Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study.

Introduction: Multi-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics. Methods: Data from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases. Results: Fractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%. Discussion: In conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review.

INTRODUCTION: Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI). AIMS: Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow. METHODS: Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration. RESULTS: We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth. CONCLUSIONS: To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.

High-frequency longitudinal white matter diffusion- and myelin-based MRI database: Reliability and variability.

Assessing the consistency of quantitative MRI measurements is critical for inclusion in longitudinal studies and clinical trials. Intraclass coefficient correlation and coefficient of variation were used to evaluate the different consistency aspects of diffusion- and myelin-based MRI measures. Multi-shell diffusion and inhomogeneous magnetization transfer data sets were collected from 20 healthy adults at a high-frequency of five MRI sessions. The consistency was evaluated across whole bundles and the track-profile along the bundles. The impact of the fiber populations on the consistency was also evaluated using the number of fiber orientations map. For whole and profile bundles, moderate to high reliability of diffusion and myelin measures were observed. We report higher reliability of measures for multiple fiber populations than single. The overall portrait of the most consistent measurements and bundles drawn from a wide range of MRI techniques presented here will be particularly useful for identifying reliable biomarkers capable of detecting, monitoring and predicting white matter changes in clinical applications and has the potential to inform patient-specific treatment strategies.

Primary mucosal melanoma of the larynx: systematic review of the literature and qualitative synthesis.

PURPOSE: Primary mucosal melanoma of the larynx (PLM) is a rare entity among head and neck cancers. Due to its rarity, clear protocols of management are not available. A deeper knowledge of the clinical and biological behaviour of PLM is strongly needed. METHODS: According to PRISMA process, we searched through electronic databases case reports, case series and review articles providing relevant clinical data. The survival analysis was performed with Kaplan-Meier survival curves, using disease free survival (DFS) and overall survival (OS) as endpoints. RESULTS: 1074 articles were initially screened, of which 37 studies describing 44 PLM cases were selected and included in the analysis. Mean age was 59.7 years with a mean follow-up time of 25.4 months. The most common symptom at presentation was hoarseness (52%), while the most involved laryngeal subsite was supraglottic region (62%). Most patients presented with an advanced stage. Tumour (T) and node (N) status at presentation did not influence OS nor DFS, whereas distant metastases (M) status resulted significantly associated with the reduction of OS and DFS time (Mantel-Cox: p < 0.0001 and p = 0.001, respectively). The laryngeal subsite and the type of surgery performed did not significantly impact on OS and DFS. CONCLUSIONS: Treatment for PLM remains debated. Surgery with safe margins is recommended due to the high rates of local recurrence. Systemic therapy is advised for metastatic disease. However, the prognosis remains poor even after radical resection or targeted therapy.