### 1.1 Image Processing and Analysis for Preclinical and Clinical Applications
#### 1.1.8 Principal and Clinical Applications
For this purpose, 148 subjects with paroxysmal AF and 24 controls were recruited and underwent a cardiac MRI, inclusive of 4D-flow. LA was isolated within the 4D-flow dataset to constrain stasis maps. The results showed that the mean LA stasis in the control was lower than that in the pre-ablation cohort and that the mean LA stasis was reduced in the post-ablation cohort compared with in the pre-ablation cohort. The study demonstrated that 4D flow-derived LA stasis mapping was clinically relevant and revealed stasis changes in the LA body pre- and post-pulmonary vein ablation. Finally, the last two published studies concern an image registration technique based on local feature of retinal vessels [22], and the hardware optimizations of the X-ray pre-processing using the field programmable gate array (FPGA) [23]. In the first of these two studies [22], an innovative method, namely CURVE, is presented to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance was tested on different datasets and compared with six state-of-the-art feature extraction methods. The results showed that the feature extraction accuracy of CURVE significantly outperformed the existing feature extraction methods. Then, CURVE was paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. CURVE-SIFT successfully registered 44% of the image pairs while existing feature-based techniques registered less than 27% of the image pairs. The last study [23] proposed the optimization of the X-ray pre-processing in CT imaging to compute total attenuation projections by avoiding the intermediate step of converting detector data to intensity images. Furthermore, a configurable hardware architecture for data acquisition systems on FPGAs was proposed to fulfill the real-time requirements and with the aim of achieving \"on-the-fly\" pre-processing of 2D projections. Finally, this architecture was configured for exploring and analyzing different arithmetic representations,such as floating-point and fixed-point data formats. In this way, the best representation and data format that minimized execution time and hardware costs was found without affecting image quality. By comparing the proposed solution with the state-of-the-art pre-processing algorithm, the latency decreased by 4.125\(\times\) and the resource utilization decreased by \(\sim\)6.5\(\times\). By using fixed-point representation in the different data precisions, the latency and the resource utilization were further decreased. In conclusion, this Special Issue covers recent trends in biomedical imaging applications, such as quantification, detection, radiomics, registration, and optimization, constituting a good sample of the current state-of-the-art results in this field. Conceptualization, A.S., F.V. and A.C.; methodology, A.S.; resources, F.V. and A.C.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.S.; supervision, A.S.; project administration, A.S., F.V. and A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.