627 research outputs found
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
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Objective: To compare different deep learning architectures for predicting
the risk of readmission within 30 days of discharge from the intensive care
unit (ICU). The interpretability of attention-based models is leveraged to
describe patients-at-risk. Methods: Several deep learning architectures making
use of attention mechanisms, recurrent layers, neural ordinary differential
equations (ODEs), and medical concept embeddings with time-aware attention were
trained using publicly available electronic medical record data (MIMIC-III)
associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was
used to compute the posterior over weights of an attention-based model. Odds
ratios associated with an increased risk of readmission were computed for
static variables. Diagnoses, procedures, medications, and vital signs were
ranked according to the associated risk of readmission. Results: A recurrent
neural network, with time dynamics of code embeddings computed by neural ODEs,
achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score:
0.372). Predictive accuracy was comparable across neural network architectures.
Groups of patients at risk included those suffering from infectious
complications, with chronic or progressive conditions, and for whom standard
medical care was not suitable. Conclusions: Attention-based networks may be
preferable to recurrent networks if an interpretable model is required, at only
marginal cost in predictive accuracy
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 comunitĂ 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
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