A specific and widely accepted protocol for quality controls in DWI is still lacking. The DWI quality assurance protocol proposed in this study can be applied in order to assess the reliability of DWI-derived indices before tackling single- as well as multicenter studies.

The rotational variance dependence of diffusion tensor imaging (DTI) derived parameters on the number of diffusion weighting directions (N) has been investigated by several Monte Carlo simulation studies. However, the dependence of fractional anisotropy (FA) and mean diffusivity (MD) maps on N, in terms of accuracy and contrast between different anatomical structures, has not been assessed in detail. This experimental study further investigated in vivo the effect of the number of diffusion weighting directions on DTI maps of FA and MD. Human brain FA and MD maps of six healthy subjects were acquired at 1.5T with varying N (6, 11, 19, 27, 55). Then, FA and MD mean values in high false(FAH,.2emMDHfalse) and low false(FAL,.2emMDLfalse) anisotropy segmented brain regions were measured. Moreover, the contrast‐to‐signal variance ratio false(CVRFA,.2emCVRMDfalse) between the main white matter and the surrounding regions was calculated. Analysis of variance showed that FAL,.2emFAH and CVRFA significantly false(p<0.05false) depend on N. In particular, FAL decreased (6%–11%) with N, whereas FAH (1.6%–2.5%) and CVRFA (4%–6.5%) increased with normalN..2emMDL,.2emMDH and CVRMD did not significantly false(p>0.05false) depend on N. Unlike MD values, FA values significantly vary with N. It is noteworthy that the observed variation is opposite in low and high anisotropic regions. In clinical studies, the effect of N may represent a confounding variable for anisotropy measurements and the employment of DTI acquisition schemes with high normalN.2emfalse(>.2em20false) allows an increased CVR and a better visualization of white matter structures in FA maps.PACS number: 87.61.Tg, 82.56.Lz

Spatial and spatio-temporal model checking techniques have a wide range of application domains, among which large scale distributed systems and signal and image analysis. We explore a new domain, namely (semi-)automatic contouring in Medical Imaging, introducing the tool VoxLogicA which merges the state-of-the-art library of computational imaging algorithms ITK with the unique combination of declarative specification and optimised execution provided by spatial logic model checking. The result is a rapid, logic based analysis development methodology. The analysis of an existing benchmark of medical images for segmentation of brain tumours shows that simple VoxLogicA analysis can reach state-of-the-art accuracy, competing with best-in-class algorithms, with the advantage of explainability and replicability. Furthermore, due to a two-orders-of-magnitude speedup compared to the existing generalpurpose spatio-temporal model checker topochecker, VoxLogicA enables interactive development of analysis of 3D medical images, which can greatly facilitate the work of professionals in this domain.

Seven patients with a diagnosis of Parkinson's disease (PD) and pathological gambling (PG) and 7 PD patients without PG were investigated by functional MRI and a block-design experiment with gambling-related visual cues alternating with neutral stimuli and rest periods. Compared with PD/non-PG, in PD/PG patients, several areas of increased cue-related blood oxygen level dependent (BOLD)-response were observed including bilateral anterior cingulate cortex, medial and superior frontal gyri, and precuneus, right inferior parietal lobule, and ventral striatum. The over activation of cingulate cortex and ventral striatum in PD/PG patients after the craving task is similar to that reported in addicted patients, whereas the activation of the parietal structures is probably related to the attentional network.

To perform a systematic review on the research on the application of artificial intelligence (AI) to imaging published in Italy and identify its fields of application, methods and results. Materials and Methods: A Pubmed search was conducted using terms Artificial Intelligence, Machine Learning, Deep learning, imaging, and Italy as affiliation, excluding reviews and papers outside time interval 2015-2020. In a second phase, participants of the working group AI4MP on Artificial Intelligence of the Italian Association of Physics in Medicine (AIFM) searched for papers on AI in imaging. Results: The Pubmed search produced 794 results. 168 studies were selected, of which 122 were from Pubmed search and 46 from the working group. The most used imaging modality was MRI (44%) followed by CT(12%) ad radiography/mammography (11%). The most common clinical indication were neurological diseases (29%) and diagnosis of cancer (25%). Classification was the most common task for AI (57%) followed by segmentation (16%). 65% of studies used machine learning and 35% used deep learning. We observed a rapid increase of research in Italy on artificial intelligence in the last 5 years, peaking at 155% from 2018 to 2019.