Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Age Encoded Adversarial Learning for Pediatric CT Segmentation

Organ segmentation from CT images is critical in the early diagnosis of diseases, progress monitoring, pre-operative planning, radiation therapy planning, and CT dose estimation. However, data limitation remains one of the main challenges in medical image segmentation tasks. This challenge is particularly huge in pediatric CT segmentation due to children's heightened sensitivity to radiation. In order to address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed conditional feature generation segmentation network (CFG-SegNet) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7 years. CFG-SegNet achieved an average segmentation accuracy of 0.681 dice similarity coefficient (DSC) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with four-fold cross-validation. We compared the segmentation accuracy of our proposed method with previously published U-Net results, and our network improved the segmentation accuracy by 2.7%, 2.6%, 2.8%, and 3.4% for the prostate, uterus, liver, and heart, respectively. The results indicate that our high-performing segmentation framework can more precisely segment organs when limited training images are available.

Synthetization of high-dose images using low-dose CT scans

BACKGROUND

Radiation dose reduction has been the focus of many research activities in x-ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients.

PURPOSE

Reconstruction algorithm-based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high-dose images (SHD) from the data acquired with low-dose scans. A representative example is the model-based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high-quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low-dose clinical datasets that overcomes both the noise texture issue and the data availability issue.

METHODS

Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick-slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD.

RESULTS

The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise-texture.

CONCLUSIONS

We present a method to generate SHD datasets from regularly acquired low-dose CT scans. Images produced with the proposed approach exhibit excellent noise-reduction with the desired noise-texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined.

Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method

INTRODUCTION

One of the current approaches to improve our understanding of osteoporosis is to study the development of bone microdamage under mechanical loading. The current practice for evaluating bone microdamage is to quantify damage volume from images of bone samples stained with a contrast agent, often composed of toxic heavy metals and requiring long tissue preparation. This work aims to evaluate the potential of linear microcracks detection and segmentation in trabecular bone samples using well-known deep learning models, namely YOLOv4 and Unet, applied on microCT images.

METHODS

Six trabecular bovine bone cylinders underwent compression until ultimate stress and were subsequently imaged with a microCT at a resolution of 1.95 μm. Two of these samples (samples 1 and 2) were then stained using barium sulfate (BaSO₄) and imaged again. The unstained samples (samples 3-6) were used to train two neural networks YOLOv4 to detect regions with microdamage further combined with Unet to segment the microdamage at the pixel level in the detected regions. Four different model versions of YOLOv4 were compared using the average Intersection over Union (IoU) and the mean average precision (mAP). The performance of Unet was also measured using two segmentation metrics, the Dice Score and the Intersection over Union (IoU). A qualitative comparison was finally done between the deep learning and the contrast agent approaches.

RESULTS

Among the four versions of YOLOv4, the YOLOv4p5 model resulted in the best performance with an average IoU of 45,32% and 51,12% and a mAP of 28.79% and 46.22%, respectively for samples 1 and 2. The segmentation performance of Unet provided better IoU and DICE score on sample 2 compared to sample 1. The poorer performance of the test on sample 1 could be explained by its poorer contrast to noise ratio (CNR). Indeed, sample 1 resulted in a CNR of 7,96, which was worse than the average CNR in the training samples, while sample 2 resulted in a CNR of 10,08. The qualitative comparison between the contrast agent and the deep learning segmentation showed that two different regions were segmented by the two techniques. Deep learning is segmenting the region inside the cracks while the contrast agent segments the region around it or even regions with no visible damage.

CONCLUSION

The combination of YOLOv4 for microdamage detection with Unet for damage segmentation showed a potential for the detection and segmentation of microdamage in trabecular bone. The accuracy of both neural networks achieved in this work is acceptable considering it is their first application in this specific field and the amount of data was limited. Even if the errors from both neural networks are accumulated, the two-steps approach is faster than the semantic segmentation of the whole volume.

Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net

Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality.

Image Sensing and Processing with Convolutional Neural Networks

Convolutional neural networks are a class of deep neural networks that leverage spatial information, and they are therefore well suited to classifying images for a range of applications [...].