Technical Note: spektr 3.0-A computational tool for x-ray spectrum modeling and analysis

Fine structural human phantom in dentistry and instance tooth segmentation

In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.

Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric

PURPOSE

Interventional Cone-Beam CT (CBCT) offers 3D visualization of soft-tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image-based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first-order image properties and that lack awareness of the underlying anatomy. This work proposes a data-driven approach to motion quantification via a learned, context-aware, deformable metric,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image.

METHODS

The proposedVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was modeled as a deep convolutional neural network (CNN) trained to recreate a reference-based structural similarity metric-visual information fidelity (VIF). The deep CNN acted on motion-corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion-free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi-branch architecture with a high-resolution branch for estimation of voxel-wise VIF on a small volume of interest. A second contextual, low-resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion-free and motion-corrupted data obtained with a high-fidelity forward projection model for a protocol involving 120 kV and 9.90 mGy. The performance ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was evaluated via metrics of correlation with ground truth VIF ${\bm{VIF}}$ and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20 mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90-120 kV) and dose (1.19-39.59 mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft-tissue boundaries and sharpness of contrast-enhanced vascularity.

RESULTS

The magnitude and spatial map ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ properly reflects the change in motion amplitudes and frequencies: voxel-wise averaging of the localVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2 mm, 12 cycles/scan) and 0.29 for the case with severe motion (12 mm, 6 cycles/scan). Autofocus motion compensation usingVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high-contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast-enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness.

CONCLUSION

The proposedVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , featuring a novel context-aware architecture, demonstrated its capacity as a reference-free surrogate of structural similarity to quantify motion-induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x-ray techniques, and anatomical instances. The proposed anatomy- and context-aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement

Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

The effects of intra-detector Compton scatter on low-frequency DQE for photon-counting CT using edge-on-irradiated silicon detectors

BACKGROUND

Edge-on-irradiated silicon detectors are currently being investigated for use in full-body photon-counting computed tomography (CT) applications. The low atomic number of silicon leads to a significant number of incident photons being Compton scattered in the detector, depositing a part of their energy and potentially being counted multiple times. Even though the physics of Compton scatter is well established, the effects of Compton interactions in the detector on image quality for an edge-on-irradiated silicon detector have still not been thoroughly investigated.

PURPOSE

To investigate and explain effects of Compton scatter on low-frequency detective quantum efficiency (DQE) for photon-counting CT using edge-on-irradiated silicon detectors.

METHODS

We extend an existing Monte Carlo model of an edge-on-irradiated silicon detector with 60 mm active absorption depth, previously used to evaluate spatial-frequency-based performance, to develop projection and image domain performance metrics for pure density and pure spectral imaging tasks with 30 and 40 cm water backgrounds. We show that the lowest energy threshold of the detector can be used as an effective discriminator of primary counts and cross-talk caused by Compton scatter. We study the developed metrics as functions of the lowest threshold energy for root-mean-square electronic noise levels of 0.8, 1.6, and 3.2 keV, where the intermediate level 1.6 keV corresponds to the noise level previously measured on a single sensor element in isolation. We also compare the performance of a modeled detector with 8, 4, and 2 optimized energy bins to a detector with 1-keV-wide bins.

RESULTS

In terms of low-frequency DQE for density imaging, there is a tradeoff between using a threshold low enough to capture Compton interactions and avoiding electronic noise counts. For 30 cm water phantom, 4 energy bins, and a root-mean-square electronic noise of 0.8, 1.6, and 3.2 keV, it is optimal to put the lowest energy threshold at 3, 6, and 1 keV, which gives optimal projection-domain DQEs of 0.64, 0.59, and 0.52, respectively. Low-frequency DQE for spectral imaging also benefits from measuring Compton interactions with respective optimal thresholds of 12, 12, and 13 keV. No large dependence on background thickness was observed. For the intermediate noise level (1.6 keV), increasing the lowest threshold from 5 to 35 keV increases the variance in a iodine basis image by 60%-62% (30 cm phantom) and 67%-69% (40 cm phantom), with 8 bins. Both spectral and density DQE are adversely affected by increasing the electronic noise level. Image-domain DQE exhibits similar qualitative behavior as projection-domain DQE.

CONCLUSIONS

Compton interactions contribute significantly to the density imaging performance of edge-on-irradiated silicon detectors. With the studied detector topology, the benefit of counting primary Compton interactions outweighs the penalty of multiple counting at all lowest threshold energies. Compton interactions also contribute significantly to the spectral imaging performance for measured energies above 10 keV.

Maximizing microcalcification detectability in low-dose dedicated cone-beam breast CT: parallel cascades-based theoretical analysis

Purpose

We aim to determine the combination of X-ray spectrum and detector scintillator thickness that maximizes the detectability of microcalcification clusters in dedicated cone-beam breast CT.

Approach

A cascaded linear system analysis was implemented in the spatial frequency domain and was used to determine the detectability index using numerical observers for the imaging task of detecting a microcalcification cluster with 0.17 mm diameter calcium carbonate spheres. The analysis considered a thallium-doped cesium iodide scintillator coupled to a complementary metal-oxide semiconductor detector and an analytical filtered-back-projection reconstruction algorithm. Independent system parameters considered were the scintillator thickness, applied X-ray tube voltage, and X-ray beam filtration. The combination of these parameters that maximized the detectability index was considered optimal.

Results

Prewhitening, nonprewhitening, and nonprewhitening with eye filter numerical observers indicate that the combination of 0.525 to 0.6 mm thick scintillator, 70 kV, and 0.25 to 0.4 mm added copper filtration maximized the detectability index at a mean glandular dose (MGD) of 4.5 mGy.

Conclusion

Using parallel cascade systems' analysis, the combination of parameters that could maximize the detection of microcalcifications was identified. The analysis indicates that a harder beam than that used in current practice may be beneficial for the task of detecting microcalcifications at an MGD suitable for breast cancer screening.

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.

Development of an open project rectangular collimator for use with intraoral dental X-ray unit

In this work, an open beam-limiting device, consisting of a rectangular collimator to be coupled to an intraoral dental X-ray device, was made using recycled lead sheets as a radiation-absorbing element. The collimator was designed for 3D printing, and using Spektr 3.0 software, the number of lead sheets needed to absorb excess radiation was calculated. The rectangular collimator reduced the radiation dose to patients by 65% when using four layers of recycled lead sheets (saturating with a 70% reduction in radiation dose at the limit of eight or more sheets of lead). The rectangular collimator does not negatively impact the quality of the radiological image, is available as an open design for 3D printing, and can be built with materials that are easily accessible to the dentist, facilitating its use in clinical practice and reducing the patient's exposure to ionizing radiation.

Technical note: SpekPy Web-online x-ray spectrum calculations using an interface to the SpekPy toolkit

Knowledge of the photon spectrum emitted from an x-ray tube is frequently needed in imaging and dosimetry contexts. As the spectrum characteristics are influenced by several parameters and routine measurement of a spectrum is often impractical, a variety of software programs have been developed over the decades for convenient calculations. SpekPy is a state-of-the-art software package containing several spectrum models, and was created to estimate photon spectra originating from x-ray tubes using a small set of input parameters (e.g., anode material, anode angle, tube potential, filtration, etc.). SpekPy is distributed as a Python toolkit and is available free of charge. The toolkit does, however, lack a graphical user interface and a user is required to write a Python script to make use of it. In this work this limitation is addressed by introducing a web application called SpekPy Web: a graphical user interface together with an application programmable interface (API). These developments both make the SpekPy spectrum models accessible to a broader set of users and increases the ease of use for existing users. SpekPy Web is hosted at: https://spekpy.smile.ki.se. The functionality of the software is demonstrated, using its API, by estimating first half-value layers (HVLs) for 15 standard beam qualities from the International Bureau of Weights and Measures (BIPM). The estimated HVLs were found to all be within 3.5% agreement when compared to experimental values, with an average calculation time of 2.5 s per spectrum. half-value-layer, software, x-ray spectrum.

Enhanced in-stent luminal visualization and restenosis diagnosis in coronary computed tomography angiography via coronary stent decomposition algorithm from dual-energy image

Stent implantation is a principal therapeutic approach for coronary artery diseases. Nonetheless, the presence of stents significantly interferes with in-stent luminal (ISL) visualization and complicates the diagnosis of in-stent restenosis (ISR), thereby increasing the risk of misdiagnoses and underdiagnoses in coronary computed tomography angiography (CCTA). Dual-energy (DE) CT could calculate the volume fraction for voxels from low- and high-energy images (LHEI) and provide information on specific three basic materials. In this study, the innovative coronary stent decomposition algorithm (CSDA) was developed from the DECT three materials decomposition (TMD), through spectral simulation to determine the scan and attenuation coefficient for the stent, and preliminary execution for an in vitro sophisticated polyether ether ketone (PEEK) 3D-printed right coronary artery (RCA) replica. Furthermore, the whole-coronary-artery replica with multi-stent implantation, the RCA replica with mimetic plaque embedded, and two patients with stent further validated the effectiveness of CSDA. Post-CSDA images manifested no weakened attenuation values, no elevated noise values, and maintained anatomical integrity in the coronary lumen. The stents were effectively removed, allowing for the ISL and ISR to be clearly visualized with a discrepancy in diameters within 10%. We believe that CSDA presents a promising solution for enhancing CCTA diagnostic accuracy post-stent implantation.

Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm

Objectives. This study aims to develop a method for predicting patient-specific head organ doses by training a support vector regression (SVR) model based on radiomics features and graphics processing unit (GPU)-calculated reference doses.Methods. In this study, 237 patients who underwent brain CT scans were selected, and their CT data were transferred to an autosegmentation software to segment head regions of interest (ROIs). Subsequently, radiomics features were extracted from the CT data and ROIs, and the benchmark organ doses were computed using fast GPU-accelerated Monte Carlo (MC) simulations. The SVR organ dose prediction model was then trained using the radiomics features and benchmark doses. For the predicted organ doses, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated. The robustness of organ dose prediction was verified by changing the patient samples on the training and test sets randomly.Results. For all head organs, the maximal difference between the reference and predicted dose was less than 1 mGy. For the brain, the organ dose was predicted with an absolute error of 1.3%, and theR2reached up to 0.88. For the eyes and lens, the organ doses predicted by SVR achieved an RRMSE of less than 13%, the MAPE ranged from 4.5% to 5.5%, and theR2values were more than 0.7.Conclusions. Patient-specific head organ doses from CT examinations can be predicted within one second with high accuracy, speed, and robustness by training an SVR using radiomics features.

Effect of tin spectral filtration on organ and effective dose in CT colonography and CT lung cancer screening

BACKGROUND

Studies of tin spectral filtration have demonstrated potential in reducing radiation dose while maintaining image quality for unenhanced computed tomography (CT) scans. The extent of dose reduction, however, was commonly measured using the change in the scanner's reported CTDIvol . This method does not account for how tin filtration affects patient organ and effective dose.

PURPOSE

To investigate the effect of tin filtration on patient organ and effective dose for CT Lung Cancer Screening (LCS) and CT Colonography (CTC).

METHODS

A previously-developed Monte Carlo program was adapted to model a 96-row CT scanner (Somatom Force, Siemens Healthineers) with tin filtration capabilities at 100 kV (100Sn) and 150 kV (150Sn). The program was then validated using experimental CTDIvol measurements at all available kV (70-150 kV) and tin-filtered kV options (100Sn and 150Sn). After validation, the program simulated LCS scans of the chest and CTC scan of the abdomen-pelvis for a population of 53 computational patient models from the extended cardiac-torso family. Each scan was performed using three different spectra: 120 kV, 100Sn, and 150Sn. CTDIvol -normalized organ doses and DLP-normalized effective doses, commonly referred to as dose conversion factors, were compared between the different spectra.

RESULTS

For all LCS and CTC scans, CTDIvol -normalized organ doses and DLP-normalized effective doses increased with increasing beam hardness (120 kV, 100Sn, 150 Sn). For LCS, relative for 120 kV, conversion factors for 100Sn produced a median increase in effective dose of 9%, with organ dose increases of 8% to lung, 5% to breast, 15% to thyroid, and 3% to skin. Conversion factors for 150Sn produced a median increase in effective dose of 20%, with organ dose increases of 16%, 18%, 26%, and 12% to these same organs, respectively. For CTC, relative for 120 kV, conversion factors for 100Sn produced a median increase in effective dose of 12%, with organ dose increases of 9% to colon, 10% to liver, 11% to stomach, and 4% to skin. Conversion factors for 150Sn produced a median increase in effective dose of 21%, with organ dose increases of 16%, 17%, 19%, and 10% to these same organs, respectively.

CONCLUSIONS

Results show that dose conversion factors are greater when using tin filtration and should be considered when evaluating tin's potential for dose reduction.

Direct energy binning for photon counting detectors: Simulation study

BACKGROUND

Photon counting detectors (PCDs) for x-ray computed tomography (CT) face spectral distortion from pulse pileup and charge sharing. The photon counting scheme used by many PCDs is threshold-subtract (TS) with pulse height analysis (PHA), where each counter counts up-crossing events when pulses exceed an energy threshold. PCD data are not Poisson-distributed due to charge sharing and pulse pileup, but the counting statistics have never been studied yet.

PURPOSE

The objectives of this study were (1) to propose a modified photon counting scheme, direct energy binning (DB), that is expected to be robust against pulse pileup; (2) to assess the performance of DB compared to TS; and (3) to evaluate its counting statistics.

METHODS

With DB scheme, counter k starts a timer upon an up-crossing event of energy threshold k, and adds a count only if the next higher energy threshold (k+1) was not crossed within a short time window (hence, the pulse peak belongs to the energy bin k). We used Monte Carlo (MC) simulation and assessed count-rate curves and count-rate-dependent spectral imaging task performance for conventional CT imaging as well as water thickness estimation, water-bone material decomposition, and K-edge imaging with tungsten as the K-edge material. We also assessed count-rate-dependent measurement statistics such as expectation, variance, and covariance of total counts as well as energy bin outputs. The agreement with counting statistics models was also evaluated.

RESULTS

The DB scheme improved the count-rate curve, that is, mean measured counts as a function of input count-rate, and peaked with 59% higher count-rate capability than the TS scheme (3.5 × 108 counts per second (cps)/mm2 versus 2.3 × 108  cps/mm2 ). The Cramér-Rao lower bounds (CRLB) of the variance of basis line integrals estimation for DB was better than those for TS by 2% for the conventional CT imaging, 30% for water-bone material decomposition, and 32% for K-edge imaging at 1000 mA (at 7.3 × 107  cps/sub-pixel after charge sharing). When count-rates were lower, PCD data statistics were dominated by charge sharing: the variance of total counts and lower energy bins was larger than the mean counts; the covariance of bin data was positive and non-zero. When count-rates were higher, PCD data statistics were dominated by pulse pileup: the variance of data was lower than the mean; the covariance of bin data was negative. The transition between the two regimes occurred smoothly, and pulse pileup dominated the statistics ≥400 mA (when the count-rate after charge sharing was 2.9 × 107  cps/sub-pixel and the probability of count-loss for DB was 37%). Both DB and TS had good agreement with Yu-Fessler's models of total counts; however, DB had a better agreement with Wang's variance and covariance models for energy bin data than TS did.

CONCLUSIONS

The proposed DB scheme had several advantages over TS. At low to moderate flux, DB could improve the resilience of PCDs to pulse pileup. Counting statistics deviated from the Poisson distribution due to charge sharing for lower count-rate conditions and pulse pileup for higher count-rate conditions.

Improving input contrast estimation to an x-ray imaging system

Objective.An appropriate parameter to study the performance of an x-ray imaging system is contrast transfer, or the system's ability to capture contrast in the radiation beam and bring it to the image. However, determining the input contrast to the system is difficult, as it is heavily affected by secondary radiation, which in turn depends on a multitude of factors. This work presents a method to improve the calculation of input contrast to the imaging system when PMMA phantoms are used.Approach.An expression to obtain input contrast from primary radiation attenuation and scatter-to-primary ratio is shown, and the approximation upon which it is based is discussed. Primary and secondary radiation emerging from the phantoms are calculated for monoenergetic pencil beams impinging on planar PMMA phantoms of different thicknesses. Monte Carlo simulations of two types of anti-scatter grids are also incorporated into the calculations.Main results.The primary and secondary components of radiant energy and grid transmission factors are presented for monoenergetic beams with energies from 10 to 150 keV. These results are then used to calculate input contrast for polyenergetic beams when using a commercial image quality phantom combined with different thicknesses of PMMA and anti-scatter grids.Significance.The information of the object contrast carried by the beam constitutes the input to the imaging system. An accurate determination of this input contrast can be carried out in a wide variety of situations from the study of a reduced number of cases, as those presented in this work for monoenergetic beams, PMMA phantoms of different thicknesses and anti-scatter grids. The relationship between the input contrast and the contrast due to primary radiation used in this work provides a good approximation for the different combinations of inserts, phantoms, grids, and energy spectra analyzed here.

Application of MLP neural network to predict X-ray spectrum from tube voltage, filter material, and filter thickness used in medical imaging systems

The X-ray energy spectrum is crucial for image quality and dosage assessment in mammography, radiography, fluoroscopy, and CT which are frequently used for the diagnosis of many diseases including but not limited to patients with cardiovascular and cerebrovascular diseases. X-ray tubes have an electron filament (cathode), a tungsten/rubidium target (anode) oriented at an angle, and a metal filter (aluminum, beryllium, etc.) that may be placed in front of an exit window. When cathode electrons meet the anode, they generate X-rays with varied energies, creating a spectrum from zero to the electrons' greatest energy. In general, the energy spectrum of X-rays depends on the electron beam's energy (tube voltage), target angle, material, filter thickness, etc. Thus, each imaging system's X-ray energy spectrum is unique to its tubes. The primary goal of the current study is to develop a clever method for quickly estimating the X-ray energy spectrum for a variety of tube voltages, filter materials, and filter thickness using a small number of unique spectra. In this investigation, two distinct filters made of beryllium and aluminum with thicknesses of 0.4, 0.8, 1.2, 1.6, and 2 mm were employed to obtain certain limited X-ray spectra for tube voltages of 20, 30, 40, 50, 60, 80, 100, 130, and 150 kV. The three inputs of 150 Multilayer Perceptron (MLP) neural networks were tube voltage, filter type, and filter thickness to forecast the X-ray spectra point by point. After training, the MLP neural networks could predict the X-ray spectra for tubes with voltages between 20 and 150 kV and two distinct filters made of aluminum and beryllium with thicknesses between 0 and 2 mm. The presented methodology can be used as a suitable, fast, accurate and reliable alternative method for predicting X-ray spectrum in medical applications. Although a technique was put out in this work for a particular system that was the subject of Monte Carlo simulations, it may be applied to any genuine system.

A quantitative CBCT pipeline based on 2D antiscatter grid and grid-based scatter sampling for image-guided radiation therapy

BACKGROUND

Quantitative accuracy is critical for expanding the role of cone beam CT (CBCT) imaging from target localization to quantitative treatment monitoring and plan adaptations in radiation therapy. Despite advances in CBCT image quality improvement methods, quantitative accuracy gap between CBCT and multi-detector CT (MDCT) remains.

PURPOSE

In this work, a physics-driven approach was investigated that combined robust scatter rejection, raw data correction and iterative image reconstruction to further improve CBCT image quality and quantitative accuracy, referred to as quantitative CBCT (qCBCT).

METHODS

QCBCT approach includes tungsten 2D antiscatter grid hardware, residual scatter correction with grid-based scatter sampling, image lag, and beam hardening correction for offset detector geometry linac-mounted CBCT. Images were reconstructed with iterative image reconstruction to reduce image noise. qCBCT was evaluated using a variety of phantoms to investigate the effect of object size and its composition on image quality, and image quality was benchmarked against clinical CBCT and gold standard MDCT images used for treatment planning.

RESULTS

QCBCT provided statistically significant improvement in CT number accuracy and reduced image artifacts when compared to clinical CBCT images. When compared to gold standard MDCT, mean HU errors in qCBCT and clinical CBCT were 17 ± 9 and 38 ± 29 HU, respectively. Magnitude of phantom size dependent HU variations were comparable between MDCT and qCBCT images. With iterative reconstruction, contrast-to-noise ratio improved by 25% when compared to clinical CBCT protocols.

CONCLUSIONS

Combination of novel scatter suppression techniques and other data correction methods in qCBCT provided CT number accuracy comparable to gold standard MDCT used for treatment planning. This approach may potentially improve CBCT's promise in fulfilling the tasks that demand high quantitative accuracy, such as online dose calculations and treatment response assessment, in image guided radiation therapy.

Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network

BACKGROUND

The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-dependent as well as system-specific.

PURPOSE

In this study, we propose a dual-domain network for reducing the bowtie-filter-induced artifacts in CBCT images.

METHODS

In the projection domain, the network compensates for the filter-induced beam-hardening that are highly related to the eclipse artifacts. The output of the projection-domain network was used for image reconstruction and the reconstructed images were fed into the image-domain network. In the image domain, the network further reduces the remaining cupping artifacts that are associated with the scatter. A single image-domain-only network was also implemented for comparison.

RESULTS

The proposed approach successfully enhanced soft-tissue contrast with much-reduced image artifacts. In the numerical study, the proposed method decreased perceptual loss and root-mean-square-error (RMSE) of the images by 84.5% and 84.9%, respectively, and increased the structure similarity index measure (SSIM) by 0.26 compared to the original input images on average. In the experimental study, the proposed method decreased perceptual loss and RMSE of the images by 87.2% and 92.1%, respectively, and increased SSIM by 0.58 compared to the original input images on average.

CONCLUSIONS

We have proposed a deep-learning-based dual-domain framework to reduce the bowtie-filter artifacts and to increase the soft-tissue contrast in CBCT images. The performance of the proposed method has been successfully demonstrated in both numerical and experimental studies.

Quantitative Evaluation of Scatter Correction in 128-slice Fan-Beam Computed Tomography Scan using Geant4 Application for Tomographic Emission Monte Carlo Simulation

Background

Simulation of tomographic imaging systems with fan-beam geometry, estimation of scattered beam profile using Monte Carlo techniques, and scatter correction using estimated data have always been new challenges in the field of medical imaging. The most important aspect is to ensure the results of the simulation and the accuracy of the scatter correction. This study aims to simulate 128-slice computed tomography (CT) scan using the Geant4 Application for Tomographic Emission (GATE) program, to assess the validity of this simulation and estimate the scatter profile. Finally, a quantitative comparison of the results is made from scatter correction.

Methods

In this study, 128-slice CT scan devices with fan-beam geometry along with two phantoms were simulated by GATE program. Two validation methods were performed to validate the simulation results. The data obtained from scatter estimation of the simulation was used in a projection-based scatter correction technique, and the post-correction results were analyzed using four quantities, such as: pixel intensity, CT number inaccuracy, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR).

Results

Both validation methods have confirmed the appropriate accuracy of the simulation. In the quantitative analysis of the results before and after the scatter correction, it should be said that the pixel intensity patterns were close to each other, and the accuracy of the CT scan number reached <10%. Moreover, CNR and SNR have increased by more than 30%-65% respectively in all studied areas.

Conclusion

The comparison of the results before and after scatter correction shows an improvement in CNR and SNR while a reduction in cupping artifact according to pixel intensity pattern and enhanced CT number accuracy.

In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation

BACKGROUND

In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high-resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random.

PURPOSE

The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation.

METHODS

The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization-based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non-intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented.

RESULTS

The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high-resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law= 1.2 ± 1 . 2 o $ = \;1.2 \pm {1.2^o}$ ), radii (median Murray deviation= 0.08 $ = \;0.08$ ), and smoothly curved, non-intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01).

CONCLUSIONS

This method facilitates the generation of large datasets of high-resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.

Evaluation of the feasibility of a multisource CBCT for maxillofacial imaging

Objective. The aim of this study was to investigate the feasibility of improving the image quality and accuracy of cone beam computed tomography (CBCT) by replacing the conventional wide cone angle x-ray tube with a distributed x-ray source array positioned in the axial direction.Approach. The multisource CBCT (ms-CBCT) design was experimentally simulated using a benchtop scanner with a carbon nanotube x-ray tube and a flat-panel detector. The source was collimated and translated in the axial direction to simulate a source array with a reduced cone angle for each beam. An adjacent scatter ratio subtraction (ASRS) method was implemented for residual scatter reduction. Several phantoms were imaged using the ms-CBCT and conventional CBCT configurations under otherwise similar conditions. The Requirements of the ms-CBCT design on the x-ray source and detector were evaluated.Main results. Compared to the conventional CBCT, the ms-CBCT design with 8 sources and ASRS significantly improved the image quality and accuracy, including: (1) reducing the cupping artifact from 15% to 3.5%; (2) reducing the spatial nonuniformity of the CT Hounsfield unit values from 38.0 to 9.2; (3) improving the contrast-to-noise ratio of the low contrast objects (acrylic and low density polyethylene inserts) against the water-equivalent background by ∼20% and (4) reducing the root-mean-square error of the HU values by 70%, from 420.1 to 124.4. The imaging dose and scanning time used by the current clinical CBCT for maxillofacial imaging can be achieved by current source and detector technologies.Significance. The ms-CBCT design significantly reduces the scatter and improves the image quality and accuracy compared to the conventional CBCT.

Feasibility of 4D HDR brachytherapy source tracking using x-ray tomosynthesis: Monte Carlo investigation

PURPOSE

High dose rate (HDR) brachytherapy rapidly delivers dose to targets with steep dose gradients. This treatment method must adhere to prescribed treatment plans with high spatiotemporal accuracy and precision, as failure to do so may degrade clinical outcomes. One approach to achieving this goal is to develop imaging techniques to track HDR sources in vivo in reference to surrounding anatomy. This work investigates the feasibility of using an isocentric C-arm x-ray imager and tomosynthesis methods to track Ir-192 HDR brachytherapy sources in vivo over time (4D).

METHODS

A tomosynthesis imaging workflow was proposed and its achievable source detectability, localization accuracy, and spatiotemporal resolution were investigated in silico. An anthropomorphic female XCAT phantom was modified to include a vaginal cylinder applicator and Ir-192 HDR source (0.5 × 0.5 × 5.0 mm3 ), and the workflow was carried out using the MC-GPU Monte Carlo image simulation platform. Source detectability was characterized using the reconstructed source signal-difference-to-noise-ratio (SDNR), localization accuracy by the absolute 3D error in its measured centroid location, and spatiotemporal resolution by the full-width-at-half-maximum (FWHM) of line profiles through the source in each spatial dimension considering a maximum C-arm angular velocity of 30° per second. The dependence of these parameters on acquisition angular range (θtot = 0°-90°), number of views, angular increment between views (Δθ = 0°-15°), and volumetric constraints imposed in reconstruction was evaluated. Organ voxel doses were tallied to derive the workflow's attributable effective dose.

RESULTS

The HDR source was readily detected and its centroid was accurately localized with the proposed workflow and method (SDNR: 10-40, 3D error: 0-0.144 mm). Tradeoffs were demonstrated for various combinations of image acquisition parameters; namely, increasing the tomosynthesis acquisition angular range improved resolution in the depth-encoded direction, for example from 2.5 mm to 1.2 mm between θtot = 30o and θtot = 90o , at the cost of increasing acquisition time from 1 to 3 s. The best-performing acquisition parameters (θtot = 90o , Δθ = 1°) yielded no centroid localization error, and achieved submillimeter source resolution (0.57 × 1.21 × 5.04 mm3 apparent source dimensions, FWHM). The total effective dose for the workflow was 263 µSv for its required pre-treatment imaging component and 7.59 µSv per mid-treatment acquisition thereafter, which is comparable to common diagnostic radiology exams.

CONCLUSIONS

A system and method for tracking HDR brachytherapy sources in vivo using C-arm tomosynthesis was proposed and its performance investigated in silico. Tradeoffs in source conspicuity, localization accuracy, spatiotemporal resolution, and dose were determined. The results suggest this approach is feasible for localizing an Ir-192 HDR source in vivo with submillimeter spatial resolution, 1-3 second temporal resolution and minimal additional dose burden.

Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning

Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.

Hybrid energy and angle dispersive X-ray diffraction computed tomography

Pixelated energy resolving detectors enable acquisition of X-ray diffraction (XRD) signals using a hybrid energy- and angle- dispersive technique, potentially paving the way for the development of novel benchtop XRD imaging or computed tomography (XRDCT) systems, utilising readily available polychromatic X-ray sources. In this work, a commercially available pixelated cadmium telluride (CdTe) detector, HEXITEC (High Energy X-ray Imaging Technology), was used to demonstrate such an XRDCT system. Specifically, a novel fly-scan technique was developed and compared to the established step-scan technique, reducing the total scan time by 42% while improving the spatial resolution, material contrast and therefore the material classification.

Virtual monochromatic imaging with projection-based material decomposition algorithm for metal artifacts reduction in photon-counting detector computed tomography

Metal artifacts present a major challenge to computed tomography (CT) because they reduce the image quality in medical diagnosis and treatment. Several metal artifact reduction (MAR) methods have been proposed to address this issue in previous studies. This study aimed to synthesize a virtual monochromatic image for MAR in CT images using projection-based material decomposition (MD) algorithms. We developed a spectral micro-CT prototype system equipped with a photon-counting detector (PCD) and PCD-CT imaging simulator to assess the performances of different MAR methods. Two projection-based MD algorithms were implemented and evaluated for their MAR performances in CT images and compared with conventional sinogram inpainting MAR methods. Different parts of digital 4D-extended cardiac torso (XCAT) phantoms with metal implants were designed to simulate various real scenarios. A homemade metal artifact evaluation (MAE) phantom was used to evaluate the MAR performance in experiments. The simulated results of the XCAT phantom indicated that the projection-based virtual monochromatic CT (VMCT) images provided better image quality than the conventional MAR images without blurring the normal tissues at the position of the metal artifacts. Various quantitative indicators support this conclusion. Additionally, the experimental results of the MAE phantom reveal that projection-based VMCT images can avoid image distortion caused by metal artifacts, unlike conventional MAR methods. In regards to the projection-based VMCT images, the simulated and experimental results demonstrated that using the linear maximum likelihood estimators with an error correction look-up table algorithm yielded better MAR performance compared to that obtained using a polynomial algorithm. Furthermore, projection-based VMCT images can not only reduce metal artifacts effectively but also simultaneously prevents object blurring at the metal artifact position and image distortion of the metal implants. Hence, the CT image quality can be further improved to increase the abilities for both preoperative and postoperative assessment of metal implants.

Sinogram domain metal artifact correction of CT via deep learning

PURPOSE

Metal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in image quality can hinder subsequent clinical diagnosis and treatment planning. Beam hardening artifacts are often manifested as severe strip artifacts in the image domain, affecting the overall quality of the reconstructed CT image. In the sinogram domain, metal is typically located in specific areas, and image processing in these regions can preserve image information in other areas, making the model more robust. To address this issue, we propose a region-based correction of beam hardening artifacts in the sinogram domain using deep learning.

METHODS

We present a model composed of three modules: (a) a Sinogram Metal Segmentation Network (Seg-Net), (b) a Sinogram Enhancement Network (Sino-Net), and (c) a Fusion Module. The model starts by using the Attention U-Net network to segment the metal regions in the sinogram. The segmented metal regions are then interpolated to obtain a sinogram image free of metal. The Sino-Net is then applied to compensate for the loss of organizational and artifact information in the metal regions. The corrected metal sinogram and the interpolated metal-free sinogram are then used to reconstruct the metal CT and metal-free CT images, respectively. Finally, the Fusion Module combines the two CT images to produce the result.

RESULTS

Our proposed method shows strong performance in both qualitative and quantitative evaluations. The peak signal-to-noise ratio (PSNR) of the CT image before and after correction was 18.22 and 30.32, respectively. The structural similarity index measure (SSIM) improved from 0.75 to 0.99, and the weighted peak signal-to-noise ratio (WPSNR) increased from 21.69 to 35.68.

CONCLUSIONS

Our proposed method demonstrates the reliability of high-accuracy correction of beam hardening artifacts.

X-ray energy spectrum estimation based on a virtual computed tomography system

This paper presents a method for estimating the x-ray energy spectrum for computed tomography (CT) in the diagnostic energy range from the reconstructed CT image itself. To this end, a virtual CT system was developed, and datasets, including CT images for the Gammex phantom labeled by the corresponding energy spectra, were generated. Using these datasets, an artificial neural network (ANN) model was trained to reproduce the energy spectrum from the CT values in the Gammex inserts. In the actual application, an aluminum-based bow-tie filter was used in the virtual CT system, and an ANN model with a bow-tie filter was also developed. Both ANN models without/with a bow-tie filter can estimate the x-ray spectrum within the agreement, which is defined as one minus the absolute error, of more than 80% on average. The agreement increases as the tube voltage increases. The estimation was occasionally inaccurate when the amount of noise on the CT image was considerable. Image quality with a signal-to-noise ratio of more than 10 for the basis material of the Gammex phantom was required to predict the spectrum accurately. Based on the experimental data acquired from Activion16 (Canon Medical System, Japan), the ANN model with a bow-tie filter produced a reasonable energy spectrum by simultaneous optimization of the shape of the bow-tie filter. The present method requires a CT image for the Gammex phantom only, and no special setup, thus it is expected to be readily applied in clinical applications, such as beam hardening reduction, CT dose management, and material decomposition, all of which require exact information on the x-ray energy spectrum.

Simulations of X-ray spectra, half value layer, and mean energy from mammography using EGSnrc Monte Carlo and SpekPy

INTRODUCTION

This study presents the simulation results of X-ray spectra, half value layers (HVLs), and mean energies (E_mean) of two mammography units using EGSnrc Monte Carlo (MC) and SpekPy computer codes.

METHODS

The spectra caused by different combinations of targets/filters at various tube voltages (kVps) of two mammography units were simulated using two different computer codes. The EGSnrc MC simulated data of spectra and E_mean were compared with those obtained from SpekPy. The simulated values of two units' HVLs obtained from two computer codes were compared with those from physical measurements from mammography machines used in clinical practice.

RESULTS

The maximum discrepancies in E_mean simulated from two codes were less than 4.1% and 1.5% for the target/filter combination of W/Rh and Mo/Mo, respectively. The HVLs of the SpekPy were well matched to the physical measurements. The percentage differences were within 3.3% and 6.8% for two units, respectively. The EGSnrc MC simulated values of HVLs show the percentage differences within 8.9% and 7.0% with those from physical measurements for two units, respectively. All methods of HVLs determination comply with the requirements of IAEA Human Health Series No.17.

CONCLUSIONS

The HVLs, E_mean, spectra varied depending on the target/filter combinations and composition of the mammography tubes. The simulation results verify that the HVLs evaluation using the EGSnrc MC and SpekPy can be validated for mammography standard beam qualities and provide prediction almost immediately compared with physical experiments.

IMPLICATIONS FOR PRACTICE

EGSnrc MC and SpekPy have been considered powerful toolkits to simulate typical X-ray tubes used in mammography due to the good agreement between the calculation of E_mean, physical measurements and simulated HVLs.

Technical note: Characteristics of the energy spread kernels of scattered radiation in an x-ray room

PURPOSE

To describe the scattered radiation spectra inside an x-ray room for different scattering conditions.

METHODS

Monte Carlo simulations of an x-ray room using phantoms of different size, varying field sizes, and a range of mono-energetic beams were carried out. For each energy, the particle fluence spectrum of scattered photons was collected at different spherical zones to describe the radiation reaching the different boundaries of the x-ray room. The effect on the scattered spectrum of the room floor was also considered.

RESULTS

The scattered spectra for mono-energetic primary beams at a given spherical zone give rise to oriented energy spread kernels (OESKs) that can be used to calculate the scattered spectrum for any poly-energetic beam at that zone. Despite the large differences, which can be seen in the OESKs when the scattering conditions vary, an important invariance is also observed: the position of the broad scatter peak for a given primary energy and zone.

CONCLUSIONS

The result of breaking down the calculation of the scattered radiation spectrum into the different factors that influence it allows estimating the spectrum in a wide range of situations. The invariant position of the broad scatter peak can be used to estimate the highest energy of the scattered photons for a given primary energy and zone, which may determine radiation shielding needs.

Hybrid Detection of Breast Abnormalities Based on Contrast Agents: Introducing a Proof of Concept from a Physics Perspective

This manuscript attempts to present a proof of concept from a physics perspective of a hybrid detective system based on the utilization of contrast agents with the purpose of indicating breast tissue abnormalities. In the present concept, the photon-counting module of the detector is set up to the K-characteristic radiation emitted by the contrast agent. Τwo X-ray spectra were used: 40 kV- W/Al (1.6 mm) and 50 kV- W/Al (1.6 mm) with additional filtration of 0.3 mm Gd. Iodine (I) contrast agent was studied as a ''fingerprint'' for tissue abnormality indication. A computational Monte Carlo model, based on previously published validated tabulated data and tissue experimental measurements, was developed with the purpose of showing that the present concept has practical potential; however, with a weakness of not being accompanied by experimental validation. The study considered two types of internal tissue layers (fibrous/tumor with thickness values of 0.2-2.5 mm) within an external layer of fat tissue (4 and 8 cm). Quantitative (number of encountered K-photons) and qualitative (tumor-fibrous ratio) advantages of using X-ray spectra of a higher tube voltage (50 kV) and of counting the Κ_α photons were found. In addition, the quantitative and qualitative benefits were correspondingly more dominant at high (2.5 mm) and low (0.2 mm) tissue thickness values. In conclusion, by utilizing suitable contrast agents as ''fingerprint'' tissue abnormalities, the acquisition of combined morphological and functional imaging features (through the counting of K-X-rays) could enhance breast imaging in its present form and lead to advanced prognostic capabilities of breast abnormalities.

Ratios of Eye Lens and Hand Equivalent Doses with Whole-Body Effective Doses for Operators Performing Interventional Radiological Procedures

ABSTRACT

Estimating radiation doses for operators performing interventional radiological procedures is crucial in the occupational radiation protection of medical staff. In this study, Monte Carlo simulations coupled with an anthropomorphic phantom were used to model various exposure scenarios during the procedures. Conversion coefficients of the dose-area product of x rays for the eye lens equivalent dose, hand equivalent dose, and whole-body effective dose of the operator were calculated. Accordingly, the relationships between these dose quantities in typical interventional configurations were established, considering various source locations, tube voltages, and use of protective equipment or not. The results are presented in a systematic way for easy comparison and use. Tables and figures of the data can be helpful to provide estimates of eye lens and hand equivalent doses when records of specific dosimeters are absent, such as in the retrospective assessment of operators' eye lens and hand equivalent doses in past practices.

Virtual computed-tomography system for deep-learning-based material decomposition

Objective. Material decomposition (MD) evaluates the elemental composition of human tissues and organs via computed tomography (CT) and is indispensable in correlating anatomical images with functional ones. A major issue in MD is inaccurate elemental information about the real human body. To overcome this problem, we developed a virtual CT system model, by which various reconstructed images can be generated based on ICRP110 human phantoms with information about six major elements (H, C, N, O, P, and Ca). Approach. We generated CT datasets labelled with accurate elemental information using the proposed generative CT model and trained a deep learning (DL)-based model to estimate the material distribution with the ICRP110 based human phantom as well as the digital Shepp–Logan phantom. The accuracy in quad-, dual-, and single-energy CT cases was investigated. The influence of beam-hardening artefacts, noise, and spectrum variations were analysed with testing datasets including elemental density and anatomical shape variations. Main results. The results indicated that this DL approach can realise precise MD, even with single-energy CT images. Moreover, noise, beam-hardening artefacts, and spectrum variations were shown to have minimal impact on the MD. Significance. Present results suggest that the difficulty to prepare a large CT database can be solved by introducing the virtual CT system and the proposed technique can be applied to clinical radiodiagnosis and radiotherapy.

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.

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.

A fidelity-embedded learning for metal artifact reduction in dental CBCT

PURPOSE

Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study, we used deep learning to reduce metal artifacts.

METHODS

The metal artifacts, appearing as streaks and shadows, are non-local and highly associated with various factors, including the geometry of metallic inserts, energy-dependent attenuation, and energy spectrum of the incident X-ray beam, making it difficult to learn their complicated structures directly. To provide a step-by-step environment in which deep learning can be trained, we propose an iterative learning approach in which the network at each iteration step learns the correction error caused by the previous network, while enforcing the data fidelity in the projection domain. To generate a realistic paired training dataset, metal-free CBCT scans were collected from patients without metallic inserts, and then simulated metal projection data were added to generate the corresponding metal-corrupted projection data.

RESULTS

The feasibility of the proposed method was investigated in clinical metal-affected CBCT scans, as well as simulated metal-affected CBCT scans. The results show that the proposed method significantly reduces metal artifacts while preserving the morphological structures near metallic objects and outperforms direct image domain learning.

CONCLUSION

The proposed fidelity-embedded learning can effectively reduce metal artifacts in dental CBCT compared with direct image domain learning. This article is protected by copyright. All rights reserved.

A simulated comparison of lung tumor target verification using stereoscopic tomosynthesis or radiography

PURPOSE

Mobile lung tumors are increasingly being treated with ablative radiotherapy, for which precise motion management is essential. In-room stereoscopic radiography systems are able to guide ablative radiotherapy for stationary cranial lesions but not optimally for lung tumors unless fiducial markers are inserted. We propose augmenting stereoscopic radiographic systems with multiple small x-ray sources to provide the capability of imaging with stereoscopic, single frame tomosynthesis.

METHODS

In single frame tomosynthesis, nine x-ray sources are placed in a 3 × 3 configuration and energized simultaneously. The beams from these sources are collimated so that they converge on the tumor and then diverge to illuminate nine non-overlapping sectors on the detector. These nine sector images are averaged together and filtered to create the tomosynthesis effect. Single frame tomosynthesis is intended to be an alternative imaging mode for existing stereoscopic systems with a field of view that is three times smaller and a temporal resolution equal to the frame rate of the detector. We simulated stereoscopic tomosynthesis and radiography using Monte Carlo techniques on 60 patients with early-stage lung cancer from the NSCLC-Radiomics dataset. Two board-certified radiation oncologists reviewed these simulated images and rated them on a 4-point scale (1: tumor not visible; 2: tumor visible but inadequate for motion management; 3: tumor visible and adequate for motion management; 4: tumor visibility excellent). Each tumor was independently presented four times (two viewing angles from radiography and two viewing angles from tomosynthesis) in a blinded fashion over two reading sessions.

RESULTS

The fraction of tumors that were rated as adequate or excellent for motion management (scores 3 or 4) from at least one viewing angle was 53% using radiography and 90% using tomosynthesis. From both viewing angles, the corresponding fractions were 7% for radiography and 48% for tomosynthesis. Readers agreed exactly on 62% of images and within 1 point on 98% of images. The acquisition technique was estimated to be 75 mAs at 120 kVp per treatment fraction assuming one verification image per breath, approximately one order of magnitude less than a standard dose cone beam CT.

CONCLUSIONS

Stereoscopic tomosynthesis may provide a noninvasive, low dose, intrafraction motion verification technique for lung tumors treated by ablative radiotherapy. The system architecture is compatible with real-time video capture at 30 frames per second. Simulations suggest that most, but not all, lung tumors can be adequately visualized from at least one viewing angle.

X-ray Compton backscattering imaging via structured light

Compton backscattering imaging (CBI) is a technique that uses ionizing radiation to detect the presence of low atomic number materials on a given target. Unlike transmission x-ray imaging, the source and sensor are located on the same side, such that the photons of interest are scattered back after the radiation impinges on the body. Rather than scanning the target pixel by pixel with a pencil-beam, this paper proposes the use of cone-beam coded illumination to create the compressive x-ray Compton backscattering imager (CXBI). The concept was developed and tested using Montecarlo simulations through the Geant4 application for tomography emissions (GATE), with conditions close to the ones encountered in experiments, and posteriorly, a test-bed implementation was mounted in the laboratory. The CXBI was evaluated under several conditions and with different materials as target. Reconstructions were run using denoising-prior-based inverse problem algorithms. Finally, a preliminary dose analysis was done to evaluate the viability of CXBI for human scanning.

Technical assessment of 2D and 3D imaging performance of an IGZO-based flat-panel X-ray detector

BACKGROUND

Indirect detection flat-panel detectors (FPDs) consisting of hydrogenated amorphous silicon (a-Si:H) thin-film transistors (TFTs) are a prevalent technology for digital x-ray imaging. However, their performance is challenged in applications requiring low exposure levels, high spatial resolution, and high frame rate. Emerging FPD designs using metal oxide TFTs may offer potential performance improvements compared to FPDs based on a-Si:H TFTs.

PURPOSE

This work investigates the imaging performance of a new indium gallium zinc oxide (IGZO) TFT-based detector in 2D fluoroscopy and 3D cone-beam CT (CBCT).

METHODS

The new FPD consists of a sensor array combining IGZO TFTs with a-Si:H photodiodes and a 0.7-mm thick CsI:Tl scintillator. The FPD was implemented on an x-ray imaging bench with system geometry emulating intraoperative CBCT. A conventional FPD with a-Si:H TFTs and a 0.6-mm thick CsI:Tl scintillator was similarly implemented as a basis of comparison. 2D imaging performance was characterized in terms of electronic noise, sensitivity, linearity, lag, spatial resolution (modulation transfer function, MTF), image noise (noise-power spectrum, NPS), and detective quantum efficiency (DQE) with entrance air kerma (EAK) ranging from 0.3 to 1.2 μGy. 3D imaging performance was evaluated in terms of the 3D MTF and noise-equivalent quanta (NEQ), soft-tissue contrast-to-noise ratio (CNR), and image quality evident in anthropomorphic phantoms for a range of anatomical sites and dose, with weighted air kerma, K w ${K_w}$ , ranging from 0.8 to 4.9 mGy.

RESULTS

The 2D imaging performance of the IGZO-based FPD exhibited up to ∼1.7× lower electronic noise than the a-Si:H FPD at matched pixel pitch. Furthermore, the IGZO FPD exhibited ∼27% increase in mid-frequency DQE (1 mm^-1 ) at matched pixel size and dose (EAK ≈ 1.0 μGy) and ∼11% increase after adjusting for differences in scintillator thickness. 2D spatial resolution was limited by the scintillator for each FPD. The IGZO-based FPD demonstrated improved 3D NEQ at all spatial frequencies in both head (≥25% increase for all dose levels) and body (≥10% increase for K w ${K_w}$ ≤2 mGy) imaging scenarios. These characteristics translated to improved low-contrast visualization in anthropomorphic phantoms, demonstrating ≥10% improvement in CNR and extension of the low-dose range for which the detector is input-quantum limited.

CONCLUSION

The IGZO-based FPD demonstrated improvements in electronic noise, image lag, and NEQ that translated to measurable improvements in 2D and 3D imaging performance compared to a conventional FPD based on a-Si:H TFTs. The improvements are most beneficial for 2D or 3D imaging scenarios involving low-dose and/or high-frame rate.

Monte Carlo simulations and phantom validation of low-dose radiotherapy to the lungs using an interventional radiology C-arm fluoroscope

PURPOSE

To use MC simulations and phantom measurements to investigate the dosimetry of a kilovoltage x-ray beam from an IR fluoroscope to deliver low-dose (0.3-1.0 Gy) radiotherapy to the lungs.

MATERIALS AND METHODS

PENELOPE was used to model a 125 kV, 5.94 mm Al HVL x-ray beam produced by a fluoroscope. The model was validated through depth-dose, in-plane/cross-plane profiles and absorbed dose at 2.5-, 5.1-, 10.2- and 15.2-cm depths against the measured beam in an acrylic phantom. CT images of an anthropomorphic phantom thorax/lungs were used to simulate 0.5 Gy dose distributions for PA, AP/PA, 3-field and 4-field treatments. DVHs were generated to assess the dose to the lungs and nearby organs. Gafchromic film was used to measure doses in the phantom exposed to PA and 4-field treatments, and compared to the MC simulations.

RESULTS

Depth-dose and profile results were within 3.2% and 7.8% of the MC data uncertainty, respectively, while dose gamma analysis ranged from 0.7 to 1.0. Mean dose to the lungs were 1.1-, 0.8-, 0.9-, and 0.8- Gy for the PA, AP/PA, 3-field, and 4-field after isodose normalization to cover ∼ 95% of each lung volume. Skin dose toxicity was highest for the PA and lowest for the 4-field, and both arrangements successfully delivered the treatment on the phantom. However, the dose distribution for the PA was highly non-uniform and produced skin doses up to 4 Gy. The dose distribution for the 4-field produced a uniform 0.6 Gy dose throughout the lungs, with a maximum dose of 0.73 Gy. The average percent difference between experimental and Monte Carlo values were -0.1% (range -3% to +4%) for the PA treatment and 0.3% (range -10.3% to +15.2%) for the 4-field treatment.

CONCLUSION

A 125 kV x-ray beam from an IR fluoroscope delivered through two or more fields can deliver an effective low-dose radiotherapy treatment to the lungs. The 4-field arrangement not only provides an effective treatment, but also significant dose sparing to healthy organs, including skin, compared to the PA treatment. Use of fluoroscopy appears to be a viable alternative to megavoltage radiation therapy equipment for delivering low-dose radiotherapy to the lungs.

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.

Spectral CT using a fine grid structure and varying x-ray incidence angle

PURPOSE

Interest in spectral computed tomography (CT) for diagnostics and therapy evaluation has been growing. Data acquisitions with distinct spectral sensitivities provide the ability to discriminate multiple materials, quantitative density estimates, and reduced artifacts due to energy dependencies. We introduce a novel spectral CT concept that includes a fine-pitch grid structure for prefiltration of the x-ray beam.

METHODS

We develop physical models for grid designs and illustrate the basic operating principles wherein small angulations of the incident x rays results significant filtration and spectral shaping of the beam. We fabricate a prototype grid with tungsten lamellae. We compare x-ray spectra induced by this filter as a function of incidence angle in both simulation students and in physical measurements. The grid is also integrated onto a CT test bench where we scanned an iodinated phantom with clinically relevant concentrations (5, 10, 20, and 50 mgI/mL) to demonstrate the ability to perform spectral CT acquisitions and material decomposition.

RESULTS

X-ray spectrometer measurements reveal diverse and controllable spectral shaping with small angle changes that are in agreement with simulation studies. Critical angles where the characteristics of the induced spectrum changes dramatically are identified. Reconstructions of projection data for two angulations separated by 2° was reconstructed and material decomposition into iodine and water images shows good agreement with the known iodine concentrations.

CONCLUSIONS

This work demonstrates the feasibility of the grid-based approach to enable spectral CT data acquisitions and accurate material decompositions. On-going and future studies will investigate the potential of this novel concept as a relatively simple upgrade to standard energy-integrating CT.

High-resolution model-based material decomposition in dual-layer flat-panel CBCT

PURPOSE

Spectral CT uses energy-dependent measurements that enable material discrimination in addition to reconstruction of structural information. Flat-panel detectors (FPDs) have been widely used in dedicated and interventional systems to deliver high spatial resolution, volumetric cone-beam CT (CBCT) in compact and OR-friendly designs. In this work, we derive a model-based method that facilitates high-resolution material decomposition in a spectral CBCT system equipped with a prototype dual-layer FPD. Through high-fidelity modeling of multilayer detector, we seek to avoid resolution loss that is present in more traditional processing and decomposition approaches.

METHOD

A physical model for spectral measurements in dual-layer flat-panel CBCT is developed including layer-dependent differences in system geometry, spectral sensitivities, and detector blur (e.g., due to varied scintillator thicknesses). This forward model is integrated into a model-based material decomposition (MBMD) method based on minimization of a penalized weighted least-squared (PWLS) objective function. The noise and resolution performance of this approach was compared with traditional projection-domain decomposition (PDD) and image-domain decomposition (IDD) approaches as well as one-step MBMD with lower-fidelity models that use approximated geometry, projection interpolation, or an idealized system geometry without system blur model. Physical studies using high-resolution three-dimensional (3D)-printed water-iodine phantoms were conducted to demonstrate the high-resolution imaging performance of the compared decomposition methods in iodine basis images and synthetic monoenergetic images.

RESULTS

Physical experiments demonstrate that the MBMD methods incorporating an accurate geometry model can yield higher spatial resolution iodine basis images and synthetic monoenergetic images than PDD and IDD results at the same noise level. MBMD with blur modeling can further improve the spatial-resolution compared with the decomposition results obtained with IDD, PDD, and MBMD methods with lower-fidelity models. Using the MBMD without or with blur model can increase the absolute modulation at 1.75 lp/mm by 10% and 22% compared with IDD at the same noise level.

CONCLUSION

The proposed model-based material decomposition method for a dual-layer flat-panel CBCT system has demonstrated an ability to extend high-resolution performance through sophisticated detector modeling including the layer-dependent blur. The proposed work has the potential to not only facilitate high-resolution spectral CT in interventional and dedicated CBCT systems, but may also provide the opportunity to evaluate different flat-panel design trade-offs including multilayer FPDs with mismatched geometries, scintillator thicknesses, and spectral sensitivities.

Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method

Purpose: The purpose of this study was to preliminarily estimate patient-specific organ doses in chest CT examinations for Chinese adults, and to investigate the effect of patient size on organ doses. Methods: By considering the body-size and body-build effects on the organ doses and taking the mid-chest water equivalent diameter (WED) as a body-size indicator, the chest scan images of 18 Chinese adults were acquired on a multi-detector CT to generate the regional voxel models. For each patient, the lungs, heart, and breasts (glandular breast tissues for both breasts) were segmented, and other organs were semi-automated segmented based on their HU values. The CT scanner and patient models simulated by MCNPX 2.4.0 software (Los Alamos National LaboratoryLos Alamos, USA) were used to calculate lung, breast, and heart doses. CTDIvol values were used to normalize simulated organ doses, and the exponential estimation model between the normalized organ dose and WED was investigated. Results: Among the 18 patients in this study, the simulated doses of lung, heart, and breast were 18.15 ± 2.69 mGy, 18.68 ± 2.87 mGy, and 16.11 ± 3.08 mGy, respectively. Larger patients received higher organ doses than smaller ones due to the higher tube current used. The ratios of lung, heart, and breast doses to the CTDIvol were 1.48 ± 0.22, 1.54 ± 0.20, and 1.41 ± 0.13, respectively. The normalized organ doses of all the three organs decreased with the increase in WED, and the normalized doses decreased more obviously in the lung and the heart than that in the breasts. Conclusions: The output of CT scanner under ATCM is positively related to the attenuation of patients, larger-size patients receive higher organ doses. The organ dose normalized by CTDIvol was negatively correlated with patient size. The organ doses could be estimated by using the indicated CTDIvol combined with the estimated WED.

Assessment of multi-energy inter-pixel coincidence counters for photon-counting detectors at the presence of charge sharing and pulse pileup: A simulation study

PURPOSE

Spectral distortion due to charge sharing (CS) and pulse pileup (PP) in photon-counting detectors (PCDs) degrades the quality of PCD data. We recently proposed multi-energy inter-pixel coincidence counters (MEICC) that provided spectral cross-talk information related to CS. When PP was absent, the normalized Cramér-Rao lower bounds (nCRLBs) of 225-µm pixel PCDs with MEICC was comparable to those of 450-µm pixel PCD without MEICC. The aim of this study was to assess the performance of PCDs with MEICC in the presence of both CS and PP using computer simulations.

METHODS

An in-house Monte Carlo program was modified to incorporate the following four temporal elements: (1) A pulse shape with a pulse duration of 20 ns, (2) delays of up to 10 ns in anode arrival times when photons were incident on pixel boundaries, (3) offsets proportional to a vertical separation between the primary and secondary charge clouds at the rate of ±4 ns per ±100 µm, and (4) a stochastic fluctuation of anode arrival times for all of the charge clouds with a standard deviation of 2 ns. We assessed the performance of five PCDs, (a)-(f), for three spectral tasks, (A)-(C): (a) The conventional PCD, (b) a PCD with MEICC, (c) a PCD with one coincidence counter (1CC), (d) a PCD with a 3 × 3 analog charge summing scheme (ACS), and (e) a PCD with a 3 × 3 digital count summing scheme (DCS); (A) conventional CT imaging with water (i.e., linear attenuation coefficient maps), (B) water-bone material decomposition, and (C) K-edge imaging with tungsten. The tube current was changed from 1 mA to 1000 mA and the nCRLB was assessed.

RESULTS

The recorded count rate curves were fitted by the non-paralyzable detection model with the effective deadtime parameter. The best fit was achieved by 25.8 ns for the conventional PCD, 18.6 ns for MEICC and 1CC, 140.5 ns for ACS, and 209.0 ns for DCS. The nCRLBs were strongly dependent on count rates. MEICC provided the best nCRLBs for all of the imaging tasks over the count rate range investigated except for a few conditions such as K-edge imaging at 1 mA. PP decreased the merit of MEICC over the conventional PCD in addressing CS. Nonetheless, MEICC consistently provided better nCRLBs than the conventional PCD did. The nCRLBs of MEICC were in the range of 49-58% of those of the conventional PCD for K-edge imaging, 45-76% for water-bone material decomposition, and 81-88% for the conventional CT imaging (i.e., linear attenuation coefficient maps). ACS provided better nCRLBs than the conventional PCD did only when the effect of PP was minor (e.g., when the counting efficiency of the conventional PCD was higher than 0.95 with the tube current of up to 100 mA).

CONCLUSION

Besides a few cases, MEICC provides the best nCRLBs for all of the tasks at all of the count rates. ACS and DCS provide better nCRLBs than the conventional PCD does only when count rates are very low.

Quantitative analysis of speckle-based X-ray dark-field imaging using numerical wave-optics simulations

The dark-field signal measures the small-angle scattering strength and provides complementary diagnostic information. This is of particular interest for lung imaging due to the pronounced small-angle scatter from the alveolar microstructure. However, most dark-field imaging techniques are relatively complex, dose-inefficient, and require sophisticated optics and highly coherent X-ray sources. Speckle-based imaging promises to overcome these limitations due to its simple and versatile setup, only requiring the addition of a random phase modulator to conventional X-ray equipment. We investigated quantitatively the influence of sample structure, setup geometry, and source energy on the dark-field signal in speckle-based X-ray imaging with wave-optics simulations for ensembles of micro-spheres. We show that the dark-field signal is accurately predicted via a model originally derived for grating interferometry when using the mean frequency of the speckle pattern power spectral density as the characteristic speckle size. The size directly reflects the correlation length of the diffuser surface and did not change with energy or propagation distance within the near-field. The dark-field signal had a distinct dependence on sample structure and setup geometry but was also affected by beam hardening-induced modifications of the visibility spectrum. This study quantitatively demonstrates the behavior of the dark-field signal in speckle-based X-ray imaging.

Technical Note: The nearest neighborhood-based approach for estimating basis line-integrals using photon-counting detector

PURPOSE

This study aims to develop a calibration-based estimator for the photon-counting detector (PCD)-based x-ray computed tomography.

METHODS

We propose the nearest neighborhood (NN)-based estimator, which searches for the nearest calibration data for a given PCD output and sets the associated basis line-integrals as the estimate. Searching for the nearest neighbors can be accelerated using the pre-calculated k-d tree for the data.

RESULTS

The proposed method is compared to the model-based maximum likelihood (ML) estimator. For slab phantom study, both ML and NN-based methods achieve the Cramér-Rao lower bound and are unbiased for various combinations of three basis materials (water, bone, and gold). The proposed method is also validated for K-edge imaging and presents almost unbiased Au concentrations in the region of interest.

CONCLUSIONS

The proposed NN-based method is demonstrated to be as accurate as the model-based ML estimator, but it is computationally efficient and requires only calibration measurements.

Technical Note: SpekPy v2.0-a software toolkit for modeling x-ray tube spectra

PURPOSE

SpekPy is a free toolkit for modeling x-ray tube spectra with the Python programming language. In this article, the advances in version 2.0 (v2) of the software are described, including additional target materials and more accurate modeling of the heel effect. Use of the toolkit is also demonstrated.

METHODS

The predictions of SpekPy are illustrated in comparison to experimentally determined spectra: three radiation quality reference (RQR) series tungsten spectra and one mammography spectrum with a molybdenum target. The capability of the software to correctly model changes in tube output with tube potential is also assessed, using the example of a GE Revolution^TM CT scanner (GE Healthcare, Waukesha, WI, USA) and specifications in the system's Technical Reference Manual. Furthermore, we note that there are several physics models available in SpekPy. These are compared on and off the central axis, to illustrate the differences.

RESULTS

SpekPy agrees closely with the experimental spectra over a wide range of tube potentials, both visually and in terms of first and second half-value layers (HVLs) (within 2% here). The CT scanner spectrum output (normalized to 120 kV tube potential) agreed within 4% over the range of 70 to 140 kV. The default physics model (casim) is adequate in most situations. The advanced option (kqp) should be used if high accuracy is desired for modeling the anode heel effect, as it fully includes the effects of bremsstrahlung anisotropy.

CONCLUSIONS

SpekPy v2 can reliably predict on- and off-axis spectra for tungsten and molybdenum targets. SpekPy's open-source MIT license allows users the freedom to incorporate this powerful toolkit into their own projects.

A Monte Carlo Platform for Characterization of X-Ray Radiation Dose in CT Imaging

Background

Computed tomography (CT) is currently known as a versatile imaging tool in the clinic used for almost all types of cancers. The major issue of CT is the health risk, belonging to X-ray radiation exposure. Concerning this, Monte Carlo (MC) simulation is recognized as a key computational technique for estimating and optimizing radiation dose. CT simulation with MCNP/MCNPX MC code has an inherent problem due to the lack of a fan-beam shaped source model. This limitation increases the run time and highly decreases the number of photons passing the body or phantom. Recently, a beta version of MCNP code called MCNP-FBSM (Fan-Beam Source Model) has been developed to pave the simulation way of CT imaging procedure, removing the need of the collimator. This is a new code, which needs to be validated in all aspects.

Objective

In this work, we aimed to develop and validate an efficient computational platform based on modified MCNP-FBSM for CT dosimetry purposes.

Material and Methods

In this experimental study, a setup is carried out to measure CTDI₁₀₀ in air and standard dosimetry phantoms. The accuracy of the developed MC CT simulator results has been widely benchmarked through comparison with our measured data, UK's National Health Service's reports (known as ImPACT), manufacturer's data, and other published results.

Results

The minimum and maximum observed mean differences of our simulation results and other above-mentioned data were the 1.5%, and 9.79%, respectively.

Conclusion

The developed FBSM MC computational platform is a beneficial tool for CT dosimetry.

Fast Monte Carlo codes for occupational dosimetry in interventional radiology

PURPOSE

Interventional radiology techniques cause radiation exposure both to patient and personnel. The radiation dose to the operator is usually measured with dosimeters located at specific points above or below the lead aprons. The aim of this study is to develop and validate two fast Monte Carlo (MC) codes for radiation transport in order to improve the assessment of individual doses in interventional radiology. The proposed methodology reduces the number of required dosemeters and provides immediate dose results.

METHODS

Two fast MC simulation codes, PENELOPE/penEasyIR and MCGPU-IR, have been developed. Both codes have been validated by comparing fast MC calculations with the multipurpose PENELOPE MC code and with measurements during a realistic interventional procedure.

RESULTS

The new codes were tested with a computation time of about 120 s to estimate operator doses while a standard simulation needs several days to obtain similar uncertainties. When compared with the standard calculation in simple set-ups, MCGPU-IR tends to underestimate doses (up to 5%), while PENELOPE/penEasyIR overestimates them (up to 18%). When comparing both fast MC codes with experimental values in realistic set-ups, differences are within 25%. These differences are within accepted uncertainties in individual monitoring.

CONCLUSION

The study highlights the fact that computational dosimetry based on the use of fast MC codes can provide good estimates of the personal dose equivalent and overcome some of the limitations of occupational monitoring in interventional radiology. Notably, MCGPU-IR calculates both organ doses and effective dose, providing a better estimate of radiation risk.