EFFECTS OF SCANNING TRAJECTORY AND PARAMETERS ON THE IMAGE QUALITIES OF MAGNETIC PARTICLE IMAGING
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Date
2024-05-17
Authors
Mukhatov, Azamat
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Today, scanning methods are getting more popular and becoming an important part of many devices like microelectromechanical systems (MEMS), light detection and ranging (LiDAR) [1], atomic force microscopy (AFM) [2], medical imaging techniques (MRI [3]–[6] and MPI [7]–[11]), and mapping and surveying mechanisms [12], frequency modulated gyroscopes [13]. However, even though scanning techniques have many uses, one of the most important is in medical imaging. These pictures are important because they can be used to see inside the body without needing surgery. They help doctors diagnose, keep track of, stop, and treat many different illnesses [14], [15]. These techniques are used to look at the patient's field of vision and take a picture to study later to understand how the patient is doing. Choosing the right scanning path is very important to get the correct results. By picking the best path, we can scan faster and make the pictures clearer to help diagnose better. This means that the way a scan is done is very important for helping patients [16].
It's important to note that all the mentioned methods are still being worked on by researchers to make them better. Even though the field is getting bigger, the main issue with current scanning methods is that it's hard to accurately estimate the size of the pixels for different scanning settings. For instance, we don't know how big each pixel will be in the scan, with a particular way of scanning a certain area and set of scanning settings. Remember that the size of the pixels you choose will affect how good the image looks after it's scanned. So, it's really important to understand how the scanner moves and works in the area it's focused on, including how dense the scanning is, how much time is spent scanning, the quality of the signal compared to the background noise, and any mistakes in each small area. It is important to think about the right size of the pixels and the space between the pattern and how it is spread out in the FOV. It is important to tell apart the ideas of image resolution and spatial resoution. The sharpness of an image depends on how many tiny dots are in the picture, and how big each dot is. For example, an image with lots of small dots instead of a few big ones will have a clear picture. So, the quality of the image is affected by the size of the pixels. The image resolution decides how much detail and sharpness you can see in the picture. On the other hand, spatial resolution means the smallest detail you can see in a picture, which determines how much detail a camera or sensor can show. Spatial resolution is how small of a thing you can see. It can be measured in millimeters, micrometers, or even nanometers. Usually, to see small details in a picture, the picture needs to have a higher resolution than the spatial resolution [17]. This means the pixels should be much smaller than the spatial resolution. This paper focuses on how the quality of images is affected by the way they are scanned, and the scanning settings used.Any system that scans a particular area has a scanning point that moves in a specific pattern [6], [18]. The quality of the scanned images can change depending on the path chosen, which can also affect how long it takes to scan them and how clear they are [8]. Therefore, it's important for system operators to be able to measure image resolution using pixel size and understand how it's related to scanning parameters [19]. This work aims to create a theory that can figure out the smallest image resolution or biggest pixel size by using all the points where paths cross in the entire view. For a range of paths that may be used in biomedical imaging, the image resolution and its effect on the quality of the reconstructed image are also assessed. These days, a variety of scanning trajectories are accessible, such as spiral, radial, unidirectional, bidirectional Cartesian (BC), triangular Lissajous (TL), sinusoidal Lissajous (SL), and radial Lissajous (RL), as well as different enhanced and unidirectional trajectories. BC, TL, SL, and RL—will be the focus of this thesis because of their high scan resolution, reasonably regular pattern generation, and capacity to provide high-quality reconstructed images with isotropic resolution [8], [20].
Also, the influence of scanning repetition on the quality of reconstructed images in Magnetic Particle Imaging (MPI) systems is thoroughly examined in this research. In order to reconstruct images of various phantoms, were investigated using MATLAB simulations.
Simulations were methodically carried out with different numbers of repetitions - 1, 2, 4, and 8 - to obtain a more detailed understanding. The trade-offs between trajectory accuracy, precision, and uniformness were well-explained by this investigation. Using performance indicators such as Normalized Root Mean Square Error (NRMSE), Normalized Total Square Error (NTSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), a thorough analysis was conducted in the post-reconstruction phase to compare scanning trajectories. Finding the trajectory that provided the most exact and accurate image reconstruction was the goal.
Description
Keywords
Magnetic Particle Imaging, Scanning Trajectories, Type of access: Restricted
Citation
Mukhatov, A. (2024). Effects of scanning trajectory and parameters on the image qualities of magnetic particle imaging. Nazarbayev University School of Engineering and Digital Sciences