Colorectal Cancer (CRC) is the third most common cancer and second most common cause of cancer deaths. Most CRCs develop from large colorectal polyps, but most polyps remain smaller than 6 mm and will never develop into cancer. Therefore, conservative selective polypectomy based on polyp size would be a much more effective colorectal screening strategy than the current practice of removing all polyps. For this purpose, automated polyp measurement would be more reproducible and perhaps more precise than manual polyp measurement in CT colonography. However, for an accurate and explainable image-based measurement, it is first necessary to determine the 3D region of the polyp. We investigated the polyp segmentation performance of a traditional 3D U-Net, transformer-based U-Net, and denoising diffusion-based U-Net on a photon-counting CT (PCCT) colonography dataset. The networks were trained on 946 polyp volumes of interest (VOIs) collected from conventional clinical CT colonography datasets, and they were tested on 17 polyp VOIs extracted from a PCCT colonography dataset of an anthropomorphic colon phantom. All three segmentation networks yielded satisfactory segmentation accuracies with average Dice scores ranging between 0.73-0.75. These preliminary results and experiences are expected to be useful in guiding the development of a deep-learning tool for reliable estimation of the polyp size for the diagnosis and management of patients in CRC screening.
We proposed a deep learning-based method for single-heartbeat 4D cardiac CT reconstruction, where a single cardiac cycle was split into multiple phases for reconstruction. First, we pre-reconstruct each phase using the projection data from itself and the neighboring phases. The pre-reconstructions are fed into a supervised registration network to generate the deformation fields between different phases. The deformation fields are trained so that it can match the ground truth images from the corresponding phases. The deformation fields are then used in the FBP-and-wrap method for motion-compensated reconstruction, where a subsequent network is used to remove residual artifacts. The proposed method was validated with simulation data from 40 4D cardiac CT scans and demonstrated improved RMSE and SSIM and less blurring compared to FBP and PICCS.
Colorectal cancer (CRC) is the third most common cancer type and the second most common cause of cancer deaths. CT colonography is a nearly ideal safe and accurate method for effective colorectal screening and prevention of CRCs, but the ionizing radiation of CT has been cited as a risk for population screening by CT colonography. Photon-counting CT (PCCT) can be used to address that risk. However, there have been no studies on the performance of automated polyp detection in PCCT colonography. In this preliminary study, we investigated the feasibility of the automated detection of clinically significant polyps from a PCCT colonography dataset. A laxative-free CT colonography examination that was simulated on an anthropomorphic colon phantom was scanned by use of a 16-slice PCCT scanner at 120 kVp and 40 mA. Our previously developed computer-aided detection (CADe) system was used to detect polyps from the PCCT dataset. The polyp detection performance was evaluated by use of 10-fold cross-validation. Our preliminary results show that the CADe system was able to detect the clinically significant polyps ≥6 mm in size from the PCCT colonography dataset at a high accuracy. This indicates that PCCT colonography is indeed a very promising approach for addressing the remaining obstacles of CT colonography in the population screening for CRC.
Photon-counting CT is an emerging technology with several advantages over conventional CT technology, such as the ability to reduce radiation exposure to CT. In this study, we evaluated the effect of the use of photon-counting CT colonography on the performance of our self-supervised 3D generative adversarial learning (GAN)-based electronic cleansing (EC) scheme. We simulated a fecal-tagging CT colonography case by use of an anthropomorphic colon phantom. The empty phantom served as the ground truth for the EC. Both the empty and fecal-tagging versions of the phantom were scanned by use of a photon-counting CT and a conventional CT scanner. We evaluated the performance of the EC scheme by using 100 paired volumes of interest extracted from the corresponding locations on the empty and fecal-tagging phantoms that had not been used for the training of the EC scheme. The peak signal-to-noise ratio was used as the metric for the quality of the EC images generated. Our preliminary results indicate that using photon-counting CT colonography at a low dose generates higher-quality EC images than those obtained by using conventional CT colonography. The results also demonstrate that our self-supervised training scheme generates images of higher quality than those obtained by use of conventional supervised training. Therefore, photon-counting CT colonography combined with our self-supervised 3DGAN EC scheme is expected to provide EC images of the highest quality in low-dose fecal-tagging CT colonography.
Dual energy computed tomography (DECT) usually uses 80kVp and 140kVp for patient scans. Due to high attenuation, the 80kVp image may become too noisy for reduced photon flux scenarios such as low-dose protocols or large-sized patients, further leading to unacceptable decomposed image quality. In this paper, we proposed a deep-neural-network-based reconstruction approach to compensate for the increased noise in low-dose DECT scan. The learned primal-dual network structure was used in this study, where the input and output of the network consisted of both low- and high-energy data. The network was trained on 30 patients who went through normal-dose chest DECT scans with simulated noises inserted into the raw data. It was further evaluated on another 10 patients undergoing half-dose chest DECT scans. Improved image quality close to the normal-dose scan was achieved and no significant bias was found on Hounsfield units (HU) values or iodine concentration.
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely applied to medical imaging denoising applications. In this work, based on the MAPEM algorithm, we propose a novel unrolled neural network framework for 3D PET image reconstruction. In this framework, the convolutional neural network is combined with the MAPEM update steps so that data consistency can be enforced. Both simulation and clinical datasets were used to evaluate the effectiveness of the proposed method. Quantification results show that our proposed MAPEM-Net method can outperform the neural network and Gaussian denoising methods.
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely applied to medical imaging denoising applications. In this work, based on the expectation maximization (EM) algorithm, we propose an unrolled neural network framework for PET image reconstruction, named EMnet. An innovative feature of the proposed framework is that the deep neural network is combined with the EM update steps in a whole graph. Thus data consistency can act as a constraint during network training. Both simulation data and real data are used to evaluate the proposed method. Quantification results show that our proposed EMnet method can outperform the neural network denoising and Gaussian denoising methods.
In computed tomographic (CT) image reconstruction, image prior design and parameter tuning are important to improving the image reconstruction quality from noisy or undersampled projections. In recent years, the development of deep learning in medical image reconstruction made it possible to automatically find both suitable image priors and hyperparameters. By unrolling reconstruction algorithm to finite iterations and parameterizing prior functions and hyperparameters with deep artificial neural networks, all the parameters can be learned end-to-end to reduce the difference between reconstructed images and the training ground truth. Despite of its superior performance, the unrolling scheme suffers from huge memory consumption and computational cost in the training phase, made it hard to apply to 3 dimensional applications in CT, such as cone-beam CT, helical CT, tomosynthesis, etc. In this paper, we proposed a training-time computational-efficient cascaded neural network for CT image reconstruction, which had several sequentially trained cascades of networks for image quality improvement, connected with data fidelity correction steps. Each cascade was trained purely in the image domain, so that image patches could be utilized for training, which would significantly accelerate the training process and reduce memory consumption. The proposed method was fully scalable to 3D data with current hardware. Simulation of sparse-view sampling were done and demonstrated that the proposed method could achieve similar image quality compared to the state-of-the-art unrolled networks.
At present, there are mainly three x-ray imaging modalities for dental clinical diagnosis: radiography, panorama and computed tomography (CT). We develop a new x-ray digital intra-oral tomosynthesis (IDT) system for quasi-three-dimensional dental imaging which can be seen as an intermediate modality between traditional radiography and CT. In addition to normal x-ray tube and digital sensor used in intra-oral radiography, IDT has a specially designed mechanical device to complete the tomosynthesis data acquisition. During the scanning, the measurement geometry is such that the sensor is stationary inside the patient’s mouth and the x-ray tube moves along an arc trajectory with respect to the intra-oral sensor. Therefore, the projection geometry can be obtained without any other reference objects, which makes it be easily accepted in clinical applications. We also present a compressed sensing-based iterative reconstruction algorithm for this kind of intra-oral tomosynthesis. Finally, simulation and experiment were both carried out to evaluate this intra-oral imaging modality and algorithm. The results show that IDT has its potentiality to become a new tool for dental clinical diagnosis.
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