Dual-energy computed tomography (DECT) enables to generate a series of virtual monoenergetic images (VMIs). Using VMIs of a desired energy level (5 – 45 keV) can enhance the lesion-to-background and voxel-to-voxel within lesion contrast, because that the lesion material composition may vary from voxel to voxel. However, there are also strong correlation of the voxel values among different energy channels. This correlation may result in redundant information for the VMIs based lesion pathology differentiation. Therefore, we transformed the VMIs in the Karhunen–Loève domain to reduce the correlation. In the new domain, the leading three principal components accounts for more than 99% information and then were used to form a new descriptor for the differentiation task. Two pathological proven datasets were used for the evaluation. Experimental results showed that the VMIs can improved the AUC (area under the receiver operating characteristic curve) value from 0.862 and 0.647 to 0.912 and 0.830 comparing to using the conventional CT.
Osteoporosis is a complex multifactorial skeletal disease and has become a major socioeconomic issue, causing tremendous hospitalization and rehabilitation costs. In addition to age, premature menopause or the use of oral corticosteroids, vertebral fractures are regarded as main risk factors for developing osteoporosis and associated fractures. In this work, we adapt an existing automated pipeline to classify fracture grades of individual vertebrae and the spine as a whole: First, vertebral body centers were identified on CT images by a hierarchical neural network. Next, the fracture grades of individual vertebrae were processed by a multi-head, feed-forward convolutional neural network. The sum of the classified grades was then evaluated according to the German radiology guidelines. A hyperparameter search on validation data showed the most promising results for an output configuration based on three sequentially applied binary classification outputs trained using binary cross-entropy: Grade 0–1 vs 2–3, and 0 vs 1–3, and 0–2 vs 3. In a cross-validation setting on 159 low-dose CT images, our pipeline accurately classified patients to have sum-scores ≥ 2 with sensitivities and specificities of 90 % ± 5.0 % and 87 % ± 2.7 %, respectively. As our method was based on classifying individual vertebrae, we were able to provide both the fracture position and severity to enhance transparency, interpretability and usability.
The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality.
Pulmonary emphysema is a form of Chronic Obstructive Pulmonary Disease (COPD) and a chronic lung condition that results in a breakdown of alveoli walls. Quantitative Computed Tomography (CT) is increasingly used to assess the presence or progression of emphysema. CT quantifications are affected by the acquisition protocols and scanner makes and models. This variability is a major concern for cross-sectional and longitudinal disease characterizations with largescale, multi-institutional datasets. Therefore, CT images need to be harmonized to reflect the patient condition and not the attributes of the imaging systems. The purpose of this study was to develop a physics-based harmonization framework that transforms CT images to a reference quality index (iso resolution and noise conditions) enabling robust emphysema quantifications across varied CT conditions. The harmonizer was developed using a virtual imaging trial (VIT) platform taking advantage of ground truth knowledge and control over CT parameters in VIT. The developed harmonizer was applied to clinical data from the COPDGene dataset to demonstrate its clinical utility. The established imaging biomarkers of “LAA-950” and “Perc15” were selected for emphysema quantifications. Results demonstrated that the harmonizer improved the quantification performance by reducing the bias in “LAA-950" from 7.03 (𝐶𝐼: [6.38, 7.68]) to 0.14 (𝐶𝐼: [0.08, 0.20]) after matching for kernel and from 2.48 (𝐶𝐼: [2.21, 2.76]) to −0.34 (𝐶𝐼: [−0.48, −0.20]) after matching for noise settings on the COPDGene dataset. This developed harmonization framework provides robust emphysema quantifications, enabling objective disease characterizations in large-scale, multi-center, and longitudinal studies.
Deep learning methods are the state-of-the-art for medical imaging segmentation tasks. Still, numerous segmentation algorithms based on heuristic-based methods have been proposed with exceptional results. To validate segmentation algorithms, manual annotations are typically considered as ground truth. However, manual annotations often suffer from inter/intra-operator variability and can also be occasionally inaccurate, especially when considering time-consuming and precise tasks. A sample case is the manual delineation of the lumen-intima (LI) and media-adventitia (MA) borders for intima-media thickness (IMT) measurement in B-mode ultrasound images. In this work, a novel hybrid learning paradigm which combines manual segmentations with the automatic segmentation of a dynamic programming technique for ground truth determination is presented. A profile consensus strategy is proposed to construct the hybrid ground truth. Two open-source datasets (n=2576) were employed for training four deep learning networks using the hybrid learning paradigm and three single source training targets as a comparison. The pipeline was fixed across the four tests and included a Faster R-CNN detection network to locate the carotid artery and then subsequent division into patches which were segmented using a UNet. The validation of the results was performed on an external test set comparing the predictions of the four different models to the annotations of three independent manual operators. The hybrid learning paradigm showed the best overall segmentation results (Dice=0.907±0.037, p<0.001) and demonstrated an exceptional correlation between the mean of three operators and the automatic measure (ICC(2,1)=0.958), demonstrating how the incorporation of heuristic-based segmentation methods within the learning paradigm of a deep neural network can enhance and improve final segmentation performance results.
