The reconstruction problem in medical imaging is a challenging task due to the scarcity of data and a limited understanding of the underlying physical processes. This leads to multiple solutions that can fit the data equally well, making it difficult to determine the unique solution to the inverse problem. However, by utilizing advanced computer algorithms, the most probable solution can be arrived at by incorporating mathematical models, regularization techniques, and prior knowledge about the object being imaged. This results in producing high-quality images that are in line with the available data and regularized to prevent overfitting and amplifying noise.

There are fundamental limitations to the resolution of medical imaging, known as the Rayleigh criterion or diffraction limit. These limitations depend on the object’s size and the radiation wavelength used in the imaging process. Higher resolution requires the use of shorter wavelengths and larger detectors or sensors. These limits can be mathematically derived and utilized to optimize imaging systems for specific applications. By grasping these limitations and utilizing the appropriate imaging techniques and algorithms, medical imaging professionals can achieve the highest possible resolution and quality in their imaging results.
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