Population Pharmacokinetics of Tracers: A New Tool for Medical Imaging?

Nursing Analysis Based on Medical Imaging Technology before and after Coronary Angiography in Cardiovascular Medicine

With the advancement of technology, medical imaging technology has been greatly improved. This article mainly studies the nursing before and after coronary angiography in cardiovascular medicine based on medical imaging technology. This paper proposes a multimodal medical image fusion algorithm based on multiscale decomposition and convolution sparse representation. The algorithm first decomposes the preregistered source medical image by NSST, takes the subimages of different scales as training images, and optimizes the subdictionaries of different scales; then convolution and sparse the subimages on each scale encoding to obtain the sparse coefficients of different subimages; secondly, the combination of improved L1 norm and improved spatial frequency (novel sum-modified SF (NMSF)) is used for high-frequency subimage coefficients, and the fusion of low-frequency subimages improved the rule of combining the L1 norm and the regional energy; finally, the final fused image is obtained by inverse NSST of the fused low-frequency subband and high-frequency subband. Experimental analysis found that the bifurcation angle has nothing to do with the damage of the branch vessels after the main branch stent is placed. The bifurcation angle greater than 50° is an independent predictor of MACE after stent extrusion for bifurcation lesions. Experimental results show that the proposed method has good performance in contrast enhancement, detail extraction, and information retention, and it improves the quality of the fusion image.

Research perspectives on animal health in the era of artificial intelligence

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009-2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

Population Pharmacokinetic Approach Applied to Positron Emission Tomography: Computed Tomography for Tumor Tissue Identification in Patients with Glioma

BACKGROUND AND AIMS

18F-fluoro-ethyl-tyrosine (FET) is a radiopharmaceutical used in positron emission tomography (PET)-computed tomography in patients with glioma. We propose an original approach combining a radiotracer-pharmacokinetic exploration performed at the voxel level (three-dimensional pixel) and voxel classification to identify tumor tissue. Our methodology was validated using the standard FET-PET approach and magnetic resonance imaging (MRI) data acquired according to the current clinical practices.

METHODS

FET-PET and MRI data were retrospectively analyzed in ten patients presenting with progressive high-grade glioma. For FET-PET exploration, radioactivity acquisition started 15 min after radiotracer injection, and was measured each 5 min during 40 min. The tissue segmentation relies on population pharmacokinetic modeling with dependent individuals (voxels). This model can be approximated by a linear mixed-effects model. The tumor volumes estimated by our approach were compared with those determined with the current clinical techniques, FET-PET standard approach (i.e., a cumulated value of FET signal is computed during a time interval) and MRI sequences (T1 and T2/fluid-attenuated inversion recovery [FLAIR]), used as references. The T1 sequence is useful to identify highly vascular tumor and necrotic tissues, while the T2/FLAIR sequence is useful to isolate infiltration and edema tissue located around the tumor.

RESULTS

With our kinetic approach, the volumes of tumor tissue were larger than the tissues identified by the standard FET-PET and MRI T1, while they were smaller than those determined with MRI T2/FLAIR.

CONCLUSION

Our results revealed the presence of suspected tumor voxels not identified by the standard PET approach.