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

Robotic Sensor Networks for Security

The development of intelligent surveillance systems is an active research area. In this context, mobile and multifunctional robots have been recently adopted as successful means to reduce fixed installations and the number of devices needed to cover a given area. On the other hand, modern techniques for data fusion and decision making can significantly increase the information content extracted from sensors both mounted on the robots and on the infrastructure. The use of many heterogeneous sensors, the number and complexity of operational tasks required for monitoring and surveillance with autonomous components like robots makes the overall system design very challenging. In this paper we present some ideas and investigations ongoing in SELEX Sistemi Integrati to assess the capability of such a kind of robots-sensors systems to improve the monitoring of large and densely populated indoor areas. In particular a discussion on some of the problems arising in robot guidance and navigation, oriented to the reduction of missed and false alarms is firstly carried out. Some numerical simulations are reported to support the proposed investigations. Follows the description of a possible decision fusion algorithm to identify and track “risky” targets in a dynamic environment with the aid of robots

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

Purpose: 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%

Real‐time assessment of potential peak local specific absorption rate value without phase monitoring: Trigonometric maximization method for worst‐case local specific absorption rate determination

PURPOSE: Multi-transmit MRI systems are typically equipped with dedicated hardware to sample the reflected/lost power in the transmit channels. After extensive calibration, the amplitude and phase of the signal at the feed of each array element can be accurately determined. However, determining the phase is more difficult and monitoring errors can lead to a hazardous peak local specific absorption rate (pSAR10g ) underestimation. For this purpose, methods were published for online maximum potential pSAR10g estimation without relying on phase monitoring, but these methods produce considerable overestimation. We present a trigonometric maximization method to determine the actual worst-case pSAR10g without any overestimation. THEORY AND METHOD: The proposed method takes advantage of the sinusoidal relation between the SAR10g in each voxel and the phases of input signals, to return the maximum achievable SAR10g in a few iterations. The method is applied to determine the worst-case pSAR10g for three multi-transmit array configurations at 7T: (1) body array with eight fractionated dipoles; (2) head array with eight fractionated dipoles; (3) head array with eight rectangular loops. The obtained worst-case pSAR10g values are compared with the pSAR10g values determined with a commonly used method and with a more efficient method based on reference-phases. RESULTS: For each voxel, the maximum achievable SAR10g is determined in less than 0.1 ms. Compared to the reference-phases-based method, the proposed method reduces the mean overestimation of the actual pSAR10g up to 52%, while never underestimating the true pSAR10g . CONCLUSION: The proposed method can widely improve the performance of parallel transmission MRI systems without phase monitoring

Conditional safety margins for less conservative peak local SAR assessment: A probabilistic approach

PURPOSE: The introduction of a linear safety factor to address peak local specific absorption rate (pSAR10g ) uncertainties (eg, intersubject variation, modeling inaccuracies) bears one considerable drawback: It often results in over-conservative scanning constraints. We present a more efficient approach to define a variable safety margin based on the conditional probability density function of the effectively obtained pSAR10g value, given the estimated pSAR10g value. METHODS: The conditional probability density function can be estimated from previously simulated data. A representative set of true and estimated pSAR10g samples was generated by means of our database of 23 subject-specific models with an 8-fractionated dipole array for prostate imaging at 7 T. The conditional probability density function was calculated for each possible estimated pSAR10g value and used to determine the corresponding safety margin with an arbitrary low probability of underestimation. This approach was applied to five state-of-the-art local SAR estimation methods, namely: (1) using just the generic body model "Duke"; (2) using our model library to assess the maximum pSAR10g value over all models; (3) using the most representative "local SAR model"; (4) using the five most representative local SAR models; and (5) using a recently developed deep learning-based method. RESULTS: Compared with the more conventional safety factor, the conditional safety-margin approach results in lower (up to 30%) mean overestimation for all investigated local SAR estimation methods. CONCLUSION: The proposed probabilistic approach for pSAR10g correction allows more accurate local SAR assessment with much lower overestimation, while a predefined level of underestimation is accepted (eg, 0.1%)

An overexploited Italian treasure: past and present distribution and exploitation of the precious red coral Corallium rubrum (L., 1758) (Cnidaria: Anthozoa)

The aim of this paper is to supply an overview of all historical and recent knowledge on landings, fishing and geographic distribution of the red coral banks along the Italian coasts in order to make a contribution to the conservation and future management of this resource. Along the Italian coasts, the banks have been exploited for millennia, using non-selective trawling gear that was banned in Europe in 1994. Today, harvesting is allowed only by scuba divers and regulated by specific laws. We examined 153 years of history of coral fishing in Italy, from Unification (1861) to 2014. Data about the coralline fleets and the amount of coral landings were recorded for the considered span of time. From a quantitative point of view, the coral fishing in Italy in the last 150 years has been dominated by the sub-fossil coral reservoirs from the Sciacca Banks (Sicily Channel), where an extraordinary amount of 18,000 tons was collected in 34 years (1875–1888 and 1893–1914). This amount represents about 90% of all red coral harvested along the Italian coast in the last 150 years. Excluding this period, the average annual yield was initially around 100 tons, decreasing to 28 tons 100 years later, therefore demonstrating a severe overexploitation of the resource. The great part of the deep red coral banks was abandoned because harvesting was no longer profitable. Nevertheless, quantitative data suggest that red coral banks, even though overexploited, are still wide- spread along Italian coasts, mainly in shallow waters. These banks show a remarkable persistence although with a generalised shift towards smaller colony sizes and higher density. The time span necessary in recovering the pristine structure, following the end of the fishing activities, appears very long and the populations of several areas might be unable to re-colonise the old and overexploited banks

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

Purpose: 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%

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

Purpose: 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%