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In the last years there has been a growing interest in the applications of diffusion tractography for target identification in neurofunctional disorders for an increasingly tailored approach. The growing diffusion of well-established neurosurgical procedures as deep brain stimulation (DBS) and trans-cranial Magnetic Resonance-guided Focused Ultrasound (tcMRgFUS) favored this trend. Tractography can indeed provide more accurate, patient-specific, information about the targeted region if compared to stereotactic atlases. On the other hand, this tractography-based approachs is not very physician-friendly, and its heavily time consuming since needs several hours for Magnetic Resonance Imaging (MRI) data processing. In this study we propose a novel open-source deep learning framework called DeLTA-BIT (acronym of Deep-learning Local TrActography for BraIn Targeting) for reconstructing thalamic probabilistic tractography maps enabling a personalized (patient-specific) target identification for functional neurological disorders. The proposed framework exploits two convolutional neural networks (CNNs) to segment the thalamus and reconstruct the connectivity map between each voxel inside the thalamus and any user-specified target cortical or subcortical masks. These CNNs were trained, validated, and tested on the datasets from the Human Connectome Project (HCP) database. Our results are comparable with the state-of-the-art data-driven segmentation and probabilistic tractography and demonstrate the high performance of proposed networks for personalized thalamic target identification with a Dice coefficient of 0.93 +/- 0.01 for thalamus segmentation and a Dice Similarity Coefficient for the projection to the precentral gyrus from the thalamus of 0.80 +/- 0.06. The code of these models is freely available on GitHub.

In this work, we apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. In the presented approach, time-dependent information on the microscopic state of each particle/pixel includes its space position and a feature representing a static characteristic of the system, i.e. the gray level of each pixel. From the introduced microscopic model we derive a kinetic formulation of the model. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach that can be obtained in the quasi-invariant scaling. We exploit the computational efficiency of direct simulation Monte Carlo methods for the obtained Boltzmann-type description of the problem for parameter identification tasks. Based on a suitable loss function measuring the distance between the ground truth segmentation mask and the evaluated mask, we minimize the introduced segmentation metric for a relevant set of 2D gray-scale images. Applications to biomedical segmentation concentrate on different imaging research contexts., Comment: 29 pages, 13 figures

The automatic assignment of a severity score to the CT scans of patients affected by COVID-19 pneumonia could reduce the workload in radiology departments. This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net_1) is devoted to the identification of the lung parenchyma, the second one (U-net_2) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice index. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated. Both Dice and accuracy showed a dependency on the quality of annotations of the available data samples. On an independent and publicly available benchmark dataset, the Dice values measured between the masks predicted by LungQuant system and the reference ones were 0.95$\pm$0.01 and 0.66$\pm$0.13 for the segmentation of lungs and COVID-19 lesions, respectively. The accuracy of 90% in the identification of the CT-SS on this benchmark dataset was achieved. We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of the Dice index, the U-net segmentation quality strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent validation sets, demonstrating the satisfactory generalization ability of the LungQuant.

In this study we investigated the repeatability and reproducibility of radiomic features extracted from MRI images and provide a workflow to identify robust features. 2D and 3D T$_2$-weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of the radiomic features were assessed respectively by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC), considering repeated acquisitions with or without phantom repositioning, and with different scanner/acquisition type, and acquisition parameters. The features showing ICC/CCC > 0.9 were selected, and their dependence on shape information (Spearman's $\rho$> 0.8) was analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities. From 944 2D features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. Much lower range (11.2% to 85.4%) was obtained after phantom repositioning. 3D extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. 82.4% to 94.9% of features showed excellent agreement when extracted from images acquired with TEs 5 ms apart (values decreased when increasing TE intervals) and 90.7% of the features exhibited excellent reproducibility for changes in TR. 2.0% of non-shape features were identified as providing only shape information. This study demonstrates that radiomic features are affected by specific MRI protocols. The use of our radiomic pelvic phantom allowed to identify unreliable features for radiomic analysis on T$_2$-weighted images. This paper proposes a general workflow to identify repeatable, reproducible, and informative radiomic features, fundamental to ensure robustness of clinical studies., Comment: 32 pages, 8 figures (7 + 1 supplemental), 8 tables (5 + 3 supplemental); Submitted to Magnetic Resonance in Medicine

Muon Spin Rotation is employed to investigate the spin dynamics of ferritin proteins isolated from the brain of an Alzheimer's disease (AD) patient and of a healthy control, using a sample of horse-spleen ferritin as a reference. A model based on the N\'eel theory of superparamagnetism is developed in order to interpret the spin relaxation rate of the muons stopped by the core of the protein. Using this model, our preliminary observations show that ferritins from the healthy control are filled with a mineral compatible with ferrihydrite, while ferritins from the AD patient contain a crystalline phase with a larger magnetocrystalline anisotropy, possibly compatible with magnetite or maghemite., Comment: 16 pages, 9 figures

Three isostructural cyano-bridged heptanuclear complexes, [{CuII(saldmen)(H2O)}6{MIII(CN)6}](ClO4)3$\cdotp$8H2O (M = FeIII 2; CoIII, 3; CrIII 4), have been obtained by reacting the binuclear copper(II) complex, [Cu2(saldmen)2(mu-H2O)(H2O)2](ClO4)2$\cdotp$2H2O 1, with K3[Co(CN)6], K4[Fe(CN)6], and, respectively, K3[Cr(CN)6] (Hsaldmen is the Schiff base resulted from the condensation of salicylaldehyde with N,N-dimethylethylenediamine). A unique octameric water cluster, with bicyclo[2,2,2]octane-like structure, is sandwiched between the heptanuclear cations in 2, 3 and 4. The cryomagnetic investigations of compounds 2 and 4 reveal ferromagnetic couplings of the central FeIII or CrIII ions with the CuII ions (JCuFe = +0.87 cm-1, JCuCr = +30.4 cm-1). The intramolecular Cu-Cu exchange interaction in 3, across the diamagnetic cobalt(III) ion, is -0.3 cm-1. The solid-state1H-NMR spectra of compounds 2 and 3 have been investigated.

The first X-ray magnetic circular dichroism (XMCD) spectra on the title compounds are presented. In Mn12 the Mn(III) and Mn(IV) contributions are clearly apparent and evidence the opposite spin arrangement of the two ions. For Fe8 the typical two peak structure of Fe(III) compounds is found. Quantitative analysis yielded for both compounds a negligible . This is the first direct experimental evidence of the quenching of the angular momentum in these systems. For Fe8 we found =1.14 in mB units per Fe atom, in reasonable agreement with the S=10 ground state assumed for in this system., Comment: 7 pages 7 figures