Deep learning-based forward and cross-scatter correction in dual-source CT

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography

BACKGROUND

Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches.

PURPOSE

We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm.

METHODS

DE CBCT projections of quantitative BME phantoms (water containers 100-165 mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x-ray system. The slow kV-switching scan protocol involved a 60 kV low energy (LE) beam and a 120 kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)-based scatter correction. Virtual non-calcium (VNCa) images for BME detection were then generated by projection-domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three-material image-domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20-160 mm) was investigated.

RESULTS

The MC-based scatter correction delivered > 69% reduction of cupping artifacts for moderate to wide collimations (> 80 mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm-sized object, a 20% increase in water concentration (edema) of a trabecular bone-mimicking mixture presented as ∼15 HU VNCa contrast using 80-160 mm beam collimations. The variability with respect to object position in the FOV was modest (< 15% coefficient of variation). The areas under the ROC curve were > 0.9. A femur-sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160 mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI-confirmed edema.

CONCLUSION

Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point-of-care bone health assessment.

Towards real-time EPID-based 3D in vivo dosimetry for IMRT with Deep Neural Networks: A feasibility study

We investigate the potential of the Deep Dose Estimate (DDE) neural network to predict 3D dose distributions inside patients with Monte Carlo (MC) accuracy, based on transmitted EPID signals and patient CTs. The network was trained using as input patient CTs and first-order dose approximations (FOD). Accurate dose distributions (ADD) simulated with MC were given as training targets. 83 pelvic CTs were used to simulate ADDs and respective EPID signals for subfields of prostate IMRT plans (gantry at 0∘). FODs were produced as backprojections from the EPID signals. 581 ADD-FOD sets were produced and divided into training and test sets. An additional dataset simulated with gantry at 90∘ (lateral set) was used for evaluating the performance of the DDE at different beam directions. The quality of the FODs and DDE-predicted dose distributions (DDEP) with respect to ADDs, from the test and lateral sets, was evaluated with gamma analysis (3%,2 mm). The passing rates between FODs and ADDs were as low as 46%, while for DDEPs the passing rates were above 97% for the test set. Meaningful improvements were also observed for the lateral set. The high passing rates for DDEPs indicate that the DDE is able to convert FODs into ADDs. Moreover, the trained DDE predicts the dose inside a patient CT within 0.6 s/subfield (GPU), in contrast to 14 h needed for MC (CPU-cluster). 3D in vivo dose distributions due to clinical patient irradiation can be obtained within seconds, with MC-like accuracy, potentially paving the way towards real-time EPID-based in vivo dosimetry.

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network

BACKGROUND

Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy.

PURPOSE

To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed.

METHODS

In this paper a scatter correction model for CBCT image, the Flip Swin Transformer U-shape network (FSTUNet) model, is proposed. In this model, the advantages of CNN in texture detail and Swin Transformer in global correlation are used to accurately extract shallow and deep features, respectively. Instead of using the original Swin Transformer tandem structure, we build the Flip Swin Transformer Block to achieve a more powerful inter-window association extraction. The validity and clinical relevance of the method is demonstrated through extensive experiments on a Monte Carlo (MC) simulation dataset and frequency split dataset generated by a validated method, respectively.

RESULT

Experimental results on the MC simulated dataset show that the root mean square error of images corrected by the method is reduced from over 100 HU to about 7 HU. Both the structural similarity index measure (SSIM) and the universal quality index (UQI) are close to 1. Experimental results on the frequency split dataset demonstrate that the method not only corrects shading artifacts but also exhibits a high degree of structural consistency. In addition, comparison experiments show that FSTUNet outperforms UNet, Deep Residual Convolutional Neural Network (DRCNN), DSENet, Pix2pixGAN, and 3DUnet methods in both qualitative and quantitative metrics.

CONCLUSIONS

Accurately capturing the features at different levels is greatly beneficial for reconstructing high-quality scatter-free images. The proposed FSTUNet method is an effective solution to CBCT scatter correction and has the potential to improve the accuracy of CBCT image-guided radiation therapy.

Evaluation of the quantitative performance of non-enhanced dual-energy CT X-map in detecting acute ischemic brain stroke: A model observer study using computer simulation

PURPOSE

A simulation study was performed to evaluate the quantitative performance of X-map images-derived from non-enhanced (NE) dual-energy computed tomography (DECT)-in detecting acute ischemic stroke (AIS) compared with that of NE-DECT mixed images.

METHODS

A virtual phantom, 150 mm in diameter, filled with tissues comprising various gray- and white-matter proportions was used to generate pairs of NE-head images at 80 kV and Sn150 kV at three dose levels (20, 40, and 60 mGy). The phantom included an inserted low-contrast object, 15 mm in diameter, with four densities (0%, 5%, 10%, and 15%) mimicking ischemic edema. Mixed and X-map images were generated from these sets of images and compared in terms of detectability of ischemic edema using a channelized Hotelling observer (CHO). The area under the curve (AUC) of the receiver operating characteristic that generated CHO for each condition was used as a figure of merit.

RESULTS

The AUCs of X-map images were always significantly higher than those of mixed images (P < 0.001). The improvement in AUC for X-map images compared with that for mixed images at edema densities was 9.2%-12.6% at 20 mGy, 10.1%-17.7% at 40 mGy, and 14.0%-19.4% at 60 mGy. At any edema density, X-map images at 20 mGy resulted in higher AUCs than mixed images acquired at any other dose level (P < 0.001), which corresponded to a 66% dose reduction on X-map images.

CONCLUSIONS

The simulation study confirmed that NE-DECT X-map images have superior capability of detecting AIS than NE-DECT mixed images.

Model-based three-material decomposition in dual-energy CT using the volume conservation constraint

Objective. We develop a model-based optimization algorithm for 'one-step' dual-energy (DE) CT decomposition of three materials directly from projection measurements.Approach.Since the three-material problem is inherently undetermined, we incorporate the volume conservation principle (VCP) as a pair of equality and nonnegativity constraints into the objective function of the recently reported model-based material decomposition (MBMD). An optimization algorithm (constrained MBMD, CMBMD) is derived that utilizes voxel-wise separability to partition the volume into a VCP-constrained region solved using interior-point iterations, and an unconstrained region (air surrounding the object, where VCP is violated) solved with conventional two-material MBMD. Constrained MBMD (CMBMD) is validated in simulations and experiments in application to bone composition measurements in the presence of metal hardware using DE cone-beam CT (CBCT). A kV-switching protocol with non-coinciding low- and high-energy (LE and HE) projections was assumed. CMBMD with decomposed base materials of cortical bone, fat, and metal (titanium, Ti) is compared to MBMD with (i) fat-bone and (ii) fat-Ti bases.Main results.Three-material CMBMD exhibits a substantial reduction in metal artifacts relative to the two-material MBMD implementations. The accuracies of cortical bone volume fraction estimates are markedly improved using CMBMD, with ∼5-10× lower normalized root mean squared error in simulations with anthropomorphic knee phantoms (depending on the complexity of the metal component) and ∼2-2.5× lower in an experimental test-bench study.Significance.In conclusion, we demonstrated one-step three-material decomposition of DE CT using volume conservation as an optimization constraint. The proposed method might be applicable to DE applications such as bone marrow edema imaging (fat-bone-water decomposition) or multi-contrast imaging, especially on CT/CBCT systems that do not provide coinciding LE and HE ray paths required for conventional projection-domain DE decomposition.

Correction of Bowtie filter induced scatter signals based on air scan data and object scan data

In a cone beam CT system, a bowtie filter brings in additional scatter signals with respect to object induced scatter signals, which can degrade image quality and sometimes result in artifacts. This work aims to improve the image quality of CT scans by analyzing the contribution of bowtie filter induced scatter signals and removing them from projection data. Air calibration is a very useful preprocessing step to eliminate the response variations of detector pixels. Bowtie filter induced scattered x-ray signals of air scans are recorded in air calibration tables and therefore considered as a part of primary signals. However, scattered X-rays behave differently in scanned objects compared to primary x-rays. The difference should be corrected to eliminate the impact of bowtie filter induced scatter signals. A kernel based correction algorithm based on air scan data, named bowtie filter scatter correction algorithm, is applied to estimate and to eliminate the bowtie filter induced scatter signals in object scans. The scatter signals of air scans can be measured with air scans or retrieved from air calibration tables of a CT system, and can be used as input of the correction algorithm to estimate the change of scatter signals caused by the scanned objects in the scan field. Based on the assumption that the scatter signals in the projection data scanned with narrow collimation can be neglected, the difference signals between narrow and broad collimations can be used to estimate bowtie filter induced scatter signals for air scans with the correction of extra-focal radiations (EFRs). The calculated bowtie filter induced scatter signals have been compared with the results of Monte Carlo simulations, and the parameters of correction algorithm have been determined by fitting the measured scatter signal curves of phantom scans with calculated curves. Projection data have been reconstructed using Filtered BackProjection (FBP) method with and without bowtie filter correction to check whether the image quality is improved. Scatter signals can be well approximated with the bowtie filter scatter correction algorithm together with an existing object scatter correction algorithm. After removing the bowtie filter induced scatter signals, the dark bands in reconstructed images in the regions near the edges of scanned objects can be mostly eliminated. The difference signals of air scan data between narrow and broad collimations can be used to estimate the bowtie filter induced scatter for air scans. The proposed bowtie filter scatter correction algorithm using air scan data can be applied to estimate and to remove most of the bowtie filter induced scatter signals in object scans.