Advantage/ Disadvantage


Dual energy subtraction images
Dual energy subtraction images

Soft-tissue removed

Bone removed


3 hybrid subtraction
3. Hybrid subtraction

Temporal subtraction + Energy subtraction


Image processing
Image processing

1. Spatial filtering

2. Pixel shifting operation

3. Temporal filtering

4. Intensity transformations

5. Window/Level techniques

6. Parametric imaging


1 spatial filtering

Spatial filtering is a method of selectively enhancing or diminishing specific spatial frequency components in an image

1. Spatial filtering

Diagram of two-dimensional digital spatial filtering


Digital filtering convolution

Methods diminishing specific spatial frequency components in an image

Low-pass filtering

High-pass filtering

Median filtering

Digital filtering(Convolution)

Each pixel in the processed images is derived from a set of pixels in the original image as determined by the mask.


Low pass digital spatial filtering smoothing
Low-pass digital spatial filtering diminishing specific spatial frequency components in an image(Smoothing)


High pass digital spatial filtering edge enhancement
High-pass digital spatial filtering diminishing specific spatial frequency components in an image(Edge enhancement)


Filtered images
Filtered images diminishing specific spatial frequency components in an image

Original

High-pass

(Edge enhancement)

Low-pass

Smoothing


Medi an filtering
Medi diminishing specific spatial frequency components in an imagean filtering

Mask = Median value of the appropriate 9 pixels in the original image


Median filtering images
Median filtering images diminishing specific spatial frequency components in an image

Digital chest radiograph with unwanted dot artifacts

After application of 3x1 medial filter to remove dots


2 pixel shifting operation
2. Pixel shifting operation diminishing specific spatial frequency components in an image

  • Rotation

  • Translation

  • Magnification

  • Minification


Pixel registration to reduce motion artifacts diminishing specific spatial frequency components in an image


3 temporal filtering
3. Temporal filtering diminishing specific spatial frequency components in an image

1. Time interval difference(TID)

2. Integration

3. Blurred mask temporal subtraction

4. Recursive filtering (real time methods)

Generalized temporal filtering diagram


3 1 time interval difference subtraction
3.1. Time -interval difference subtraction diminishing specific spatial frequency components in an image


3 2 integration
3.2. Integration diminishing specific spatial frequency components in an image

Pre-contrast and post-contrast images are summated(integrated) to reduce noise


Image integration

Single pre-contrast image diminishing specific spatial frequency components in an image

Single post-contrast images

8 pre-contrast image

8 post-contrast image

Image integration


3 3 blurred mask temporal subtraction
3.3. Blurred mask diminishing specific spatial frequency components in an imagetemporal subtraction

For cardiac study : increase s/n for mask image and the edge of cardiac will blurred


3 4 recursive filtering real time methods
3.4. Recursive filtering diminishing specific spatial frequency components in an image(real time methods)

1. Reduce radiation dose

2. Reduce motion artifacts


4 intensity transformation
4. Intensity transformation diminishing specific spatial frequency components in an image

Use of image processing to correct the non linearity of film


Gamma correction curve
Gamma correction curve diminishing specific spatial frequency components in an image

Gamma correction curves can be use to enhance or reduce contrast


Contrast enhancement diminishing specific spatial frequency components in an image

Contrast reduction

Original image


Histogram equalization
Histogram equalization diminishing specific spatial frequency components in an image

Original arterial DSA image of the kidney

After histogram equalization


Histogram equalization1
Histogram equalization diminishing specific spatial frequency components in an image


5 windows level techniques

Gray scale diminishing specific spatial frequency components in an image

display

1024

White

Window

center C

Window

width W

Black

0

Windowing

5. Windows / Level Techniques


Double windows techniques

Gray scale diminishing specific spatial frequency components in an image

display

1024

White

Window 1

Black

Window 2

Windowing

0

Double windows techniques


6 parametric imaging
6. Parametric imaging diminishing specific spatial frequency components in an image

  • The algorithms for image processing that provide a final displayed image in which the value of each pixel is related to the attenuation or attenuation change at the particular point in the patient


Parametric functional imaging
Parametric diminishing specific spatial frequency components in an image(functional) imaging

Acute Tubular Necrosis

Example of parametric imaging

1. Time to peak enhancement

2. Mean transit time

3. Maximum pixel attenuation

4. Integrated attenuation change

5. Local volume distribution


6 diminishing specific spatial frequency components in an image.Quantitative imaging : Temporal processing

An idealized contrast enhancement curve or Indicator dilution curve


Quantitative imaging

Example of calculation diminishing specific spatial frequency components in an image

1. Peak or Maximum enhancement

2. Time to maximum enhancement

3.Time to half maximum enhancement

4. Integrated enhancement(area under the curve)

5. Mean transit time

6. etc

Application

A. Cardiac output

B. Regional blood flow

C. Cardiac ventricular ejection fraction

D. Quantitation of left to right shut

E. etc

Quantitative imaging


Gamma variate parameters of typical diminishing specific spatial frequency components in an imagetime-concentration curve



Dsa quantitation of vessel stenosis
DSA quantitation of vessel stenosis standard thermodilution methods

Identifies the region of stenosis, and normal portion, then calculate the degree of narrowing

DSA of right coronary artery stenosis


Boundary detection
Boundary detection standard thermodilution methods

1.After location of aortic valve plane and apex, the computer constructs a ray passing through the center(x) of the LV

2. A series of rays emanating from the center are drawn by the computer

3. The density of pixel values is measured , the edge is determined at 50% of the maximum values


End-diastolic standard thermodilution methodsED contours are shown for different thresholds values(50% and 75%)

End-systolicES contours are shown for different thresholds values(50% and 75%)

The ejection fraction is computed using the 50% thresholds silhouettes


แบบฝึกหัด standard thermodilution methods

  • ให้ใช้ โปรแกรม ImageJ ซึ่งสามารถทำ Image processing ได้หลากหลายวิธี นำมาใช้เป็นเครื่องมือในการทำ subtraction โดยให้นักศึกษาเลือกภาพต้นฉบับของตนเอง(*.jpg)ขนาดไม่เกิน 500k ส่งให้อาจารย์ที่ web ของรายวิชา 437401 Medical imaging https://bme.kmitnb.ac.th/mmi1_elearning/ จากนั้นอาจารย์จะสร้างวัตถุแปลกปลอมในภาพนั้นและส่งกลับให้นักศึกษาเพื่อให้ นักศึกษาใช้โปรแกรม ในการสร้างภาพสิ่งแปลกปลอมนั้นและส่งกลับที่ web เดิม

https://bme.kmitnb.ac.th/mmi1_elearning/


บรรณานุกรม standard thermodilution methods

http://rsb.info.nih.gov/ij/

  • Image processing program with Java

  • Digital Subtraction Angiography. USA,


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