Generating PET Attenuation Maps via Sim2Real Deep Learning –Based Tissue Composition Estimation Combined with MLACF
AbstractDeep learning (DL) has recently attracted attention for data processing in positron emission tomography (PET). Attenuation correction (AC) without computed tomography (CT) data is one of the interests. Here, we present, to our knowledge, the first attempt to generate an attenuation map of the human head via Sim2Real DL-based tissue composition estimation from model training using only the simulated PET dataset. The DL model accepts a two-dimensional non-attenuation-corrected PET image as input and outputs a four-channel tissue-composition map of soft tissue, bone, cavity, and background. Then, an attenuation map is...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer
AbstractCervical cancer is a significant health problem worldwide, and early detection and treatment are critical to improving patient outcomes. To address this challenge, a deep learning (DL)-based cervical classification system is proposed using 3D convolutional neural network and Vision Transformer (ViT) module. The proposed model leverages the capability of 3D CNN to extract spatiotemporal features from cervical images and employs the ViT model to capture and learn complex feature representations. The model consists of an input layer that receives cervical images, followed by a 3D convolution block, which extracts feat...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
SAA-SDM: Neural Networks Faster Learned to Segment Organ Images
AbstractIn the field of medicine, rapidly and accurately segmenting organs in medical images is a crucial application of computer technology. This paper introduces a feature map module, Strength Attention Area Signed Distance Map (SAA-SDM), based on the principal component analysis (PCA) principle. The module is designed to accelerate neural networks ’ convergence speed in rapidly achieving high precision. SAA-SDM provides the neural network with confidence information regarding the target and background, similar to the signed distance map (SDM), thereby enhancing the network’s understanding of semantic information rel...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images
AbstractLung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development of computer-aided analysis technology has made it possible to automatically detect lung nodules in Computed Tomography (CT) screening. In this paper, we propose a novel detection network, TiCNet. It is attempted to embed a transformer module in the 3D Convolutional Neural Network (CNN) for pulmonary nodule detection on CT images. First, we integrate the transformer...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Reliable Delineation of Clinical Target Volumes for Cervical Cancer Radiotherapy on CT/MR Dual-Modality Images
This study addresses the integration of magnetic resonance (MR) images to aid in target delineation on computed tomography (CT) images. However, obtaining MR images directly can be challenging. Therefore, we employ AI-based image generation techniques to “intelligentially generate” MR images from CT images to improve CTV delineation based on CT images. To generate high-quality MR images, we propose an attention-guided single-loop image generation model. The model can yield higher-quality images by introducing an attention mechanism in feature ex traction and enhancing the loss function. Based on the generated MR images...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Machine Learning Supported the Modified Gustafson ’s Criteria for Dental Age Estimation in Southwest China
In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson ’s criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to th e modified Gustafson’s criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. T...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
PET KinetiX —A Software Solution for PET Parametric Imaging at the Whole Field of View Level
AbstractKinetic modeling represents the ultimate foundations of PET quantitative imaging, a unique opportunity to better characterize the diseases or prevent the reduction of drugs development. Primarily designed for research, parametric imaging based on PET kinetic modeling may become a reality in future clinical practice, enhanced by the technical abilities of the latest generation of commercially available PET systems. In the era of precision medicine, such paradigm shift should be promoted, regardless of the PET system. In order to anticipate and stimulate this emerging clinical paradigm shift, we developed a construct...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma
The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer
AbstractDeep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cr...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer
AbstractPharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration –time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was us...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists
This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automati...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer
AbstractThe study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a c...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Robustness of Deep Networks for Mammography: Replication Across Public Datasets
In this study, we evaluate four state-of-the-art publicly available models using four publicly available mammography datasets (CBIS-DDSM, INbreast, CMMD, OMI-DB). Where test data was available, published results were replicated. The best-performing model, which achieved an area under the ROC curve (AUC) of 0.88 on internal data from NYU, achieved here an AUC of 0.9 on the external CMMD dataset (N = 826 exams). On the larger OMI-DB dataset (N = 11,440 exams), it achieved an AUC of 0.84 but did not match the performance of individual radiologists (at a specificity of 0.92, the sensitivity was 0.97 for the radiologist...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Fully Automated Measurement of the Insall-Salvati Ratio with Artificial Intelligence
AbstractPatella alta (PA) and patella baja (PB) affect 1 –2% of the world population, but are often underreported, leading to potential complications like osteoarthritis. The Insall-Salvati ratio (ISR) is commonly used to diagnose patellar height abnormalities. Artificial intelligence (AI) keypoint models show promising accuracy in measuring and detecti ng these abnormalities.An AI keypoint model is developed and validated to study the Insall-Salvati ratio on a random population sample of lateral knee radiographs. A keypoint model was trained and internally validated with 689 lateral knee radiographs from five sites in a...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
Automated Quantification of Total Cerebral Blood Flow from Phase-Contrast MRI and Deep Learning
AbstractKnowledge of input blood to the brain, which is represented as total cerebral blood flow (tCBF), is important in evaluating brain health. Phase-contrast (PC) magnetic resonance imaging (MRI) enables blood velocity mapping, allowing for noninvasive measurements of tCBF. In the procedure, manual selection of brain-feeding arteries is an essential step, but is time-consuming and often subjective. Thus, the purpose of this work was to develop and validate a deep learning (DL)-based technique for automated tCBF quantifications. To enhance the DL segmentation performance on arterial blood vessels, in the preprocessing st...
Source: Journal of Digital Imaging - January 10, 2024 Category: Radiology Source Type: research
