Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network

Bliesener, Yannick; Acharya, Jay; Nayak, Krishna S.

doi:10.1109/tmi.2019.2953901

Cited by 19 publications

(18 citation statements)

References 58 publications

(130 reference statements)

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“…Uncertainty quantification through probabilistic assessment is a statistical tool to understand a complex engineering process under extrinsic or intrinsic variations [20,21]. It is important a decision-maker to quantify uncertainties for making a sensible judgement about the model response [22,23].…”

Section: Probabilistic Analysis For Single Diode Model Of a Solar Cellmentioning

confidence: 99%

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

Samadhiya

Namrata

2021

Complex Intell. Syst.

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The paper presents a hierarchical polynomial chaos expansion-based probabilistic approach to analyze the single diode solar cell model under Gaussian parametric uncertainty. It is important to analyze single diode solar cell model response under random events or factors due to uncertainty propagation. The optimal values of five electrical parameters associated with the single diode model are estimated using six deterministic optimization techniques through the root-mean-square minimization approach. Values corresponding to the best objective function response are further utilized to describe the probabilistic design space of each random electrical parameter under uncertainty. Adequate samples of each parameter corresponding Gaussian uncertain distribution are generated using Latin hypercube sampling. Furthermore, a multistage probabilistic approach is adopted to evaluate the model response using low-cost polynomial chaos series expansion and perform global sensitivity analysis under specified Gaussian distribution. Coefficients of polynomial basis functions are calculated using least square and least angle regression techniques. Unlike the highly non-linear and complex single diode representation of solar cells, the polynomial chaos expansion model provides a low computational burden and reduced complexity. To ensure reproducibility, probabilistic output response computed using proposed polynomial chaos expansion model is compared with the true model response. Finally, a multidimensional sensitivity analysis is performed through Sobol decomposition of polynomial chaos series representation to quantify the contribution of each parameter to the variance of the probabilistic response. The validation and assessment result shows that the output probabilistic response of the solar cell under Gaussian parametric uncertainty correlates to a Rayleigh probability distribution function. Output response is characterized by a mean value of 0.0060 and 0.0760 for RTC France and Solarex MSX83 solar cells, respectively. The standard deviation of $$ \pm $$ ± 0.0034 and $$ \pm $$ ± 0.0052 was observed in the probabilistic response for RTC France and Solarex MSX83 solar cells, respectively.

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Section: Probabilistic Analysis For Single Diode Model Of a Solar Cellmentioning

confidence: 99%

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

Samadhiya

Namrata

2021

Complex Intell. Syst.

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“…They design a learning-based diabetic retinopathy grading CAD system to provide a medically interpretable explanation, the designed system could estimate how uncertain that the prediction is. Considering the intrinsic characteristics of the tracer-kinetic model, Bliesener et al [51] develop a approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty, they train a powerful neural network to estimate the uncertainties for each voxel, which are specific to the patient, exam, and lesion. Recently, various deep learning methods take advantage of predictive uncertainty at inference time in several ways, These studies all try to provide a more reliable interpretation of predictions for experts [27]- [31].…”

Section: A Uncertainty Estimationmentioning

confidence: 99%

DONet: Dual Objective Networks for Skin Lesion Segmentation

Wang

Wei²,

Qian

et al. 2020

Preprint

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“…Recent investigations addressed these limitations by using DL-based direct estimation techniques. Bliesener et al 20 estimated K trans from fully sampled data using DL at each pixel individually using one-dimensional (1D) convolution. However, instead of image as input, they utilized concentration maps and arterial input function (AIF) as input.…”

Section: Introductionmentioning

confidence: 99%

VTDCE‐Net: A time invariant deep neural network for direct estimation of pharmacokinetic parameters from undersampled DCE MRI data

Rastogi¹,

Dutta²,

Yalavarthy³

2022

Medical Physics

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To propose a robust time and space invariant deep learning (DL) method to directly estimate the pharmacokinetic/tracer kinetic (PK/TK) parameters from undersampled dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data. Methods: DCE-MRI consists of 4D (3D-spatial + temporal) data and has been utilized to estimate 3D (spatial) tracer kinetic maps. Existing DL architecture for this task needs retraining for variation in temporal and/or spatial dimensions. This work proposes a DL algorithm that is invariant to training and testing in both temporal and spatial dimensions. The proposed network was based on a 2.5-dimensional Unet architecture, where the encoder consists of a 3D convolutional layer and the decoder consists of a 2D convolutional layer. The proposed VTDCE-Net was evaluated for solving the ill-posed inverse problem of directly estimating TK parameters from undersampled k − t space data of breast cancer patients, and the results were systematically compared with a total variation (TV) regularization based direct parameter estimation scheme. In the breast dataset, the training was performed on patients with 32 time samples, and testing was carried out on patients with 26 and 32 time samples. Translation of the proposed VTDCE-Net for brain dataset to show the generalizability was also carried out. Undersampling rates (R) of 8×, 12×, and 20× were utilized with PSNR and SSIM as the figures of merit in this evaluation. TK parameter maps estimated from fully sampled data were utilized as ground truth. Results: Experiments carried out in this work demonstrate that the proposed VTDCE-Net outperforms the TV scheme on both breast and brain datasets across all undersampling rates. For K trans and V p maps, the improvement over TV is as high as 2 and 5 dB, respectively, using the proposed VTDCE-Net. Conclusion: Temporal points invariant DL network that was proposed in this work to estimate the TK-parameters using DCE-MRI data has provided stateof -the-art performance compared to standard image reconstruction methods and is shown to work across all undersampling rates.

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Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network

Cited by 19 publications

References 58 publications

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

DONet: Dual Objective Networks for Skin Lesion Segmentation

VTDCE‐Net: A time invariant deep neural network for direct estimation of pharmacokinetic parameters from undersampled DCE MRI data

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