expand the following paragraph in an extreme scientific and professional manner. Add some examples on CT and MRI and their contribution to larger part of science.
Imaging modalities like CT, MRI etc are great to generate high quality reconstructions of the interior of static objects. Reason is that the corresponding inverse problems are only moderately ill-posed.
The field of medical imaging has revolutionized the diagnosis and treatment of various diseases and medical conditions. Among the numerous imaging modalities, computed tomography (CT) and magnetic resonance imaging (MRI) have emerged as invaluable tools for generating high-quality reconstructions of the interior of static objects, due to the fact that the corresponding inverse problems are only moderately ill-posed.

CT imaging, which involves the use of X-rays and computer algorithms to create cross-sectional images of the body, has made significant contributions to the field of medical science. CT scans are routinely used to detect various pathologies such as tumors, fractures, and internal bleeding. For example, CT scans have been used to identify brain tumors, abdominal masses, and pulmonary embolisms, among other conditions. Furthermore, CT scans have been instrumental in guiding various medical procedures, such as biopsies and radiation therapy.

MRI, on the other hand, utilizes strong magnetic fields and radio waves to create detailed images of internal organs and tissues. MRI has several advantages over other imaging modalities, including the absence of ionizing radiation, high spatial resolution, and the ability to visualize soft tissues. MRI is particularly useful in detecting neurological and musculoskeletal disorders, such as multiple sclerosis, stroke, and joint injuries. Additionally, MRI has been used to study brain function and connectivity, allowing researchers to gain insights into various neurological disorders, including Alzheimer's disease and schizophrenia.

Overall, CT and MRI imaging modalities have played an essential role in advancing our understanding of various diseases and medical conditions. They have become indispensable tools in clinical practice, enabling healthcare professionals to make accurate diagnoses and guiding therapeutic interventions. The continued development and refinement of these imaging modalities are essential for improving patient outcomes and advancing medical research.
Now explain in more detail why the reconstruction problem is ill-posed and how this is resolved in the computer. In your answer, please mention also fundamental resolution limits and how they can be derived.
The reconstruction problem is ill-posed because it does not satisfy one or more of the three conditions for a well-posed problem: existence, uniqueness, and stability of the solution. For example, in CT imaging, if the number of projections is insufficient or the angular range is incomplete, then there may be no solution or multiple solutions that fit the measurements. Moreover, even if there is a unique solution, it may be highly sensitive to noise or errors in the measurements, leading to artifacts or distortions in the reconstructed image.

To resolve this problem in the computer, regularization and approximation methods are used. These methods introduce some prior information or constraints on the solution space to reduce its dimensionality and complexity1. For example, one can assume that the solution has some smoothness properties or sparsity patterns that can be enforced by adding a regularization term to the cost function2. Alternatively, one can approximate the inverse operator by using a truncated singular value decomposition (SVD) or a generalized inverse matrix that reduces its condition number1.

The resolution limits of image reconstruction depend on several factors, such as measurement noise level, sampling rate, detector size, and geometry. One way to derive these limits is to use Fourier analysis and examine how different spatial frequencies are affected by these factors. For example, in CT imaging, one can use the Radon transform and Fourier slice theorem to relate projections and images in the frequency domain. Then one can estimate how much information is lost due to noise filtering or limited angular range by looking at how different frequency components are attenuated or missing. Similarly, in MRI imaging, one can use k-space analysis and Nyquist theorem to relate Fourier coefficients and images in frequency domain. Then one can estimate how much information is lost due to undersampling or aliasing by looking at how different frequency components are distorted or overlapped.