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)

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

${\bf Purpose}$: Earlier work showed that IVIM-NET$_{orig}$, an unsupervised physics-informed deep neural network, was more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to DWI. This study presents an improved version: IVIM-NET$_{optim}$, and characterizes its superior performance in pancreatic ductal adenocarcinoma (PDAC) patients. ${\bf Method}$: In simulations (SNR=20), the accuracy, independence and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, # hidden layers, dropout, batch normalization, learning rate), by calculating the NRMSE, Spearman's $\rho$, and the coefficient of variation (CV$_{NET}$), respectively. The best performing network, IVIM-NET$_{optim}$ was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET$_{optim}$'s performance was evaluated in 23 PDAC patients. 14 of the patients received no treatment between scan sessions and 9 received chemoradiotherapy between sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. ${\bf Results}$: In simulations, IVIM-NET$_{optim}$ outperformed IVIM-NET$_{orig}$ in accuracy (NRMSE(D)=0.18 vs 0.20; NMRSE(f)=0.22 vs 0.27; NMRSE(D*)=0.39 vs 0.39), independence ($\rho$(D*,f)=0.22 vs 0.74) and consistency (CV$_{NET}$ (D)=0.01 vs 0.10; CV$_{NET}$ (f)=0.02 vs 0.05; CV$_{NET}$ (D*)=0.04 vs 0.11). IVIM-NET$_{optim}$ showed superior performance to the LS and Bayesian approaches at SNRs<50. In vivo, IVIM-NET$_{optim}$ sshowed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NET$_{optim}$ detected the most individual patients with significant parameter changes compared to day-to-day variations. ${\bf Conclusion}$: IVIM-NET$_{optim}$ is recommended for IVIM fitting to DWI data

Glutathione in Cancer Cell Death

Glutathione (L-γ-glutamyl-L-cysteinyl-glycine; GSH) in cancer cells is particularly relevant in the regulation of carcinogenic mechanisms; sensitivity against cytotoxic drugs, ionizing radiations, and some cytokines; DNA synthesis; and cell proliferation and death. The intracellular thiol redox state (controlled by GSH) is one of the endogenous effectors involved in regulating the mitochondrial permeability transition pore complex and, in consequence, thiol oxidation can be a causal factor in the mitochondrion-based mechanism that leads to cell death. Nevertheless GSH depletion is a common feature not only of apoptosis but also of other types of cell death. Indeed rates of GSH synthesis and fluxes regulate its levels in cellular compartments, and potentially influence switches among different mechanisms of death. How changes in gene expression, post-translational modifications of proteins, and signaling cascades are implicated will be discussed. Furthermore, this review will finally analyze whether GSH depletion may facilitate cancer cell death under in vivo conditions, and how this can be applied to cancer therapy

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Quantitative imaging to characterize pancreatic and esophagogastric cancer

Patient with pancreatic and esophageal cancer have a dismal prognosis and treatment response varies greatly between them. In the clinical work-up of pancreatic and esophageal cancer patients there is therefore a great demand for non-invasive makers that can stratify patients and predict treatment response. Potentially, such markers could be extracted using non-invasive, quantitative, imaging methods. Such quantitative imaging biomarkers can inform on biological processes involved in the development and treatment of cancer. The general aim of this thesis is to characterize the tumor microenvironment of pancreatic and esophagogastric cancer by quantitative imaging and to investigate if these methods enable to monitor treatment and predict patient outcome in these cancers. We develop, validate and apply multiple imaging techniques and processing methods on multiple imaging modalities in both a clinical and pre-clinical setting to investigate the potential of imaging biomarkers in the clinical work-up of pancreatic and esophagogastric cancer. The methods and results described in this thesis demonstrate the potential impact of quantitative imaging biomarkers in clinical practice. However, implementation of such methods in a clinical work-up and routine is still lacking

Minimizing the Acquisition Time for Intravoxel Incoherent Motion Magnetic Resonance Imaging Acquisitions in the Liver and Pancreas

OBJECTIVES: The aim of this study was to determine the combination of b-values and signal averages for diffusion-weighted image acquisitions that render the minimum acquisition time necessary to obtain values of the intravoxel incoherent motion (IVIM) model parameters in vivo in the pancreas or liver with acceptable reproducibility. MATERIALS AND METHODS: For 16 volunteers, diffusion-weighted images, with 14 b-values and 9 acquisitions per b-value, were acquired in 2 scan sessions. The IVIM model was fitted to data from lesion-sized regions of interest (ROIs) (1.7 cm(3)) as well as organ-sized ROIs in the pancreas and liver. By deleting data during analyzes, the IVIM model parameters, D and f, could be determined as a function of the number of b-values as well as the number of measurements per b-value taken along. For the IVIM model parameters, we examined the behavior reproducibility, in the form of the within-subject coefficient of variation (CVw), as a function of the amount of data taken along in the fits. Finally, we determined the minimum acquisition time required as a function of CVw. RESULT: For the lesion-sized ROI, the intersession CVws were 8%/46% and 13%/55% for D/f in the pancreas and liver, respectively, when all data were taken along. For 1.2 times larger CVws, acquisition in the pancreas could be done in 5:15 minutes using 9 acquisitions per b-value at b = 0, 30, 50, 65, 100, 375, and 500 mm(-2)s and for the liver in 2:15 using 9 acquisitions per b-value at b = 0, 40, and 500 mm(-2)s. CONCLUSIONS: Acquiring 7 b-values in the pancreas and 3 b-values in the liver only decreases the reproducibility by 20% compared with an acquisition with 14 b-values. The understanding of the behavior of reproducibility as a function of b-values and acquisitions per b-values scanned will help researchers select the shortest IVIM protocol

Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients

In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74–0.83) and 0.65 (95% ci: 0.57–0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83–0.90) and 0.79 (95% ci 0.72–0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

Purpose: Earlier work showed that IVIM-NET orig, an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NET optim, and characterizes its superior performance in pancreatic cancer patients. Method: In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CV NET), respectively. The best performing network, IVIM-NET optim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET optim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. Results: In simulations (SNR = 20), IVIM-NET optim outperformed IVIM-NET orig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CV NET(D) = 0.013 vs 0.104; CV NET(f) = 0.020 vs 0.054; CV NET(D*) = 0.036 vs 0.110). IVIM-NET optim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NET optim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NET optim detected the most individual patients with significant parameter changes compared to day-to-day variations. Conclusion: IVIM-NET optim is recommended for accurate, informative, and consistent IVIM fitting to DWI data

Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an MRI technique for quantifying perfusion that can be used in clinical applications for classification of tumours and other types of diseases. Conventionally, the non-linear least squares (NLLS) methods is used for tracer-kinetic modelling of DCE data. However, despite promising results, NLLS suffers from long processing times (minutes-hours) and noisy parameter maps due to the non-convexity of the cost function. In this work, we investigated physics-informed deep neural networks for estimating physiological parameters from DCE-MRI signal-curves. Three voxel-wise temporal frameworks (FCN, LSTM, GRU) and two spatio-temporal frameworks (CNN, U-Net) were investigated. The accuracy and precision of parameter estimation by the temporal frameworks were evaluated in simulations. All networks showed higher precision than the NLLS. Specifically, the GRU showed to decrease the random error on ve by a factor of 4.8 with respect to the NLLS for noise (SD) of 1/20. The accuracy was better for the prediction of the ve parameter in all networks compared to the NLLS. The GRU and LSTM worked with arbitrary acquisition lengths. The GRU was selected for in vivo evaluation and compared to the spatio-temporal frameworks in 28 patients with pancreatic cancer. All neural network approaches showed less noisy parameter maps than the NLLS. The GRU had better test-retest repeatability than the NLLS for all three parameters and was able to detect one additional patient with significant changes in DCE parameters post chemo-radiotherapy. Although the U-Net and CNN had even better test-retest characteristics than the GRU, and were able to detect even more responders, they also showed potential systematic errors in the parameter maps. Therefore, we advise using our GRU framework for analysing DCE data

Repeatability and correlations of dynamic contrast enhanced and T2* MRI in patients with advanced pancreatic ductal adenocarcinoma

In current oncological practice of pancreatic ductal adenocarcinoma (PDAC), there is a great demand for response predictors and markers for early treatment evaluation. In this study, we investigated the repeatability and the interaction of dynamic contrast enhanced (DCE) and T2* MRI in patients with advanced PDAC to enable for such evaluation using these techniques. p). Quantitative R2* (1/T2*) maps were obtained from the multi-echo T2* images. Differences between normal and cancerous pancreas were determined using a Wilcoxon matched pairs test. Repeatability was obtained using Bland-Altman analysis and relations between DCE and T2*/R2* were observed by Spearman correlation and voxel-wise binned plots of tumor voxels. e. ewith tissue T2* and R2* indicating substantial value of these parameters for detecting tumor hypoxia in future studies. The results from our study pave the way for further response evaluation studies and patient selection based on DCE and T2* parameter