Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

PURPOSE: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy. MATERIAL AND METHODS: The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size. RESULTS: The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4mm and statistically low average gadolinium-enhancement contrast. CONCLUSION: It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4mm.

Verbal fluency output in children aged 7-16 as a function of the production criterion: qualitative analysis of clustering, switching processes, and semantic network exploitation.

Developmental changes in children's verbal fluency were explored in this study. One hundred and forty children aged from 7 to 16 completed four verbal fluency tasks, each with a different the production criterion (letter, sound, semantic, and free). The age differences were analyzed both in terms of number of words produced, and clustering, switching, and semantic network exploration. Analysis of the number of words produced showed a larger difference between the 7-8- and the 9-10-year-olds in semantic than in letter fluency, but this difference gradually disappeared with increasing age for semantic fluency while remaining constant for letter fluency. In letter fluency production, age modified both the number of switches and clusters formed whereas in semantic fluency tasks, only cluster size changed with age. Concerning the semantic network exploration indicators derived from the supermarket fluency task, the number of categories sampled increased from 11 to 12 years, but efficient semantic exploitation occurred only after the age of 13-14 years. These results are discussed in terms of the development of strategic retrieval components and categorical knowledge.