Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too

DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio (SNR). Aim. The aim is to select a DTI sequence for brain clinical studies, optimizing SNR and resolution. Methods and Results. We applied 6 methods for SNR computation in 26 DTI sequences with different parameters using 4 healthy volunteers (HV). We choosed two DTI sequences for their high SNR, they differed by voxel size and b-value. Subsequently, the two selected sequences were acquired from 30 multiple sclerosis (MS) patients with different disability and lesion load and 18 age matched HV. We observed high concordance between mean diffusivity (MD) and fractional anysotropy (FA), nonetheless the DTI sequence with smaller voxel size displayed a better correlation with disease progression, despite a slightly lower SNR. The reliability of corpus callosum (CC) fiber tracking with the chosen DTI sequences was also tested. Conclusions. The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity

Whole-Head Functional Near-Infrared Spectroscopy as an Ecological Monitoring Tool for Assessing Cortical Activity in Parkinson’s Disease Patients at Different Stages

Functional near-infrared spectroscopy (fNIRS) is increasingly employed as an ecological neuroimaging technique in assessing age-related chronic neurological disorders, such as Parkinson’s disease (PD), mainly providing a cross-sectional characterization of clinical phenotypes in ecological settings. Current fNIRS studies in PD have investigated the effects of motor and non-motor impairment on cortical activity during gait and postural stability tasks, but no study has employed fNIRS as an ecological neuroimaging tool to assess PD at different stages. Therefore, in this work, we sought to investigate the cortical activity of PD patients during a motor grasping task and its relationship with both the staging of the pathology and its clinical variables. This study considered 39 PD patients (age 69.0 ± 7.64, 38 right-handed), subdivided into two groups at different stages by the Hoehn and Yahr (HY) scale: early PD (ePD; N = 13, HY = [1; 1.5]) and moderate PD (mPD; N = 26, HY = [2; 2.5; 3]). We employed a whole-head fNIRS system with 102 measurement channels to monitor brain activity. Group-level activation maps and region of interest (ROI) analysis were computed for ePD, mPD, and ePD vs. mPD contrasts. A ROI-based correlation analysis was also performed with respect to contrasted subject-level fNIRS data, focusing on age, a Cognitive Reserve Index questionnaire (CRIQ), disease duration, the Unified Parkinson’s Disease Rating Scale (UPDRS), and performances in the Stroop Color and Word (SCW) test. We observed group differences in age, disease duration, and the UPDRS, while no significant differences were found for CRIQ or SCW scores. Group-level activation maps revealed that the ePD group presented higher activation in motor and occipital areas than the mPD group, while the inverse trend was found in frontal areas. Significant correlations with CRIQ, disease duration, the UPDRS, and the SCW were mostly found in non-motor areas. The results are in line with current fNIRS and functional and anatomical MRI scientific literature suggesting that non-motor areas—primarily the prefrontal cortex area—provide a compensation mechanism for PD motor impairment. fNIRS may serve as a viable support for the longitudinal assessment of therapeutic and rehabilitation procedures, and define new prodromal, low-cost, and ecological biomarkers of disease progression

Beat-to-beat variability of microvascular peripheral resistances assessed with a non-invasive approach

The pressure-flow relationship at peripheral level is non-invasively studied in human subjects: the impedance function and the beat-to-beat variability series of microvascular peripheral resistance are estimated. The frequency content of this variability signal is compared to those of more classical variability series at rest and during mild supine physical exercise

Brain-vascular segmentation for SEEG planning via a 3D fully-convolutional neural network

Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence. Moreover, a two-stage training has been proposed as a countermeasure for the unbalanced nature of the dataset. The FCNN training was performed on 20 CE-CBCT volumes exploiting mini-batch gradient descent andthe Adam optimizer. Binary cross-entropy was used as loss function. Performance evaluation was conducted considering 5 datasets. A median value of Dice, Precision and Recall of 0.79, 0.8 and 0.69 were obtained with respect to manual annotations

Cerebral blood flow and cerebrovascular reactivity correlate with severity of motor symptoms in Parkinson’s disease

Background: Parkinson\u2019s disease (PD) is a progressive neurodegenerative disorder that is mainly characterized by movement dysfunction. Neurovascular unit (NVU) disruption has been proposed to be involved in the disease, but its role in PD neurodegenerative mechanisms is still unclear. The aim of this study was to investigate cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) within the regions belonging to the motor network, in patients with mild to moderate stages of PD. Methods: Twenty-eight PD patients (66.6 \ub1 8.6 years, 22 males, median [interquartile range, IQR] Hoehn & Yahr = 1.5 [1\u20131.9]) and 32 age- and sex-matched healthy controls (HCs) were scanned with arterial spin labeling (ASL) magnetic resonance imaging (MRI) for CBF assessment. ASL MRI was also acquired in hypercapnic conditions to induce vasodilation and subsequently allow for CVR measurement in a subgroup of 13 PD patients and 13 HCs. Median CBF and CVR were extracted from cortical and subcortical regions belonging to the motor network and compared between PD patients and HCs. In addition, the correlation between these parameters and the severity of PD motor symptoms [quantified with Unified Parkinson\u2019s Disease Rating Scale part III (UPDRS III)] was assessed. The false discovery rate (FDR) method was used to correct for multiple comparisons. Results: No significant differences in terms of CBF and CVR were found between PD patients and HCs. Positive significant correlations were observed between CBF and UPDRS III within the precentral gyrus, postcentral gyrus, supplementary motor area, striatum, pallidum, thalamus, red nucleus, and substantia nigra (pFDR < 0.05). Conversely, significant negative correlation between CVR and UPDRS III was found in the corpus striatum (pFDR < 0.05). Conclusion: CBF and CVR assessment provides information about NVU integrity in an indirect and noninvasive way. Our findings support the hypothesis of NVU involvement at the mild to moderate stages of PD, suggesting that CBF and CVR within the motor network might be used as either diagnostic or prognostic markers for PD

VALIDATION OF AN AUTOMATIC HARD TISSUE SEGMENTATION ALGORITHM FOR CONE BEAM CT DATA

An automatic algorithm for hard tissue segmentation in CBCT data is presented and validated on 30 subjects. Bone segmentation threshold was set after voxel clustering through a sub-set of slices and the elimination of outliers with teeth and metal artifacts. Comparison with manual thresholding by experts gave no significant difference

Numerical algorithm to recover contrast dynamics in 3D digital subtraction angiography data-sets: a preliminary clinical validation

Several neurosurgical procedures, such as ArteroVenous Malformations (AVMs) and StereoElectroEncephaloGraphy (SEEG) require accurate reconstruction of the cerebral vascular tree, as well as the classification of arteries and veins, to increase the safety of the intervention. We propose ART-3.5D, a novel approach to recover the dynamic information from standard Cone Beam Computed Tomography Angiography scans based on the post- processing of both the segmented angiogram and the raw data-set