Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI

Purpose: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted magnetic resonance imaging (DW-MRI) data and evaluates its performance. Methods: In May 2011, ten male volunteers (age range: 29 to 53 years, mean: 37 years) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T magnetic resonance scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by two readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass Correlation Coefficients (ICC) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using Coefficients of Variation (CV). The fitting error was calculated based on simulated data and the average fitting time of each method was recorded. Results: DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the two readers (ICCs between 50 and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods but the networks may need to be re-trained for different acquisition protocols or imaged anatomical regions. Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available at (1)

A variational approach to the registration of tensor-valued images

We present a variational framework for the registration of tensor valued images. It is based on an energy functional with four terms: a data term based on a diffusion tensor constancy constraint, a compatibility term encoding the physical model linking domain deformations and tensor reorientation, and smoothness terms for deformation and tensor reorientation. Although the tensor deformation model employed here is designed with regard to diffusion tensor MRI data, the separation of data and compatibility term allows to adapt the model easily to different tensor deformation models. We minimise the energy functional with respect to both transformation fields by a multiscale gradient descent. Experiments demonstrate the viability and potential of this approach in the registration of tensor-valued images

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI.

OBJECTIVES To differentiate prostate cancer lesions with high and with low Gleason score by diffusion-weighted-MRI (DW-MRI). METHODS This prospective study was approved by the responsible ethics committee. DW-MRI of 84 consenting prostate and/or bladder cancer patients scheduled for radical prostatectomy were acquired and used to compute apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM: the pure diffusion coefficient D t, the pseudo-diffusion fraction F p and the pseudo-diffusion coefficient D p), and high b value (as acquired and Hessian filtered) parameters within the index lesion. These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on whether whole-prostate histopathological analysis after prostatectomy determined a high (≥7) or low (6) Gleason score. RESULTS Mean ADC and D t differed significantly (p of independent two-sample t test < 0.01) between high- and low-grade lesions. The highest classification accuracy was achieved by the mean ADC (AUC 0.74) and D t (AUC 0.70). A logistic regression model based on mean ADC, mean F p and mean high b value image led to an AUC of 0.74 following leave-one-out cross-validation. CONCLUSIONS Classification by IVIM parameters was not superior to classification by ADC. DW-MRI parameters correlated with Gleason score but did not provide sufficient information to classify individual patients. KEY POINTS • Mean ADC and diffusion coefficient differ between high- and low-grade prostatic lesions. • Accuracy of trivariate logistic regression is not superior to using ADC alone. • DW-MRI is not a valid substitute for biopsies in clinical routine yet

A variational approach to the registration of tensor-valued images

We present a variational framework for the registration of tensor valued images. It is based on an energy functional with four terms: a data term based on a diffusion tensor constancy constraint, a compatibility term encoding the physical model linking domain deformations and tensor reorientation, and smoothness terms for deformation and tensor reorientation. Although the tensor deformation model employed here is designed with regard to diffusion tensor MRI data, the separation of data and compatibility term allows to adapt the model easily to different tensor deformation models. We minimise the energy functional with respect to both transformation fields by a multiscale gradient descent. Experiments demonstrate the viability and potential of this approach in the registration of tensor-valued images

Phase Change Material Evolution in Thermal Energy Storage Systems for the Building Sector, with a Focus on Ground-Coupled Heat Pumps

The building sector is responsible for a third of the global energy consumption and a quarter of greenhouse gas emissions. Phase change materials (PCMs) have shown high potential for latent thermal energy storage (LTES) through their integration in building materials, with the aim of enhancing the efficient use of energy. Although research on PCMs began decades ago, this technology is still far from being widespread. This work analyses the main contributions to the employment of PCMs in the building sector, to better understand the motivations behind the restricted employment of PCM-based LTES technologies. The main research and review studies are critically discussed, focusing on: strategies used to regulate indoor thermal conditions, the variation of mechanical properties in PCMs-based mortars and cements, and applications with ground-coupled heat pumps. The employment of materials obtained from wastes and natural sources was also taken in account as a possible key to developing composite materials with good performance and sustainability at the same time. As a result, the integration of PCMs in LTES is still in its early stages, but reveals high potential for employment in the building sector, thanks to the continuous design improvement and optimization driven by high-performance materials and a new way of coupling with tailored envelopes

On the Reliability of Diffusion Neuroimaging

Over the last years, diffusion imaging techniques like DTI, DSI or Q-Ball received increasin

Le comunità energetiche montane. I casi studio di Champdepraz, La Salle e Venaus

A fronte della trasformazione del quadro energetico nazionale causato dai cambiamenti climatici, vi è la necessità di trovare soluzioni alternative per quanto riguarda la produzione, il consumo, la gestione e lo scambio di energia. In questo lavoro vengono esaminati diversi scenari e soluzioni che propongono l'auto-produzione e l'auto-consumo di energia considerando le tecnologie con fonti energetiche rinnovabili (FER) disponibili localmente. In particolare, l’obiettivo è quello di studiare la fattibilità tecnica, economica e ambientale di diverse comunità energetiche montane in Italia, precisamente a La Salle e Champdepraz in Valle d’Aosta e a Venaus in Piemonte. Il progetto individua le potenzialità del territorio partendo da una pianificazione energetica ad ampia scala e successivamente valuta un'aggregazione di utenti con una certa domanda di energia adatti a formare una comunità energetica rinnovabile. Infine si calcolano indicatori e flussi di energia ed emissioni che consentono di valutare i diversi scenari sfruttando anche l’incentivo del Decreto MISE 16/09/2020

miR-382-5p Controls Hematopoietic Stem Cell Differentiation Through the Downregulation of MXD1

microRNAs are key regulators of gene expression that control stem cell fate by posttranscriptional downregulation of hundreds of target genes through seed pairing in their 3' untranslated region. In fact, miRNAs tightly regulate fundamental stem cell processes, like self-renewal, proliferation, and differentiation; therefore, miRNA deregulation may contribute to the development of solid tumors and hematological malignancies. miR-382-5p has been found to be upregulated in patients with myeloid neoplasms, but its role in normal hematopoiesis is still unknown. In this study, we demonstrated that miR-382-5p overexpression in CD34(+) hematopoietic stem/progenitor cells (HSPCs) leads to a significant decrease of megakaryocyte precursors coupled to increase of granulocyte ones. Furthermore, by means of a computational analysis using different prediction algorithms, we identified several putative mRNA targets of miR-382-5p that are downregulated upon miRNA overexpression (ie, FLI1, GATA2, MAF, MXD1, RUNX1, and SGK1). Among these, we validated MXD1 as real target of miR-382-5p by luciferase reporter assay. Finally, we showed that MXD1 knockdown mimics the effects of miR-382-5p overexpression on granulocyte and megakaryocyte differentiation of CD34(+) cells. Overall, our results demonstrated that miR-382-5p expression favors the expansion of granulocyte lineage and impairs megakaryocyte commitment through MXD1 downregulation. Therefore, our data showed for the first time that the miR-382-5p/MXD1 axis plays a critical role in myelopoiesis by affecting the lineage choice of CD34(+) HSPCs