Uncertainty Assessment for Deep Learning Radiotherapy Applications

In the last 5 years, deep learning applications for radiotherapy have undergone great development. An advantage of radiotherapy over radiological applications is that data in radiotherapy are well structured, standardized, and annotated. Furthermore, there is much to be gained in automating the current laborious workflows in radiotherapy. After the initial peak in the belief in deep learning, researchers have also identified fundamental weaknesses of deep learning. The basic assumption in deep learning is that the training and test data originate from the same data generating process. This is not always clear-cut in clinical practice, eg, data acquired with 2 different scanners of different vendors might not originate from the same data generating process. Furthermore, it is important to realize residual uncertainties remain even if test data arise from the same data generating process as the training data. As deep learning applications are being introduced in clinical radiotherapy workflows, a deep learning model must express to a user when a prediction exceeds a certain uncertainty threshold. The literature on uncertainty assessment for deep learning applications in radiotherapy is still in its infancy; however, quite a body of literature exists on the validity and uncertainty of deep learning models for computer vision applications. This paper tries to explain these general concepts to the radiotherapy community. Concepts of epistemic and aleatoric uncertainties and techniques to model them in deep learning are described in detail. It is discussed how they can be applied to maximize confidence in automated deep learning-driven workflows. Their usage is demonstrated in 3 examples from radiotherapy literature on deep learning applications, ie, dose prediction, synthetic CT generation, and contouring. In the final part, some of the key elements to ensure confidence and automatic alerting that are still missing are discussed. State-of-the-art automatic solutions for checking within-distribution vs out-of-distribution test samples are discussed. However, these methodologies are still immature, and strict QA protocols and close human supervision will still be needed. Nevertheless, deep learning models offer already much value for radiotherapy

Intersubject specific absorption rate variability analysis through construction of 23 realistic body models for prostate imaging at 7T

Purpose: For ultrahigh field (UHF) MRI, the expected local specific absorption rate (SAR) distribution is usually calculated by numerical simulations using a limited number of generic body models and adding a safety margin to take into account intersubject variability. Assessment of this variability with a large model database would be desirable. In this study, a procedure to create such a database with accurate subject-specific models is presented. Using 23 models, intersubject variability is investigated for prostate imaging at 7T with an 8-channel fractionated dipole antenna array with 16 receive loops. Method: From Dixon images of a volunteer acquired at 1.5T with a mockup array in place, an accurate dielectric model is built. Following this procedure, 23 subject-specific models for local SAR assessment at 7T were created enabling an extensive analysis of the intersubject B1 + and peak local SAR variability. Results: For the investigated setup, the maximum possible peak local SAR ranges from 2.6 to 4.6 W/kg for 8 × 1 W input power. The expected peak local SAR values represent a Gaussian distribution (µ∕σ =2.29∕0.29 W/kg) with realistic prostate-shimmed phase settings and a gamma distribution Γ(24,0.09) with multidimensional radiofrequency pulses. Prostate-shimmed phase settings are similar for all models. Using 1 generic phase setting, average B1 + reduction is 7%. Using only 1 model, the required safety margin for intersubject variability is 1.6 to 1.8. Conclusion: The presented procedure allows for the creation of a customized model database. The results provide valuable insights into B1 + and local SAR variability. Recommended power thresholds per channel are 3.1 W with phase shimming on prostate or 2.6 W for multidimensional pulses

Opening a new window on MR-based Electrical Properties Tomography with deep learning

In the radiofrequency (RF) range, the electrical properties of tissues (EPs: conductivity and permittivity) are modulated by the ionic and water content, which change for pathological conditions. Information on tissues EPs can be used e.g. in oncology as a biomarker. The inability of MR-Electrical Properties Tomography techniques (MR-EPT) to accurately reconstruct tissue EPs by relating MR measurements of the transmit RF field to the EPs limits their clinical applicability. Instead of employing electromagnetic models posing strict requirements on the measured MRI quantities, we propose a data driven approach where the electrical properties reconstruction problem can be casted as a supervised deep learning task (DL-EPT). DL-EPT reconstructions for simulations and MR measurements at 3 Tesla on phantoms and human brains using a conditional generative adversarial network demonstrate high quality EPs reconstructions and greatly improved precision compared to conventional MR-EPT. The supervised learning approach leverages the strength of electromagnetic simulations, allowing circumvention of inaccessible MR electromagnetic quantities. Since DL-EPT is more noise-robust than MR-EPT, the requirements for MR acquisitions can be relaxed. This could be a major step forward to turn electrical properties tomography into a reliable biomarker where pathological conditions can be revealed and characterized by abnormalities in tissue electrical properties

Opening a new window on MR-based Electrical Properties Tomography with deep learning

In the radiofrequency (RF) range, the electrical properties of tissues (EPs: conductivity and permittivity) are modulated by the ionic and water content, which change for pathological conditions. Information on tissues EPs can be used e.g. in oncology as a biomarker. The inability of MR-Electrical Properties Tomography techniques (MR-EPT) to accurately reconstruct tissue EPs by relating MR measurements of the transmit RF field to the EPs limits their clinical applicability. Instead of employing electromagnetic models posing strict requirements on the measured MRI quantities, we propose a data driven approach where the electrical properties reconstruction problem can be casted as a supervised deep learning task (DL-EPT). DL-EPT reconstructions for simulations and MR measurements at 3 Tesla on phantoms and human brains using a conditional generative adversarial network demonstrate high quality EPs reconstructions and greatly improved precision compared to conventional MR-EPT. The supervised learning approach leverages the strength of electromagnetic simulations, allowing circumvention of inaccessible MR electromagnetic quantities. Since DL-EPT is more noise-robust than MR-EPT, the requirements for MR acquisitions can be relaxed. This could be a major step forward to turn electrical properties tomography into a reliable biomarker where pathological conditions can be revealed and characterized by abnormalities in tissue electrical properties

A deep learning method for image-based subject-specific local SAR assessment

\u3cp\u3ePurpose: Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work, we present a deep learning–based method for image-based subject-specific local SAR assessment. We propose to train a convolutional neural network to learn a “surrogate SAR model” to map the relation between subject-specific (Formula presented.) maps and the corresponding local SAR. Method: Our database of 23 subject-specific models with an 8–transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex (Formula presented.) maps and local SAR distributions were used to train a conditional generative adversarial network. Extra penalization for local SAR underestimation errors was included in the loss function. In silico and in vivo validation were performed. Results: In silico cross-validation shows a good qualitative and quantitative match between predicted and ground-truth local SAR distributions. The peak local SAR estimation error distribution shows a mean overestimation error of 15% with 13% probability of underestimation. The higher accuracy of the proposed method allows the use of less conservative safety factors compared with standard procedures. In vivo validation shows that the method is applicable with realistic measurement data with impressively good qualitative and quantitative agreement to simulations. Conclusion: The proposed deep learning method allows online image-based subject-specific local SAR assessment. It greatly reduces the uncertainty in current state-of-the-art SAR assessment methods, reducing the time in the examination protocol by almost 25%.\u3c/p\u3