Abstract: Systems and methods are provided for predictive volumetric and structural evaluation of petroleum product containers. The system includes a computing device in communication with data input devices and implementation tools including calibration devices for measuring tank volume among other physical parameters bearing on tank volume. The computing device receives sets of historical physical parameter data for a plurality of tanks and, using machine learning (ML), generates predictive ML models for estimating volumetric parameters of tanks. The predictive model is applied by the system to historical and current data values to estimate current volumetric parameters for a given tank and, based on the results, the system performs or coordinates further operations for the given tank using an implementation tool. The further opera ions can include inventory management, physical calibration, maintenance and inspection as well as system evaluation and control operations.

Abstract: Systems and methods for multi-modal, multi-resolution deep learning neural networks for segmentation, outcomes prediction and longitudinal response monitoring to immunotherapy and radiotherapy are detailed herein. A structure-specific Generational Adversarial Network (SSGAN) is used to synthesize realistic and structure-preserving images not produced using state-of-the art GANs and simultaneously incorporate constraints to produce synthetic images. A deeply supervised, Multi-modality, Multi-Resolution Residual Networks (DeepMMRRN) for tumor and organs-at-risk (OAR) segmentation may be used for tumor and OAR segmentation. The DeepMMRRN may combine multiple modalities for tumor and OAR segmentation. Accurate segmentation is may be realized by maximizing network capacity by simultaneously using features at multiple scales and resolutions and feature selection through deep supervision. DeepMMRRN Radiomics may be used for predicting and longitudinal monitoring response to immunotherapy.

Abstract: Described herein are systems and methods for synthetic CT image creation that allow MR-only radiotherapy of cancer patients, e.g., head and neck (H&N) cancer patients, prostate cancer patients, patients with cancer of the pelvis, abdomen cancer patients, patients with cancer of the extremities, brain cancer patients, or thorax cancer patients. The methods and systems described herein feature image processing techniques that improve the similarity between CT and MR images prior to CT-MR image registration, as well as standardization of the MR intensity histograms prior to MR-MR registration. Application of the techniques result in more accurate assignment of the Hounsfield unit to each point in the synthetic CT compared to other atlas-based methods, providing for more accurate dosing in MR-only radiotherapy simulation and planning.

Abstract: Described herein are systems and methods for synthetic CT image creation that allow MR-only radiotherapy of cancer patients, e.g., head and neck (H&N) cancer patients, prostate cancer patients, patients with cancer of the pelvis, abdomen cancer patients, patients with cancer of the extremities, brain cancer patients, or thorax cancer patients. The methods and systems described herein feature image processing techniques that improve the similarity between CT and MR images prior to CT-MR image registration, as well as standardization of the MR intensity histograms prior to MR-MR registration. Application of the techniques result in more accurate assignment of the Hounsfield unit to each point in the synthetic CT compared to other atlas-based methods, providing for more accurate dosing in MR-only radiotherapy simulation and planning.