Image assurance/quality autoregulation

In summary, the conversation focuses on the topic of imaging quality assurance and the search for relevant papers and methods in the field. The conversation mentions the importance of both qualitative and quantitative measures in determining the quality of an image, such as SNR, CNR, and dose to patient size. The person is looking for papers and resources in medical physics and the clinical world, and references some specific guidelines and reports. They also mention the possibility of a more general reference covering concepts like modulation transfer function and detective quantum efficiency.
  • #1
duplImaging
1
0
Hello, I just was looking for possible information in the field of imaging quality assurance. I was wondering if their were any predominant scripts or papers on the subject of detecting the quality of an image. I know measuring how "good" an image is incredibly qualitative however, there are quantitative parts to it. Measurable things such as SNR, CNR or dose to patient size; I'm currently trying to find papers in the field that discuss the idea of quality assurance using methods such as these, and ideally other methods. Sorry if this is the wrong place to post, but doing my own research I haven't found anything in medical physics or the clinical world that touches on the subjects. I was recently discussing ideas to execute this sort of quality determination and I'm very curious about it now. Thank you.
 
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  • #2
A lot of the imaging QA standards are going to be modality- and even device-specific. So for example, some guidelines that I often refer to are here:
You can probably look to the AAPM Task group reports if you need something more specific.

Or are you looking for a reference that covers concepts more generally - like a modulation transfer function, detective quantum efficiency, contrast-detail curves, etc?
My goto reference for such things is: https://www.amazon.com/dp/0781780578/?tag=pfamazon01-20
 
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Related to Image assurance/quality autoregulation

1. What is image assurance/quality autoregulation?

Image assurance/quality autoregulation refers to the process of automatically adjusting and maintaining the quality of images in order to ensure they meet certain standards or requirements. This is typically done through the use of algorithms and software that analyze and adjust various aspects of the image, such as sharpness, contrast, and color accuracy.

2. Why is image assurance/quality autoregulation important?

Image assurance/quality autoregulation is important because it helps to ensure that the images being produced are of the highest possible quality. This is especially important in scientific research and other fields where accurate and reliable images are crucial for analysis and interpretation. Autoregulation helps to eliminate human error and bias in image processing, resulting in more consistent and reliable data.

3. How does image assurance/quality autoregulation work?

Image assurance/quality autoregulation works by using advanced algorithms to analyze various aspects of an image and make adjustments as needed. These algorithms are often based on established standards and guidelines for image quality, and can be customized to meet specific requirements. The autoregulation process can also involve the use of calibration tools and reference images to ensure accuracy.

4. Can image assurance/quality autoregulation be used for all types of images?

Yes, image assurance/quality autoregulation can be used for all types of images, including photographs, digital images, and scientific images such as microscopy or satellite images. The principles of autoregulation remain the same, but the specific algorithms and settings may vary depending on the type of image being processed.

5. What are the benefits of using image assurance/quality autoregulation?

Using image assurance/quality autoregulation has several benefits, including increased accuracy and consistency in image processing, improved efficiency and time-saving, and reduced potential for human error. It also helps to ensure that images meet established standards and can be reliably used for analysis and interpretation, enhancing the overall quality of scientific research and other fields that rely on accurate imaging.

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