Rapid estimation of patient-specific organ doses using a deep learning network

[Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography]

CLINICAL-METHODOLOGICAL PROBLEM

Imaging procedures employing ionizing radiation require compliance with European directives and national regulations in order to protect patients. Each exposure must be indicated, individually adapted, and documented. Unacceptable dose exceedances must be detected and reported. These tasks are time-consuming and require meticulous diligence.

STANDARD RADIOLOGICAL METHODS

Computed tomography (CT) is the most important contributor to medical radiation exposure. Optimizing the patient's dose is therefore mandatory. Use of modern technology and reconstruction algorithms already reduces exposure. Checking the indication, planning, and performing the examination are further important process steps with regard to radiation protection. Patient exposure is usually monitored by dose management systems (DMS). In special cases, a risk assessment is required by calculating the organ doses.

METHODOLOGICAL INNOVATIONS

Artificial intelligence (AI)-assisted techniques are increasingly used in various steps of the process: they support examination planning, improve patient positioning, and enable automated scan length adjustments. They also provide real-time estimates of individual organ doses.

EVALUATION

The integration of AI into medical imaging is proving successful in terms of dose optimization in various areas of the radiological workflow, from reconstruction to examination planning and performing exams. However, the use of AI in conjunction with DMS has not yet been considered on a large scale.

PRACTICAL RECOMMENDATION

AI processes offer promising tools to support dose management. However, their implementation in the clinical setting requires further research, extensive validation, and continuous monitoring.

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning

BACKGROUND

Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research.

PURPOSE

The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms.

METHODS

A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics.

RESULTS

The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent.

CONCLUSIONS

We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.

Is deep learning-enabled real-time personalized CT dosimetry feasible using only patient images as input?

PURPOSE

To propose a novel deep-learning based dosimetry method that allows quick and accurate estimation of organ doses for individual patients, using only their computed tomography (CT) images as input.

METHODS

Despite recent advances in medical dosimetry, personalized CT dosimetry remains a labour-intensive process. Current state-of-the-art methods utilize time-consuming Monte Carlo (MC) based simulations for individual organ dose estimation in CT. The proposed method uses conditional generative adversarial networks (cGANs) to substitute MC simulations with fast dose image generation, based on image-to-image translation. The pix2pix architecture in conjunction with a regression model was utilized for the generation of the synthetic dose images. The lungs, heart, breast, bone and skin were manually segmented to estimate and compare organ doses calculated using both the original and synthetic dose images, respectively.

RESULTS

The average organ dose estimation error for the proposed method was 8.3% and did not exceed 20% for any of the organs considered. The performance of the method in the clinical environment was also assessed. Using segmentation tools developed in-house, an automatic organ dose calculation pipeline was set up. Calculation of organ doses for heart and lung for each CT slice took about 2 s.

CONCLUSIONS

This work shows that deep learning-enabled personalized CT dosimetry is feasible in real-time, using only patient CT images as input.

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm

PURPOSE

This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources.

MATERIALS AND METHODS

We randomly selected the image data of 723 patients who underwent thoracic CT examinations. We performed auto-segmentation based on the selected data to generate the regions of interest (ROIs) of thoracic organs using the DeepViewer software. For each patient, radiomics features of the thoracic ROIs were extracted via the Pyradiomics package. The support vector regression (SVR) model was trained based on the radiomics features and reference organ dose obtained by Monte Carlo (MC) simulation. The root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were evaluated. The robustness was verified by randomly assigning patients to the train and test sets of data and comparing regression metrics of different patient assignments.

RESULTS

For the right lung, left lung, lungs, esophagus, heart, and trachea, results showed that the trained SVR model achieved the RMSEs of 2 mGy to 2.8 mGy on the test sets, 1.5 mGy to 2.5 mGy on the train sets. The calculated MAPE ranged from 0.1 to 0.18 on the test sets, and 0.08 to 0.15 on the train sets. The calculated R-squared was 0.75 to 0.89 on test sets.

CONCLUSIONS

By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.

Image detection of aortic dissection complications based on multi-scale feature fusion

Background

Aortic dissection refers to the true and false two-lumen separation of the aortic wall, in which the blood in the aortic lumen enters the aortic mesomembrane from the tear of the aortic intima to separate the mesomembrane and expand along the long axis of the aorta.

Purpose

In view of the problems of individual differences, complex complications and many small targets in clinical aortic dissection detection, this paper proposes a convolution neural network MFF-FPN (Multi-scale Feature Fusion based Feature Pyramid Network) for the detection of aortic dissection complications.

Methods

The proposed model uses Resnet50 as the backbone for feature extraction and builds a pyramid structure to fuse low-level and high-level feature information. We add an attention mechanism to the backbone network, which can establish inter-dependencies between feature graph channels and enhance the representation quality of CNN.

Results

The proposed method has a mean average precision (MAP) of 99.40% in the task of multi object detection for aortic dissection and complications, which is higher than the accuracy of 96.3% on SSD model and 99.05% on YoloV7 model. It greatly improves the accuracy of small target detection such as cysts, making it more suitable for clinical focus detection.

Conclusions

The proposed deep learning model achieves feature reuse and focuses on local important information. By adding only a small number of model parameters, we are able to greatly improve the detection accuracy, which is effective in detecting small target lesions commonly found in clinical settings, and also performs well on other medical and natural datasets.

Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm

Objectives. This study aims to develop a method for predicting patient-specific head organ doses by training a support vector regression (SVR) model based on radiomics features and graphics processing unit (GPU)-calculated reference doses.Methods. In this study, 237 patients who underwent brain CT scans were selected, and their CT data were transferred to an autosegmentation software to segment head regions of interest (ROIs). Subsequently, radiomics features were extracted from the CT data and ROIs, and the benchmark organ doses were computed using fast GPU-accelerated Monte Carlo (MC) simulations. The SVR organ dose prediction model was then trained using the radiomics features and benchmark doses. For the predicted organ doses, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated. The robustness of organ dose prediction was verified by changing the patient samples on the training and test sets randomly.Results. For all head organs, the maximal difference between the reference and predicted dose was less than 1 mGy. For the brain, the organ dose was predicted with an absolute error of 1.3%, and theR2reached up to 0.88. For the eyes and lens, the organ doses predicted by SVR achieved an RRMSE of less than 13%, the MAPE ranged from 4.5% to 5.5%, and theR2values were more than 0.7.Conclusions. Patient-specific head organ doses from CT examinations can be predicted within one second with high accuracy, speed, and robustness by training an SVR using radiomics features.