Simulation-driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans

BACKGROUND

Due to the high attenuation of metals, severe artifacts occur in cone beam computed tomography (CBCT). The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact reduction (MAR) algorithms.

PURPOSE

The occurrence of truncation caused by the limited detector size leads to the incomplete acquisition of metal masks from the threshold-based method in CBCT volume. Therefore, segmenting metal directly in CBCT projections is pursued in this work.

METHODS

Since the generation of high quality clinical training data is a constant challenge, this study proposes to generate simulated digital radiographs (data I) based on real CT data combined with self-designed computer aided design (CAD) implants. In addition to the simulated projections generated from 3D volumes, 2D x-ray images combined with projections of implants serve as the complementary data set (data II) to improve the network performance. In this work, SwinConvUNet consisting of shift window (Swin) vision transformers (ViTs) with patch merging as encoder is proposed for metal segmentation.

RESULTS

The model's performance is evaluated on accurately labeled test datasets obtained from cadaver scans as well as the unlabeled clinical projections. When trained on the data I only, the convolutional neural network (CNN) encoder-based networks UNet and TransUNet achieve only limited performance on the cadaver test data, with an average dice score of 0.821 and 0.850. After using both data II and data I during training, the average dice scores for the two models increase to 0.906 and 0.919, respectively. By replacing the CNN encoder with Swin transformer, the proposed SwinConvUNet reaches an average dice score of 0.933 for cadaver projections when only trained on the data I. Furthermore, SwinConvUNet has the largest average dice score of 0.953 for cadaver projections when trained on the combined data set.

CONCLUSIONS

Our experiments quantitatively demonstrate the effectiveness of the combination of the projections simulated under two pathways for network training. Besides, the proposed SwinConvUNet trained on the simulated projections performs state-of-the-art, robust metal segmentation as demonstrated on experiments on cadaver and clinical data sets. With the accurate segmentations from the proposed model, MAR can be conducted even for highly truncated CBCT scans.

Deep learning in computed tomography super resolution using multi-modality data training

BACKGROUND

One of the limitations in leveraging the potential of artificial intelligence in X-ray imaging is the limited availability of annotated training data. As X-ray and CT shares similar imaging physics, one could achieve cross-domain data sharing, so to generate labeled synthetic X-ray images from annotated CT volumes as digitally reconstructed radiographs (DRRs). To account for the lower resolution of CT and the CT-generated DRRs as compared to the real X-ray images, we propose the use of super-resolution (SR) techniques to enhance the CT resolution before DRR generation.

PURPOSE

As spatial resolution can be defined by the modulation transfer function kernel in CT physics, we propose to train a SR network using paired low-resolution (LR) and high-resolution (HR) images by varying the kernel's shape and cutoff frequency. This is different to previous deep learning-based SR techniques on RGB and medical images which focused on refining the sampling grid. Instead of generating LR images by bicubic interpolation, we aim to create realistic multi-detector CT (MDCT) like LR images from HR cone-beam CT (CBCT) scans.

METHODS

We propose and evaluate the use of a SR U-Net for the mapping between LR and HR CBCT image slices. We reconstructed paired LR and HR training volumes from the same CT scans with small in-plane sampling grid size of0.20 × 0.20 mm 2 $0.20 \times 0.20 \, {\rm mm}^2$ . We used the residual U-Net architecture to train two models. SRUNR e s K $^K_{Res}$ : trained with kernel-based LR images, and SRUNR e s I $^I_{Res}$ : trained with bicubic downsampled data as baseline. Both models are trained on one CBCT dataset (n = 13 391). The performance of both models was then evaluated on unseen kernel-based and interpolation-based LR CBCT images (n = 10 950), and also on MDCT images (n = 1392).

RESULTS

Five-fold cross validation and ablation study were performed to find the optimal hyperparameters. Both SRUNR e s K $^K_{Res}$ and SRUNR e s I $^I_{Res}$ models show significant improvements (p-value < $<$ 0.05) in mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index measures (SSIMs) on unseen CBCT images. Also, the improvement percentages in MAE, PSNR, and SSIM by SRUNR e s K $^K_{Res}$ is larger than SRUNR e s I $^I_{Res}$ . For SRUNR e s K $^K_{Res}$ , MAE is reduced by 14%, and PSNR and SSIMs increased by 6 and 8%, respectively. To conclude, SRUNR e s K $^K_{Res}$ outperforms SRUNR e s I $^I_{Res}$ , which the former generates sharper images when tested with kernel-based LR CBCT images as well as cross-modality LR MDCT data.

CONCLUSIONS

Our proposed method showed better performance than the baseline interpolation approach on unseen LR CBCT. We showed that the frequency behavior of the used data is important for learning the SR features. Additionally, we showed cross-modality resolution improvements to LR MDCT images. Our approach is, therefore, a first and essential step in enabling realistic high spatial resolution CT-generated DRRs for deep learning training.

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.

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications

BACKGROUND

Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations.

PURPOSE

To predict the scatter radiation signal in DBT projections within clinically-acceptable times and using only clinically-available data, such as compressed breast thickness and acquisition angle.

METHODS

MC simulations to obtain scatter estimates were generated from two types of digital breast phantoms. One set consisted of 600 realistically-shaped homogeneous breast phantoms for initial DL training. The other set was composed of 80 anthropomorphic phantoms, containing realistic internal tissue texture, aimed at fine tuning the DL model for clinical applications. The MC simulations generated scatter and primary maps per projection angle for a wide-angle DBT system. Both datasets were used to train (using 7680 projections from homogeneous phantoms), validate (using 960 and 192 projections from the homogeneous and anthropomorphic phantoms, respectively), and test (using 960 and 48 projections from the homogeneous and anthropomorphic phantoms, respectively) the DL model. The DL output was compared to the corresponding MC ground truth using both quantitative and qualitative metrics, such as mean relative and mean absolute relative differences (MRD and MARD), and to previously-published scatter-to-primary (SPR) ratios for similar breast phantoms. The scatter corrected DBT reconstructions were evaluated by analyzing the obtained linear attenuation values and by visual assessment of corrected projections in a clinical dataset. The time required for training and prediction per projection, as well as the time it takes to produce scatter-corrected projection images, were also tracked.

RESULTS

The quantitative comparison between DL scatter predictions and MC simulations showed a median MRD of 0.05% (interquartile range (IQR), -0.04% to 0.13%) and a median MARD of 1.32% (IQR, 0.98% to 1.85%) for homogeneous phantom projections and a median MRD of -0.21% (IQR, -0.35% to -0.07%) and a median MARD of 1.43% (IQR, 1.32% to 1.66%) for the anthropomorphic phantoms. The SPRs for different breast thicknesses and at different projection angles were within ± 15% of the previously-published ranges. The visual assessment showed good prediction capabilities of the DL model with a close match between MC and DL scatter estimates, as well as between DL-based scatter corrected and anti-scatter grid corrected cases. The scatter correction improved the accuracy of the reconstructed linear attenuation of adipose tissue, reducing the error from -16% and -11% to -2.3% and 4.4% for an anthropomorphic digital phantom and clinical case with similar breast thickness, respectively. The DL model training took 40 min and prediction of a single projection took less than 0.01 s. Generating scatter corrected images took 0.03 s per projection for clinical exams and 0.16 s for one entire projection set.

CONCLUSIONS

This DL-based method for estimating the scatter signal in DBT projections is fast and accurate, paving the way for future quantitative applications.

Towards a biomechanical breast model to simulate and investigate breast compression and its effects in mammography and tomosynthesis

OBJECTIVE

In mammography, breast compression forms an essential part of the examination and is achieved by lowering a compression paddle on the breast. Compression force is mainly used as parameter to estimate the degree of compression. As the force does not consider variations of breast size or tissue composition, over- and undercompression are a frequent result. This causes a highly varying perception of discomfort or even pain in the case of overcompression during the procedure. To develop a holistic, patient specific workflow, as a first step, breast compression needs to be thoroughly understood. The aim is to develop a biomechanical finite element breast model that accurately replicates breast compression in mammography and tomosynthesis and allows in-depth investigation. The current work focuses thereby, as a first step, to replicate especially the correct breast thickness under compression.&#xD;Approach: A dedicated method for acquiring ground truth data of uncompressed and compressed breasts within magnetic resonance (MR) imaging is introduced and transferred to the compression within x-ray mammography. Additionally, we created a simulation framework where individual breast models were generated based on MR images. &#xD;Main Results: By fitting the finite element model to the results of the ground truth images, a universal set of material parameters for fat and fibroglandular tissue could be determined. Overall, the breast models showed high agreement in compression thickness with a deviation of less than ten percent from the ground truth. &#xD;Significance: The introduced breast models show a huge potential for a better understanding of the breast compression process.

Clinical prototype implementation enabling an improved day-to-day mammography compression

PURPOSE

In mammography, breast compression is achieved by lowering a compression paddle on the breast. Despite the directive that compression is needed, there is no concrete guideline on its execution. To estimate the degree of compression, current mammography units only provide compression force and breast thickness as parameters. Therefore, radiographers could be induced to mainly determine the level of compression based on compression force and apply the same value to all breast sizes. In this case, smaller breast sizes are exposed to higher pressure. This results in a highly varying perception of discomfort or even pain during the procedure, depending on the breast size.

METHODS

To overcome this imbalance, current research results suggest that pressure might be a more qualified parameter for a more uniform compression among all breast sizes. To utilize pressure, the contact area between breast and compression paddle must be determined. In this paper, we present an easy-to-implement prototype enabling a real-time pressure-based measure without the need of direct patient contact. Using an optical camera, the contact area between the breast and the compression paddle is automatically segmented by a deep learning model.

RESULTS

The model provides a mean pixel accuracy of 96.7% (SD: 2.3%), mean frequency-weighted intersection over union of 88.5% (SD: 6.3%), and a Dice score of 93.6% (SD: 2.2%). The subsequent pressure display is updated more than five times per second which enables the use in clinical routines to set the compression level.

CONCLUSION

This prototype could help guiding to an improved breast compression routine in mammography procedures.

Generative compressed breast shape model for digital mammography and digital breast tomosynthesis

BACKGROUND

Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast-like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms.

PURPOSE

To develop a point distribution model of the breast shape obtained through principal component analysis (PCA) of structured light (SL) scans from patient compressed breasts.

METHODS

SL scans were acquired at our institution during routine craniocaudal-view DBT imaging of 236 patients, creating a dataset containing DBT and SL scans with matching information. Thereafter, the SL scans were cleaned, merged, simplified, and set to a regular grid across all cases. A comparison between the initial SL scans after cleaning and the gridded SL scans was performed to determine the absolute difference between them. The scans with points in a regular grid were then used for PCA. Additionally, the correspondence between SL scans and DBT scans was assessed by comparing features such as the chest-to-nipple distance (CND), the projected breast area (PBA) and the length along the chest-wall (LCW). These features were compared using a paired t-test or the Wilcoxon signed rank sum test. Thereafter, the PCA shape prediction and SL scans were evaluated by calculating the mean absolute error to determine whether the model had adequately captured the information in the dataset. The coefficients obtained from the PCA could then parameterize a given breast shape as an offset from the sample means. We also explored correlations of the PCA breast shape model parameters with certain patient characteristics: age, glandular volume, glandular density by mass, total breast volume, compressed breast thickness, compression force, nipple location, and centre of the chest-wall.

RESULTS

The median value across cases for the 90^th and 99^th percentiles of the interpolation error between the initial SL scans after cleaning and the gridded SL scans was 0.50 and 1.16 mm, respectively. The comparison between SL and DBT scans resulted in small, but statistically significant, mean differences of 1.6 mm, 1.6 mm, and 2.2 cm² for the LCW, CND, and PBA, respectively. The final model achieved a median mean absolute error of 0.68 mm compared to the scanned breast shapes and a perfect correlation between the first PCA coefficient and the patient breast compressed thickness, making it possible to use it to generate new model-based breast shapes with a specific breast thickness.

CONCLUSION

There is a good agreement between the breast shape coverage obtained with SL scans used to construct our model and the DBT projection images, and we could therefore create a generative model based on this data that is available for download on Github.

Dual-Energy Cone-Beam CT with Three-Material Decomposition for Bone Marrow Edema Imaging

We investigate the feasibility of bone marrow edema (BME) detection using a kV-switching Dual-Energy (DE) Cone-Beam CT (CBCT) protocol. This task is challenging due to unmatched x-ray paths in the low-energy (LE) and high-energy (HE) spectral channels, CBCT non-idealities such as x-ray scatter, and narrow spectral separation between fat (bone marrow) and water (BME). We propose a comprehensive DE decomposition framework consisting of projection interpolation onto matching LE and HE view angles, fast Monte Carlo scatter correction with low number of tracked photons and Gaussian denoising, and two-stage three-material decompositions involving two-material (fat-Aluminium) Projection-Domain Decomposition (PDD) followed by image-domain three-material (fat-water-bone) base-change. Performance in BME detection was evaluated in simulations and experiments emulating a kV-switching CBCT wrist imaging protocol on a robotic x-ray system with 60 kV LE beam, 120 kV HE beam, and 0.5° angular shift between the LE and HE views. Cubic B-spline interpolation was found to be adequate to resample HE and LE projections of a wrist onto common view angles required by PDD. The DE decomposition maintained acceptable BME detection specificity (<0.2 mL erroneously detected BME volume compared to 0.85 mL true BME volume) over +/-10% range of scatter magnitude errors, as long as the scatter shape was estimated without major distortions. Physical test bench experiments demonstrated successful discrimination of ~20% change in fat concentrations in trabecular bone-mimicking solutions of varying water and fat content.

Feasibility of Dual-Energy Cone-Beam CT of Bone Marrow Edema Using Dual-Layer Flat Panel Detectors

Purpose

We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector (FPD) and three-material decomposition.

Methods

A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The CBCT system featured 975 mm source-axis distance, 1,362 mm source-detector distance and a 430 × 430 mm2 dual-layer FPD (top layer: 0.20 mm CsI:Tl, bottom layer: 0.55 mm CsI:Tl; a 1 mm Cu filter between the layers to improve spectral separation). Tube settings were 120 kV (+2 mm Al, +0.2 mm Cu) and 10 mAs per exposure. The digital phantom consisted of a 160 mm water cylinder with inserts containing mixtures of water (volume fraction ranging 0.18 to 0.46) - fat (0.5 to 0.7) - Ca (0.04 to 0.12); decreasing fractions of fat indicated increasing degrees of BME. A two-stage three-material DE decomposition was applied to DE CBCT projections: first, projection-domain decomposition (PDD) into fat-aluminum basis, followed by CBCT reconstruction of intermediate base images, followed by image-domain change of basis into fat, water and bone. Sensitivity to scatter was evaluated by i) adjusting source collimation (12 to 400 mm width) and ii) subtracting various fractions of the true scatter from the projections at 400 mm collimation. The impact of spectral calibration was studied by shifting the effective beam energy (± 2 keV) when creating the PDD lookup table. We further simulated a realistic BME imaging framework, where the scatter was estimated using a fast Monte Carlo (MC) simulation from a preliminary decomposition of the object; the object was a realistic wrist phantom with an 0.85 mL BME stimulus in the radius.

Results

The decomposition is sensitive to scatter: approx. <20 mm collimation width or <10% error of scatter correction in a full field-of-view setting is needed to resolve BME. A mismatch in PDD decomposition calibration of ± 1 keV results in ~25% error in fat fraction estimates. In the wrist phantom study with MC scatter corrections, we were able to achieve ~0.79 mL true positive and ~0.06 mL false positive BME detection (compared to 0.85 mL true BME volume).

Conclusions

Detection of BME using DE CBCT with dual-layer FPD is feasible, but requires scatter mitigation, accurate scatter estimation, and robust spectral calibration.

The effect of tin prefiltration on extremity cone-beam CT imaging with a twin robotic X-ray system

INTRODUCTION

While tin prefiltration is established in various CT applications, its value in extremity cone-beam CT relative to optimized spectra has not been thoroughly assessed thus far. This study aims to investigate the effect of tin filters in extremity cone-beam CT with a twin-robotic X-ray system.

METHODS

Wrist, elbow and ankle joints of two cadaveric specimens were examined in a laboratory setup with different combinations of prefiltration (copper, tin), tube voltage and current-time product. Image quality was assessed subjectively by five radiologists with Fleiss' kappa being computed to measure interrater agreement. To provide a semiquantitative criterion for image quality, contrast-to-noise ratios (CNR) were compared for standardized regions of interest. Volume CT dose indices were calculated for a 16 cm polymethylmethacrylate phantom.

RESULTS

Radiation dose ranged from 17.4 mGy in the clinical standard protocol without tin filter to as low as 0.7 mGy with tin prefiltration. Image quality ratings and CNR for tin-filtered scans with 100 kV were lower than for 80 kV studies with copper prefiltration despite higher dose (11.2 and 5.6 vs. 4.5 mGy; p < 0.001). No difference was ascertained between 100 kV scans with tin filtration and 60 kV copper-filtered scans with 75% dose reduction (subjective: p = 0.101; CNR: p = 0.706). Fleiss' kappa of 0.597 (95% confidence interval 0.567-0.626; p < 0.001) indicated moderate interrater agreement.

CONCLUSION

Considerable dose reduction is feasible with tin prefiltration, however, the twin-robotic X-ray system's low-dose potential for extremity 3D imaging is maximized with a dedicated low-kilovolt scan protocol in situations without extensive beam-hardening artifacts.

IMPLICATIONS FOR PRACTICE

Low-kilovolt imaging with copper prefiltration provides a superior trade-off between dose reduction and image quality compared to tin-filtered cone-beam CT scan protocols with higher tube voltage.

Slot-scan dual-energy bone densitometry using motorized X-ray systems

PURPOSE

We investigate the feasibility of slot-scan dual-energy (DE) bone densitometry on motorized radiographic equipment. This approach will enable fast quantitative measurements of areal bone mineral density (aBMD) for opportunistic evaluation of osteoporosis.

METHODS

We investigated DE slot-scan protocols to obtain aBMD measurements at the lumbar spine (L-spine) and hip using a motorized x-ray platform capable of synchronized translation of the x-ray source and flat-panel detector (FPD). The slot dimension was 5 × 20 cm² . The DE slot views were processed as follows: (1) convolution kernel-based scatter correction, (2) unfiltered backprojection to tile the slots into long-length radiographs, and (3) projection-domain DE decomposition, consisting of an initial adipose-water decomposition in a bone-free region followed by water-CaHA decomposition with adjustment for adipose content. The accuracy and reproducibility of slot-scan aBMD measurements were investigated using a high-fidelity simulator of a robotic x-ray system (Siemens Multitom Rax) in a total of 48 body phantom realizations: four average bone density settings (cortical bone mass fraction: 10-40%), four body sizes (waist circumference, WC = 70-106 cm), and three lateral shifts of the body within the slot field of view (FOV) (centered and ±1 cm off-center). Experimental validations included: (1) x-ray test-bench feasibility study of adipose-water decomposition and (2) initial demonstration of slot-scan DE bone densitometry on the robotic x-ray system using the European Spine Phantom (ESP) with added attenuation (polymethyl methacrylate [PMMA] slabs) ranging 2 to 6 cm thick.

RESULTS

For the L-spine, the mean aBMD error across all WC settings ranged from 0.08 g/cm² for phantoms with average cortical bone fraction w_cortical  = 10% to ∼0.01 g/cm² for phantoms with w_cortical  = 40%. The L-spine aBMD measurements were fairly robust to changes in body size and positioning, e.g., coefficient of variation (CV) for L1 with w_cortical  = 30% was ∼0.034 for various WC and ∼0.02 for an obese patient (WC = 106 cm) changing lateral shift. For the hip, the mean aBMD error across all phantom configurations was about 0.07 g/cm² for a centered patient. The reproducibility of hip aBMD was slightly worse than in the L-spine (e.g., in the femoral neck, the CV with respect to changing WC was ∼0.13 for phantom realizations with w_cortical  = 30%) due to more challenging scatter estimation in the presence of an air-tissue interface within the slot FOV. The aBMD of the hip was therefore sensitive to lateral positioning of the patient, especially for obese patients: e.g., the CV with respect to patient lateral shift for femoral neck with WC = 106 cm and w_cortical  = 30% was 0.14. Empirical evaluations confirmed substantial reduction in aBMD errors with the proposed adipose estimation procedure and demonstrated robust aBMD measurements on the robotic x-ray system, with aBMD errors of ∼0.1 g/cm² across all three simulated ESP vertebrae and all added PMMA attenuator settings.

CONCLUSIONS

We demonstrated that accurate aBMD measurements can be obtained on a motorized FPD-based x-ray system using DE slot-scans with kernel-based scatter correction, backprojection-based slot view tiling, and DE decomposition with adipose correction.

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system. Fourteen breast screening radiologists interpreted 190 DBT examinations (90 normal, 26 with benign findings, and 74 with malignant findings), with the reference standard being verified by using histopathologic analysis or at least 1 year of follow-up. Reading was performed in two sessions, separated by at least 4 weeks, with a random mix of examinations being read with and without AI decision and navigation support. Forced Breast Imaging Reporting and Data System (categories 1-5) and level of suspicion (1-100) scores were given per breast by each reader. The area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity were compared between conditions by using the public-domain iMRMC software. The average reading times were compared by using the Wilcoxon signed rank test. Results The 190 women had a median age of 54 years (range, 48-63 years). The examination-based reader-averaged AUC was higher when interpreting results with AI support than when reading unaided (0.88 [95% CI: 0.84, 0.92] vs 0.85 [95% CI: 0.80, 0.89], respectively; P = .01). The average sensitivity increased with AI support (64 of 74, 86% [95% CI: 80%, 92%] vs 60 of 74, 81% [95% CI: 74%, 88%]; P = .006), whereas no differences in the specificity (85 of 116, 73.3% [95% CI: 65%, 81%] vs 83 of 116, 71.6% [95% CI: 65%, 78%]; P = .48) or reading time (48 seconds vs 45 seconds; P = .35) were detected. Conclusion Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program with higher performance, especially in terms of an increase in cancers detected, than using single-view DBT alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chan and Helvie in this issue.

A phantom study on dose efficiency for orthopedic applications: Comparing slot-scanning radiography using ultra-small-angle tomosynthesis to conventional radiography

PURPOSE

This paper studies the abilities of a twin-robotic x-ray slot-scanning system for orthopedic imaging to reduce dose by scatter rejection compared to conventional digital radiography.

METHODS

We investigate the dose saving capabilities, especially in terms of the signal- and the contrast-to-noise ratio, as well as the scatter-to-primary ratio of the proposed slot-scanning method in comparison to the state-of-the-art method for length-extended imaging. As a baseline, we use x-ray parameters of two clinically established acquisition protocols that provide the same detector entrance dose but are profoundly different in patient dose. To obtain an estimate of the photon-related noise directly from an x-ray image, we implement a Poisson-Gaussian noise model. This model is used to compare the dose efficiency of two settings and combined with the well-known K SNR to determine the transmission parameters. We present a method with an associated measurement protocol, utilizing the robotic capabilities of the used system to automatically obtain quasi-scatter-free ground-truth data with exact geometric correspondence to full-field and slot acquisitions. In total, we investigate two body regions (thoracic spine and lumbar spine) in anterior-posterior view with two patient sizes (BMI = 22 and 30) in two acquisition modes (conventional and slot scan with a flat-panel detector) with and without anti-scatter grid using an anthropomorphic upper-body phantom.

RESULTS

We have shown that it is feasible to combine the proposed approach with the K SNR for the determination of scatter rejection parameters. The use of an anti-scatter grid is indicated for full-field acquisitions allowing for dose savings up to 46% compared to their gridless counterparts. When changing the acquisition mode to the investigated slot scan, the use of an anti-scatter grid has no major impact on the image quality in terms of dose efficiency, in particular for patients with a BMI of 22. However, an increased contrast improvement factor was found. For normal-sized patients, up to 53% of dose can be saved additionally in comparison to full-field acquisitions with grid. Moreover, we could demonstrate that a slot size of 5 cm and air gap of 10 cm is sufficient to achieve scatter-to-primary ratios, which are equal or better compared to those of the full-field acquisitions with a grid.

CONCLUSIONS

We have shown, that the slot-scanning approach is always superior to the conventional full-field acquisition in terms of signal-to-noise and scatter-to-primary ratios. Compared to the state-of-the-art acquisition protocols with a grid, dose savings up to 53% are possible due to the scatter rejection without compromising the SNR. Hence, the use of the slot-scanning method is indicated, especially when it comes to regularly carried-out follow-up acquisitions, for example, in the case of scoliosis monitoring.

Cone-beam imaging with tilted rotation axis: Method and performance evaluation

PURPOSE

The recently introduced robotic x-ray systems provide the flexibility to acquire cone-beam computed tomography (CBCT) data using customized, application-specific source-detector trajectories. We exploit this capability to mitigate the effects of x-ray scatter and noise in CBCT imaging of weight-bearing foot and cervical spine (C-spine) using scan orbits with a tilted rotation axis.

METHODS

We used an advanced CBCT simulator implementing accurate models of x-ray scatter, primary attenuation, and noise to investigate the effects of the orbital tilt angle in upright foot and C-spine imaging. The system model was parameterized using a laboratory version of a three-dimensional (3D) robotic x-ray system (Multitom RAX, Siemens Healthineers). We considered a generalized tilted axis scan configuration, where the detector remained parallel to patient's long body axis during the acquisition, but the elevation of source and detector was changing. A modified Feldkamp-Davis-Kress (FDK) algorithm was developed for reconstruction in this configuration, which departs from the FDK assumption of a detector that is perpendicular to the scan plane. The simulated foot scans involved source-detector distance (SDD) of 1386 mm, orbital tilt angles ranging 10° to 40°, and 400 views at 1 mAs/view and 0.5° increment; the C-spine scans involved -25° to -45° tilt angles, SDD of 1090 mm, and 202 views at 1.3 mAs and 1° increment The imaging performance was assessed by projection-domain measurements of the scatter-to-primary ratio (SPR) and by reconstruction-domain measurements of contrast, noise and generalized contrast-to-noise ratio (gCNR, accounting for both image noise and background nonuniformity) of the metatarsals (foot imaging) and cervical vertebrae (spine imaging). The effects of scatter correction were also compared for horizontal and tilted scans using an ideal Monte Carlo (MC)-based scatter correction and a frame-by-frame mean scatter correction.

RESULTS

The proposed modified FDK, involving projection resampling, mitigated streak artifacts caused by the misalignment between the filtering direction and the detector rows. For foot imaging (no grids), an optimized 20° tilted orbit reduced the maximum SPR from ~1.5 in a horizontal scan to <0.5. The gCNR of the second metatarsal was enhanced twofold compared to a horizontal orbit. For the C-spine (with vertical grids), imaging with a tilted orbit avoided highly attenuating x-ray paths through the lower cervical vertebrae and shoulders. A -35° tilted orbit yielded improved image quality and visualization of the lower cervical spine: the SPR of lower cervical vertebrae was reduced from ~10 (horizontal orbit) to <6 (tilted orbit), and the gCNR for C5-C7 increased by a factor of 2. Furthermore, tilted orbits showed potential benefits over horizontal orbits by enabling scatter correction with a simple frame-by-frame mean correction without substantial increase in noise-induced artifacts after the correction.

CONCLUSIONS

Tilted scan trajectories, enabled by the emerging robotic x-ray system technology, were optimized for CBCT imaging of foot and cervical spine using an advanced simulation framework. The results demonstrated the potential advantages of tilted axis orbits in mitigation of scatter artifacts and improving contrast-to-noise ratio in CBCT reconstructions.

Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment

Assessment of breast density at the point of mammographic examination could lead to optimized breast cancer screening pathways. The onsite breast density information may offer guidance of when to recommend supplemental imaging for women in a screening program. A software application (Insight BD, Siemens Healthcare GmbH) for fast onsite quantification of volumetric breast density is evaluated. The accuracy of the method is assessed using breast tissue equivalent phantom experiments resulting in a mean absolute error of 3.84%. Reproducibility of measurement results is analyzed using 8427 exams in total, comparing for each exam (if available) the densities determined from left and right views, from cranio-caudal and medio-lateral oblique views, from full-field digital mammograms (FFDM) and digital breast tomosynthesis (DBT) data and from two subsequent exams of the same breast. Pearson correlation coefficients of 0.937, 0.926, 0.950, and 0.995 are obtained. Consistency of the results is demonstrated by evaluating the dependency of the breast density on women's age. Furthermore, the agreement between breast density categories computed by the software with those determined visually by 32 radiologists is shown by an overall percentage agreement of 69.5% for FFDM and by 64.6% for DBT data. These results demonstrate that the software delivers accurate, reproducible, and consistent measurements that agree well with the visual assessment of breast density by radiologists.

150-μm Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images

OBJECTIVE

The aims of this study were to quantitatively assess two new scan modes on a photon-counting detector computed tomography system, each designed to maximize spatial resolution, and to qualitatively demonstrate potential clinical impact using patient data.

MATERIALS AND METHODS

This Health Insurance Portability Act-compliant study was approved by our institutional review board. Two high-spatial-resolution scan modes (Sharp and UHR) were evaluated using phantoms to quantify spatial resolution and image noise, and results were compared with the standard mode (Macro). Patients were scanned using a conventional energy-integrating detector scanner and the photon-counting detector scanner using the same radiation dose. In first patient images, anatomic details were qualitatively evaluated to demonstrate potential clinical impact.

RESULTS

Sharp and UHR modes had a 69% and 87% improvement in in-plane spatial resolution, respectively, compared with Macro mode (10% modulation-translation-function values of 16.05, 17.69, and 9.48 lp/cm, respectively). The cutoff spatial frequency of the UHR mode (32.4 lp/cm) corresponded to a limiting spatial resolution of 150 μm. The full-width-at-half-maximum values of the section sensitivity profiles were 0.41, 0.44, and 0.67 mm for the thinnest image thickness for each mode (0.25, 0.25, and 0.5 mm, respectively). At the same in-plane spatial resolution, Sharp and UHR images had up to 15% lower noise than Macro images. Patient images acquired in Sharp mode demonstrated better delineation of fine anatomic structures compared with Macro mode images.

CONCLUSIONS

Phantom studies demonstrated superior resolution and noise properties for the Sharp and UHR modes relative to the standard Macro mode and patient images demonstrated the potential benefit of these scan modes for clinical practice.

Quarter-millimeter spectral coronary stent imaging with photon-counting CT: Initial experience

PURPOSE

To evaluate the performance and clinical feasibility of 0.25 mm resolution mode of a dual-energy photon-counting detector (PCD) computed tomography (CT) system for coronary stent imaging and to compare the results to state-of-the-art dual-energy energy-integrating detector (EID) CT.

MATERIALS AND METHODS

Coronary stents with different diameters (2.0-4.0 mm) were examined inside a coronary artery phantom consisting of plastic tubes filled with iodine-based and gadolinium-based contrast material diluted to approximate clinical concentrations (n = 18). EID images were acquired using 2nd and 3rd generation dual-source CT systems (SOMATOM Flash and SOMATOM Force, Siemens Healthcare) at 0.60 mm (defined as standard-resolution (SR)) isotropic voxel size. Radiation-dose matched PCD images were acquired using a human prototype PCD system (Siemens Healthcare) at 0.50 mm (SR) and 0.25 mm (HR) imaging modes. Images were reconstructed using optimized convolution kernels.

RESULTS

Dual-energy HR PCD images significantly better stent lumen visualization (median: 69.5%, IQR: 61.2-78.9%) over dual-energy EID, and standard-resolution PCD images (median: 53.2-57.4%, all P < 0.01). HR PCD acquisitions reconstructed at SR image voxel size showed 25.3% lower image noise compared to SR PCD acquisitions (P < 0.001). High-resolution iodine and gadolinium maps, as well as virtual monoenergetic images, were calculated from the PCD data and enabled estimation of contrast agent concentration in the lumen without interference from the coronary stent.

CONCLUSION

HR spectral PCD imaging significantly improves coronary stent lumen visibility over dual-energy EID. When the PCD-HR data was reconstructed into standard voxel sizes (0.5 mm isotropic) the image noise decreased by 25% compared to SR acquisition of PCD. Both dual-energy systems were consistent in estimating contrast agent concentrations.

Spatio-energetic cross-talk in photon counting detectors: N × N binning and sub-pixel masking

PURPOSE

Smaller pixel sizes of x-ray photon counting detectors (PCDs) benefit count rate capabilities but increase cross-talk and "double-counting" between neighboring PCD pixels. When an x-ray photon produces multiple (n) counts at neighboring (sub-)pixels and they are added during post-acquisition N × N binning process, the variance of the final PCD output-pixel will be larger than its mean. In the meantime, anti-scatter grids are placed at the pixel boundaries in most of x-ray CT systems and will decrease cross-talk between sub-pixels because the grids mask sub-pixels underneath them, block the primary x-rays, and increase the separation distance between active sub-pixels. The aim of this paper was, first, to study the PCD statistics with various N × N binning schemes and three different masking methods in the presence of cross-talks, and second, to assess one of the most fundamental performances of x-ray CT: soft tissue contrast visibility.

METHODS

We used a PCD cross-talk model (Photon counting toolkit, PcTK) and produced cross-talk data between 3 × 3 neighboring sub-pixels and calculated the mean, variance, and covariance of output-pixels with each of N × N binning scheme [4 × 4 binning, 2 × 2 binning, and 1 × 1 binning (i.e., no binning)] and three different sub-pixel masking methods (no mask, 1-D mask, and 2-D mask). We then set up simulation to evaluate the soft tissue contrast visibility. X-rays of 120 kVp were attenuated by 10-40 cm-thick water, with the right side of PCDs having 0.5 cm thicker water than the left side. A pair of output-pixels across the left-right boundary were used to assess the sensitivity index (SI or d'), which typically ranges 0-1 and is a generalized signal-to-noise ratio and a statistics used in signal detection theory.

RESULTS

Binning a larger number of sub-pixels resulted in larger mean counts and larger variance-to-mean ratio when the lower threshold of the energy window was lower than the half of the incident energy. Mean counts are in the order of no mask (the largest), 1-D mask, and 2-D mask but the difference in variance-to-mean ratio was small. For a given sub-pixel size and masking method, binning more sub-pixels degraded the normalized SI values but the difference between 4 × 4 binning and 1 × 1 binning was typically less than 0.06. 1-D mask provided better normalized SI values than no mask and 2-D mask for side-by-side case and the improvements were larger with fewer binnings, although the difference was less than 0.10. 2-D mask was the best for embedded case. The normalized SI values of combined binning, sub-pixel size, and masking were in the order of 1 × 1 (900 μm)² binning, 2 × 2 (450 μm)² binning, and 4 × 4 (225 μm)² binning for a given masking method but the difference between each of them were typically 0.02-0.05.

CONCLUSION

We have evaluated the effect of double-counting between PCD sub-pixels with various binning and masking methods. SI values were better with fewer number of binning and larger sub-pixels. The difference among various binning and masking methods, however, was typically less than 0.06, which might result in a dose penalty of 13% if the CT system were linear.

Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon-counting detector computed tomography system

An ultra-high resolution (UHR) mode, with a detector pixel size of [Formula: see text] relative to isocenter, has been implemented on a whole body research photon-counting detector (PCD) computed tomography (CT) system. Twenty synthetic lung nodules were scanned using UHR and conventional resolution (macro) modes and reconstructed with medium and very sharp kernels. Linear regression was used to compare measured nodule volumes from CT images to reference volumes. The full-width-at-half-maximum of the calculated curvature histogram for each nodule was used as a shape index, and receiver operating characteristic analysis was performed to differentiate sphere- and star-shaped nodules. Results showed a strong linear relationship between measured nodule volumes and reference volumes for both modes. The overall volume estimation was more accurate using UHR mode and the very sharp kernel, having 4.8% error compared with 10.5% to 12.6% error in the macro mode. The improvement in volume measurements using the UHR mode was more evident for small nodule sizes or star-shaped nodules. Images from the UHR mode with the very sharp kernel consistently demonstrated the best performance [[Formula: see text]] for separating star- from sphere-shaped nodules, showing advantages of UHR mode on a PCD CT scanner for lung nodule characterization.

Estimation of Basis Line-Integrals in a Spectral Distortion-Modeled Photon Counting Detector Using Low-Rank Approximation-Based X-Ray Transmittance Modeling: K-Edge Imaging Application

Photon counting detectors (PCDs) provide multiple energy-dependent measurements for estimating basis line-integrals. However, the measured spectrum is distorted from the spectral response effect (SRE) via charge sharing, K-fluorescence emission, and so on. Thus, in order to avoid bias and artifacts in images, the SRE needs to be compensated. For this purpose, we recently developed a computationally efficient three-step algorithm for PCD-CT without contrast agents by approximating smooth X-ray transmittance using low-order polynomial bases. It compensated the SRE by incorporating the SRE model in a linearized estimation process and achieved nearly the minimum variance and unbiased (MVU) estimator. In this paper, we extend the three-step algorithm to K-edge imaging applications by designing optimal bases using a low-rank approximation to model X-ray transmittances with arbitrary shapes (i.e., smooth without the K-edge or discontinuous with the K-edge). The bases can be used to approximate the X-ray transmittance and to linearize the PCD measurement modeling and then the three-step estimator can be derived as in the previous approach: estimating the x-ray transmittance in the first step, estimating basis line-integrals including that of the contrast agent in the second step, and correcting for a bias in the third step. We demonstrate that the proposed method is more accurate and stable than the low-order polynomial-based approaches with extensive simulation studies using gadolinium for the K-edge imaging application. We also demonstrate that the proposed method achieves nearly MVU estimator, and is more stable than the conventional maximum likelihood estimator in high attenuation cases with fewer photon counts.

Photon-Counting CT of the Brain: In Vivo Human Results and Image-Quality Assessment

BACKGROUND AND PURPOSE

Photon-counting detectors offer the potential for improved image quality for brain CT but have not yet been evaluated in vivo. The purpose of this study was to compare photon-counting detector CT with conventional energy-integrating detector CT for human brains.

MATERIALS AND METHODS

Radiation dose-matched energy-integrating detector and photon-counting detector head CT scans were acquired with standardized protocols (tube voltage/current, 120 kV(peak)/370 mAs) in both an anthropomorphic head phantom and 21 human asymptomatic volunteers (mean age, 58.9 ± 8.5 years). Photon-counting detector thresholds were 22 and 52 keV (low-energy bin, 22-52 keV; high-energy bin, 52-120 keV). Image noise, gray matter, and white matter signal-to-noise ratios and GM-WM contrast and contrast-to-noise ratios were measured. Image quality was scored by 2 neuroradiologists blinded to the CT detector type. Reproducibility was assessed with the intraclass correlation coefficient. Energy-integrating detector and photon-counting detector CT images were compared using a paired t test and the Wilcoxon signed rank test.

RESULTS

Photon-counting detector CT images received higher reader scores for GM-WM differentiation with lower image noise (all P < .001). Intrareader and interreader reproducibility was excellent (intraclass correlation coefficient, ≥0.86 and 0.79, respectively). Quantitative analysis showed 12.8%-20.6% less image noise for photon-counting detector CT. The SNR of photon-counting detector CT was 19.0%-20.0% higher than of energy-integrating detector CT for GM and WM. The contrast-to-noise ratio of photon-counting detector CT was 15.7% higher for GM-WM contrast and 33.3% higher for GM-WM contrast-to-noise ratio.

CONCLUSIONS

Photon-counting detector brain CT scans demonstrated greater gray-white matter contrast compared with conventional CT. This was due to both higher soft-tissue contrast and lower image noise for photon-counting CT.

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with five known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology (NIST). Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

Feasibility of Dose-reduced Chest CT with Photon-counting Detectors: Initial Results in Humans

Purpose To investigate whether photon-counting detector (PCD) technology can improve dose-reduced chest computed tomography (CT) image quality compared with that attained with conventional energy-integrating detector (EID) technology in vivo. Materials and Methods This was a HIPAA-compliant institutional review board-approved study, with informed consent from patients. Dose-reduced spiral unenhanced lung EID and PCD CT examinations were performed in 30 asymptomatic volunteers in accordance with manufacturer-recommended guidelines for CT lung cancer screening (120-kVp tube voltage, 20-mAs reference tube current-time product for both detectors). Quantitative analysis of images included measurement of mean attenuation, noise power spectrum (NPS), and lung nodule contrast-to-noise ratio (CNR). Images were qualitatively analyzed by three radiologists blinded to detector type. Reproducibility was assessed with the intraclass correlation coefficient (ICC). McNemar, paired t, and Wilcoxon signed-rank tests were used to compare image quality. Results Thirty study subjects were evaluated (mean age, 55.0 years ± 8.7 [standard deviation]; 14 men). Of these patients, 10 had a normal body mass index (BMI) (BMI range, 18.5-24.9 kg/m²; group 1), 10 were overweight (BMI range, 25.0-29.9 kg/m²; group 2), and 10 were obese (BMI ≥30.0 kg/m², group 3). PCD diagnostic quality was higher than EID diagnostic quality (P = .016, P = .016, and P = .013 for readers 1, 2, and 3, respectively), with significantly better NPS and image quality scores for lung, soft tissue, and bone and with fewer beam-hardening artifacts (all P < .001). Image noise was significantly lower for PCD images in all BMI groups (P < .001 for groups 1 and 3, P < .01 for group 2), with higher CNR for lung nodule detection (12.1 ± 1.7 vs 10.0 ± 1.8, P < .001). Inter- and intrareader reproducibility were good (all ICC > 0.800). Conclusion Initial human experience with dose-reduced PCD chest CT demonstrated lower image noise compared with conventional EID CT, with better diagnostic quality and lung nodule CNR. ^© RSNA, 2017 Online supplemental material is available for this article.

Estimation of signal and noise for a whole-body research photon-counting CT system

Photon-counting detector CT has a large number of acquisition parameters that require optimization, particularly the energy threshold configurations. Fast and accurate estimation of both signal and noise in photon-counting CT (PCCT) images can facilitate such optimization. Using the detector response function of a research PCCT system, we derived mathematical models for both signal and noise estimation, taking into account beam spectrum and filtration, object attenuation, water beam hardening, detector response, radiation dose, energy thresholds, and the propagation of noise. To determine the absolute noise value, a noise lookup table (LUT) for all available energy thresholds was acquired using a number of calibration scans. The noise estimation algorithm then used the noise LUT to estimate noise for scans with a variety of combination of energy thresholds, dose levels, and object attenuations. Validation of the estimation algorithms was performed on a whole-body research PCCT system using semianthropomorphic water phantoms and solutions of calcium and iodine. Clinical feasibility of noise estimation was assessed with scans of a cadaver head and a living swine. The algorithms achieved accurate estimation of both signal and noise for a variety of scanning parameter combinations. Maximum discrepancies were below 15%, while most errors were below 5%.

Spatio-energetic cross talk in photon counting detectors: Detector model and correlated Poisson data generator

PURPOSE

An x-ray photon interacts with photon counting detectors (PCDs) and generates an electron charge cloud or multiple clouds. The clouds (thus, the photon energy) may be split between two adjacent PCD pixels when the interaction occurs near pixel boundaries, producing a count at both of the pixels. This is called double-counting with charge sharing. (A photoelectric effect with K-shell fluorescence x-ray emission would result in double-counting as well). As a result, PCD data are spatially and energetically correlated, although the output of individual PCD pixels is Poisson distributed. Major problems include the lack of a detector noise model for the spatio-energetic cross talk and lack of a computationally efficient simulation tool for generating correlated Poisson data. A Monte Carlo (MC) simulation can accurately simulate these phenomena and produce noisy data; however, it is not computationally efficient.

METHODS

In this study, the authors developed a new detector model and implemented it in an efficient software simulator that uses a Poisson random number generator to produce correlated noisy integer counts. The detector model takes the following effects into account: (1) detection efficiency; (2) incomplete charge collection and ballistic effect; (3) interaction with PCDs via photoelectric effect (with or without K-shell fluorescence x-ray emission, which may escape from the PCDs or be reabsorbed); and (4) electronic noise. The correlation was modeled by using these two simplifying assumptions: energy conservation and mutual exclusiveness. The mutual exclusiveness is that no more than two pixels measure energy from one photon. The effect of model parameters has been studied and results were compared with MC simulations. The agreement, with respect to the spectrum, was evaluated using the reduced χ² statistics or a weighted sum of squared errors, χ_red²(≥1), where χ_red²=1 indicates a perfect fit.

RESULTS

The model produced spectra with flat field irradiation that qualitatively agree with previous studies. The spectra generated with different model and geometry parameters allowed for understanding the effect of the parameters on the spectrum and the correlation of data. The agreement between the model and MC data was very strong. The mean spectra with 90 keV and 140 kVp agreed exceptionally well: χ_red² values were 1.049 with 90 keV data and 1.007 with 140 kVp data. The degrees of cross talk (in terms of the relative increase from single pixel irradiation to flat field irradiation) were 22% with 90 keV and 19% with 140 kVp for MC simulations, while they were 21% and 17%, respectively, for the model. The covariance was in strong agreement qualitatively, although it was overestimated. The noisy data generation was very efficient, taking less than a CPU minute as opposed to CPU hours for MC simulators.

CONCLUSIONS

The authors have developed a novel, computationally efficient PCD model that takes into account double-counting and resulting spatio-energetic correlation between PCD pixels. The MC simulation validated the accuracy.

Ultra-High Spatial Resolution, Multi-Energy CT using Photon Counting Detector Technology

Two ultra-high-resolution (UHR) imaging modes, each with two energy thresholds, were implemented on a research, whole-body photon-counting-detector (PCD) CT scanner, referred to as sharp and UHR, respectively. The UHR mode has a pixel size of 0.25 mm at iso-center for both energy thresholds, with a collimation of 32 × 0.25 mm. The sharp mode has a 0.25 mm pixel for the low-energy threshold and 0.5 mm for the high-energy threshold, with a collimation of 48 × 0.25 mm. Kidney stones with mixed mineral composition and lung nodules with different shapes were scanned using both modes, and with the standard imaging mode, referred to as macro mode (0.5 mm pixel and 32 × 0.5 mm collimation). Evaluation and comparison of the three modes focused on the ability to accurately delineate anatomic structures using the high-spatial resolution capability and the ability to quantify stone composition using the multi-energy capability. The low-energy threshold images of the sharp and UHR modes showed better shape and texture information due to the achieved higher spatial resolution, although noise was also higher. No noticeable benefit was shown in multi-energy analysis using UHR compared to standard resolution (macro mode) when standard doses were used. This was due to excessive noise in the higher resolution images. However, UHR scans at higher dose showed improvement in multi-energy analysis over macro mode with regular dose. To fully take advantage of the higher spatial resolution in multi-energy analysis, either increased radiation dose, or application of noise reduction techniques, is needed.

Dual Energy CT Kidney Stone Differentiation in Photon Counting Computed Tomography

This study evaluates the capabilities of a whole-body photon counting CT system to differentiate between four common kidney stone materials, namely uric acid (UA), calcium oxalate monohydrate (COM), cystine (CYS),and apatite (APA) ex vivo. Two different x-ray spectra (120 kV and 140 kV) were applied and two acquisition modes were investigated; The macro-mode generates two energy threshold based image-volumes and two energy bin based image-volumes. In the chesspattern-mode, however, four energy thresholds are applied. A virtual low energy image, as well as a virtual high energy image are derived from initial threshold-based images, while considering their statistically correlated nature. The energy bin based images of the macro-mode, as well as the virtual low and high energy image of the chesspattern-mode serve as input for our dual energy evaluation. The dual energy ratio of the individually segmented kidney stones were utilized to quantify the discriminability of the different materials. The dual energy ratios of the two acquisition modes showed high correlation for both applied spectra. Wilcoxon-rank sum tests and the evaluation of the area under the receiver operating characteristics curves suggest that the UA kidney stones are best differentiable from all other materials (AUC = 1.0), followed by CYS (AUC ≈ 0.9 compared against COM and APA). COM and APA, however, are hardly distinguishable (AUC between 0.63 and 0.76). The results hold true for the measurements of both spectra and both acquisition modes.

Renal Stone Characterization using High Resolution Imaging Mode on a Photon Counting Detector CT System

In addition to the standard-resolution (SR) acquisition mode, a high-resolution (HR) mode is available on a research photon-counting-detector (PCD) whole-body CT system. In the HR mode each detector consists of a 2x2 array of 0.225 mm × 0.225 mm subpixel elements. This is in contrast to the SR mode that consists of a 4x4 array of the same sub-elements, and results in 0.25 mm isotropic resolution at iso-center for the HR mode. In this study, we quantified ex vivo the capabilities of the HR mode to characterize renal stones in terms of morphology and mineral composition. Forty pure stones - 10 uric acid (UA), 10 cystine (CYS), 10 calcium oxalate monohydrate (COM) and 10 apatite (APA) - and 14 mixed stones were placed in a 20 cm water phantom and scanned in HR mode, at radiation dose matched to that of routine dual-energy stone exams. Data from micro CT provided a reference for the quantification of morphology and mineral composition of the mixed stones. The area under the ROC curve was 1.0 for discriminating UA from CYS, 0.89 for CYS vs COM and 0.84 for COM vs APA. The root mean square error (RMSE) of the percent UA in mixed stones was 11.0% with a medium-sharp kernel and 15.6% with the sharpest kernel. The HR showed qualitatively accurate characterization of stone morphology relative to micro CT.

Lung Nodule Volume Quantification and Shape Differentiation with an Ultra-High Resolution Technique on a Photon Counting Detector CT System

A new ultra high-resolution (UHR) mode has been implemented on a whole body photon counting-detector (PCD) CT system. The UHR mode has a pixel size of 0.25 mm by 0.25 mm at the iso-center, while the conventional (macro) mode is limited to 0.5 mm by 0.5 mm. A set of synthetic lung nodules (two shapes, five sizes, and two radio-densities) was scanned using both the UHR and macro modes and reconstructed with 2 reconstruction kernels (4 sets of images in total). Linear regression analysis was performed to compare measured nodule volumes from CT images to reference volumes. Surface curvature was calculated for each nodule and the full width half maximum (FWHM) of the curvature histogram was used as a shape index to differentiate sphere and star shape nodules. Receiver operating characteristic (ROC) analysis was performed and area under the ROC curve (AUC) was used as a figure of merit for the differentiation task. Results showed strong linear relationship between measured nodule volume and reference standard for both UHR and macro mode. For all nodules, volume estimation was more accurate using UHR mode with sharp kernel (S80f), with lower mean absolute percent error (MAPE) (6.5%) compared with macro mode (11.1% to 12.9%). The improvement of volume measurement from UHR mode was more evident particularly for small nodule size (3mm, 5mm), or star-shape nodules. Images from UHR mode with sharp kernel (S80f) consistently demonstrated the best performance (AUC = 0.85) when separating star from sphere shape nodules among all acquisition and reconstruction modes. Our results showed the advantages of UHR mode on a PCD CT scanner in lung nodule characterization. Various clinical applications, including quantitative imaging, can benefit substantially from this high resolution mode.

Estimation of Basis Line-Integrals in a Spectral Distortion-Modeled Photon Counting Detector Using Low-Order Polynomial Approximation of X-ray Transmittance

Photon counting detector (PCD)-based computed tomography exploits spectral information from a transmitted x-ray spectrum to estimate basis line-integrals. The recorded spectrum, however, is distorted and deviates from the transmitted spectrum due to spectral response effect (SRE). Therefore, the SRE needs to be compensated for when estimating basis line-integrals. One approach is to incorporate the SRE model with an incident spectrum into the PCD measurement model and the other approach is to perform a calibration process that inherently includes both the SRE and the incident spectrum. A maximum likelihood estimator can be used to the former approach, which guarantees asymptotic optimality; however, a heavy computational burden is a concern. Calibration-based estimators are a form of the latter approach. They can be very efficient; however, a heuristic calibration process needs to be addressed. In this paper, we propose a computationally efficient three-step estimator for the former approach using a low-order polynomial approximation of x-ray transmittance. The low-order polynomial approximation can change the original non-linear estimation method to a two-step linearized approach followed by an iterative bias correction step. We show that the calibration process is required only for the bias correction step and prove that it converges to the unbiased solution under practical assumptions. Extensive simulation studies validate the proposed method and show that the estimation results are comparable to those of the ML estimator while the computational time is reduced substantially.

Dose-efficient ultrahigh-resolution scan mode using a photon counting detector computed tomography system

An ultrahigh-resolution (UHR) data collection mode was enabled on a whole-body, research photon counting detector (PCD) computed tomography system. In this mode, 64 rows of [Formula: see text] detector pixels were used, which corresponded to a pixel size of [Formula: see text] at the isocenter. Spatial resolution and image noise were quantitatively assessed for the UHR PCD scan mode, as well as for a commercially available UHR scan mode that uses an energy-integrating detector (EID) and a set of comb filters to decrease the effective detector size. Images of an anthropomorphic lung phantom, cadaveric swine lung, swine heart specimen, and cadaveric human temporal bone were qualitatively assessed. Nearly equivalent spatial resolution was demonstrated by the modulation transfer function measurements: 15.3 and [Formula: see text] spatial frequencies were achieved at 10% and 2% modulation, respectively, for the PCD system and 14.2 and [Formula: see text] for the EID system. Noise was 29% lower in the PCD UHR images compared to the EID UHR images, representing a potential dose savings of 50% for equivalent image noise. PCD UHR images from the anthropomorphic phantom and cadaveric specimens showed clear delineation of small structures.

Low-dose lung cancer screening with photon-counting CT: a feasibility study

To evaluate the feasibility of using a whole-body photon-counting detector (PCD) CT scanner for low-dose lung cancer screening compared to a conventional energy integrating detector (EID) system. Radiation dose-matched EID and PCD scans of the COPDGene 2 phantom were acquired at different radiation dose levels (CTDI_vol: 3.0, 1.5, and 0.75 mGy) and different tube voltages (120, 100, and 80 kVp). EID and PCD images were compared for quantitative Hounsfield unit (HU) accuracy, noise levels, and contrast-to-noise ratios (CNR) for detection of ground-glass nodules (GGN) and emphysema. The PCD HU accuracy was better than EID for water at all scan parameters. PCD HU stability for lung, GGN and emphysema regions were superior to EID and PCD attenuation values were more reproducible than EID for all scan parameters (all P  <  0.01), while HUs for lung, GGN and emphysema ROIs changed significantly for EID with decreasing dose (all P  <  0.001). PCD showed lower noise levels at the lowest dose setting at 120, 100 and 80 kVp (15.2  ±  0.3 HU versus 15.8  ±  0.2 HU, P  =  0.03; 16.1  ±  0.3 HU versus 18.0  ±  0.4 HU, P  =  0.003; and 16.1  ±  0.3 HU versus 17.9  ±  0.3 HU, P  =  0.001, respectively), resulting in superior CNR for evaluation of GGNs and emphysema at 100 and 80 kVp. PCD provided better HU stability for lung, ground-glass, and emphysema-equivalent foams at lower radiation dose settings with better reproducibility than EID. Additionally, PCD showed up to 10% less noise, and 11% higher CNR at 0.75 mGy for both 100 and 80 kVp. PCD technology may help reduce radiation exposure in lung cancer screening while maintaining diagnostic quality.

Noise performance of low-dose CT: comparison between an energy integrating detector and a photon counting detector using a whole-body research photon counting CT scanner

Photon counting detector (PCD)-based computed tomography (CT) is an emerging imaging technique. Compared to conventional energy integrating detector (EID)-based CT, PCD-CT is able to exclude electronic noise that may severely impair image quality at low photon counts. This work focused on comparing the noise performance at low doses between the PCD and EID subsystems of a whole-body research PCD-CT scanner, both qualitatively and quantitatively. An anthropomorphic thorax phantom was scanned, and images of the shoulder portion were reconstructed. The images were visually and quantitatively compared between the two subsystems in terms of streak artifacts, an indicator of the impact of electronic noise. Furthermore, a torso-shaped water phantom was scanned using a range of tube currents. The product of the noise and the square root of the tube current was calculated, normalized, and compared between the EID and PCD subsystems. Visual assessment of the thorax phantom showed that electronic noise had a noticeably stronger degrading impact in the EID images than in the PCD images. The quantitative results indicated that in low-dose situations, electronic noise had a noticeable impact (up to a 5.8% increase in magnitude relative to quantum noise) on the EID images, but negligible impact on the PCD images.

Evaluation of models of spectral distortions in photon-counting detectors for computed tomography

A semi-analytical model describing spectral distortions in photon-counting detectors (PCDs) for clinical computed tomography was evaluated using simulated data. The distortions were due to count rate-independent spectral response effects and count rate-dependent pulse-pileup effects and the model predicted both the mean count rates and the spectral shape. The model parameters were calculated using calibration data. The model was evaluated by comparing the predicted x-ray spectra to Monte Carlo simulations of a PCD at various count rates. The data-model agreement expressed as weighted coefficient of variation [Formula: see text] was better than [Formula: see text] for dead time losses up to 28% and [Formula: see text] or smaller for dead time losses up to 69%. The accuracy of the model was also tested for the purpose of material decomposition by estimating material thicknesses from simulated projection data. The estimated attenuator thicknesses generally agreed with the true values within one standard deviation of the statistical uncertainty obtained from multiple noise realizations.

A High-Resolution Imaging Technique using a Whole-body, Research Photon Counting Detector CT System

A high-resolution (HR) data collection mode has been introduced to the whole-body, research photon-counting-detector CT system installed in our laboratory. In this mode, 64 rows of 0.45 mm × 0.45 mm detectors pixels were used, which corresponded to a pixel size of 0.225 mm × 0.225 mm at the iso-center. Spatial resolution of this HR mode was quantified by measuring the MTF from a scan of a 50 micron wire phantom. An anthropomorphic lung phantom, cadaveric swine lung, temporal bone and heart specimens were scanned using the HR mode, and image quality was subjectively assessed by two experienced radiologists. Comparison of the HR mode images against their energy integrating system (EID) equivalents using comb filters was also performed. High spatial resolution of the HR mode was evidenced by the MTF measurement, with 15 lp/cm and 20 lp/cm at 10% and 2% MTF. Images from anthropomorphic phantom and cadaveric specimens showed clear delineation of small structures, such as lung vessels, lung nodules, temporal bone structures, and coronary arteries. Temporal bone images showed critical anatomy (i.e. stapes superstructure) that was clearly visible in the PCD system but hardly visible with the EID system. These results demonstrated the potential application of this imaging mode in lung, temporal bone, and vascular imaging. Other clinical applications that require high spatial resolution, such as musculoskeletal imaging, may also benefit from this high resolution mode.

Evaluation of conventional imaging performance in a research whole-body CT system with a photon-counting detector array

This study evaluated the conventional imaging performance of a research whole-body photon-counting CT system and investigated its feasibility for imaging using clinically realistic levels of x-ray photon flux. This research system was built on the platform of a 2nd generation dual-source CT system: one source coupled to an energy integrating detector (EID) and the other coupled to a photon-counting detector (PCD). Phantom studies were conducted to measure CT number accuracy and uniformity for water, CT number energy dependency for high-Z materials, spatial resolution, noise, and contrast-to-noise ratio. The results from the EID and PCD subsystems were compared. The impact of high photon flux, such as pulse pile-up, was assessed by studying the noise-to-tube-current relationship using a neonate water phantom and high x-ray photon flux. Finally, clinical feasibility of the PCD subsystem was investigated using anthropomorphic phantoms, a cadaveric head, and a whole-body cadaver, which were scanned at dose levels equivalent to or higher than those used clinically. Phantom measurements demonstrated that the PCD subsystem provided comparable image quality to the EID subsystem, except that the PCD subsystem provided slightly better longitudinal spatial resolution and about 25% improvement in contrast-to-noise ratio for iodine. The impact of high photon flux was found to be negligible for the PCD subsystem: only subtle high-flux effects were noticed for tube currents higher than 300 mA in images of the neonate water phantom. Results of the anthropomorphic phantom and cadaver scans demonstrated comparable image quality between the EID and PCD subsystems. There were no noticeable ring, streaking, or cupping/capping artifacts in the PCD images. In addition, the PCD subsystem provided spectral information. Our experiments demonstrated that the research whole-body photon-counting CT system is capable of providing clinical image quality at clinically realistic levels of x-ray photon flux.

Analytic noise-propagation in indirect fan-beam FBP reconstruction

Precise knowledge of the local image noise is an essential ingredient to efficient application of post-processing methods such as wavelet or diffusion filtering to computed tomography (CT) images. The non-stationary, object dependent nature of noise in CT images is a direct result from the noise present in the projection data. Since quantum and electronics noise are the dominating noise sources, comparably simple models can be used for direct noise estimates in the individual projections. In this article, we describe the analytic propagation of these noise estimates through fan-beam filtered backprojection (FBP) reconstruction. Contrary to earlier publications in this field, we include the correlations within the parallel projections resulting from the rebinning, the convolution, and the backprojection processes. The method has been validated against Monte-Carlo results and good accuracy with an average relative error below 3.6% was achieved for arbitrary objects and over the full range of commonly used convolution kernels and field-of-view settings.

Measurement of the semileptonic branching ratio of B_{s};{0} to an orbitally excited D_{s};{**} state: Br(B_{s};{0}-->D_{s1};{-}(2536)mu;{+}nuX)

In a data sample of approximately 1.3 fb;{-1} collected with the D0 detector between 2002 and 2006, the orbitally excited charm state D_{s1};{+/-}(2536) has been observed with a measured mass of 2535.7+/-0.6(stat)+/-0.5(syst) MeV/c;{2} via the decay mode B_{s};{0}-->D_{s1};{-}(2536)mu;{+}nu_{mu}X. A first measurement is made of the branching ratio product Br(b[over ]-->D_{s1};{-}(2536)mu;{+}nu_{mu}X)xBr(D_{s1};{-}-->D;{*-}K_{S};{0}). Assuming that D_{s1};{-}(2536) production in semileptonic decay is entirely from B_{s};{0}, an extraction of the semileptonic branching ratio Br(B_{s};{0}-->D_{s1};{-}(2536)mu;{+}nu_{mu}X) is made.

Measurement of Bs0 mixing parameters from the flavor-tagged decay Bs0-->J/psiphi

From an analysis of the flavor-tagged decay Bs0-->J/psiphi we obtain the width difference between the Bs0 light and heavy mass eigenstates, DeltaGammas = 0.19+/-0.07(stat)(-0.01)+0.02(syst) ps(-1), and the CP-violating phase, phi s= -0.57(-0.30)+0.24(stat)(-0.02)+0.08(syst). The allowed 90% CL intervals of DeltaGammas and phi s are 0.06 < DeltaGammas < 0.30 ps(-1) and -1.20 < phi s < 0.06, respectively. The data sample corresponds to an integrated luminosity of 2.8 fb(-1) accumulated with the D0 detector at the Fermilab Tevatron collider.

Search for pair production of doubly charged Higgs bosons in the H++H- - -->mu+ mu+ mu- mu- final state

We report the results of a search for pair production of doubly charged Higgs bosons via pp over-->H++H - - X-->mu+ mu+ mu- mu- X at sqrt s=1.96 TeV. We use a data set corresponding to an integrated luminosity of 1.1 fb(-1) collected from 2002 to 2006 by the D0 detector at the Fermilab Tevatron Collider. In the absence of an excess above the standard model background, lower mass limits of M(H L +/- +/-) >150 GeV/c2 and M(H R+/- +/-) >127 GeV/c2 at 95% C.L. are set, respectively, for left-handed and right-handed doubly charged Higgs bosons assuming a 100% branching ratio into muons.

Measurement of the inclusive jet cross section in pp[over] collisions at square root [s]=1.96 TeV

We report on a measurement of the inclusive jet cross section in pp[over ] collisions at a center-of-mass energy sqrt[s]=1.96 TeV using data collected by the D0 experiment at the Fermilab Tevatron Collider corresponding to an integrated luminosity of 0.70 fb;{-1}. The data cover jet transverse momenta from 50 to 600 GeV and jet rapidities in the range -2.4 to 2.4. Detailed studies of correlations between systematic uncertainties in transverse momentum and rapidity are presented, and the cross section measurements are found to be in good agreement with next-to-leading order QCD calculations.

Search for decay of a fermiophobic Higgs boson hf-->gammagamma with the D0 detector at sqrt s=1.96 TeV

We report the results of a search for a narrow resonance decaying into two photons in 1.1 fb;{-1} of data collected by the D0 experiment at the Fermilab Tevatron Collider during the period 2002-2006. We find no evidence for such a resonance and set a lower limit on the mass of a fermiophobic Higgs boson of m_{h_{f}}>100 GeV at the 95% C.L. This exclusion limit exceeds those obtained in previous searches at the Fermilab Tevatron and covers a significant region of the parameter space B(h_{f}-->gammagamma) vs m_{h_{f}} which was not accessible at the CERN Large Electron-Positron Collider.

Search for large extra dimensions via single photon plus missing energy final states at sqrt s = 1.96 TeV

We report on a search for large extra dimensions in a data sample of approximately 1 fb(-1) of pp[over] collisions at sqrt s=1.96 TeV. We investigate Kaluza-Klein graviton production with a photon and missing transverse energy in the final state. At the 95% C.L. we set limits on the fundamental mass scale M(D) from 884 to 778 GeV for two to eight extra dimensions.