Principles and applications of multienergy CT: Report of AAPM Task Group 291

Spectral performance for iodine quantification of a dual-source, dual-kV photon counting detector CT

BACKGROUND

Multi-energy CT (MECT) enables quantification of material concentrations by measuring linear attenuation coefficient line integrals with multiple x-ray spectra. Photon counting detector (PCD)-CT utilizes a detector-based approach for MECT that can suffer from substantial spectral overlap, resulting in amplified material quantification noise. Dual-source dual-kV approaches for MECT are currently utilized in some energy-integrating detector (EID)-CT systems and can potentially be utilized with PCD-CT for improved spectral separation.

PURPOSE

To evaluate the iodine quantification performance of a novel dual-source (DS)-PCD-CT scan mode and compare to single-source (SS)-PCD-CT and DS-EID-CT.

MATERIALS AND METHODS

A 30 cm × 40 cm solid water phantom with four iodine inserts (2, 5, 10, and 15 mg/mL) was scanned with the three spectral modalities: SS-PCD-CT with two energy thresholds, DS-PCD-CT (90/Sn150 kV), and DS-EID-CT (90/Sn150 kV). For each modality, full-dose (12 mGy) and half-dose scans were acquired, and images were reconstructed with filtered back-projection using a quantitative (Qr40) kernel. When scanning in a DS configuration, the total radiation dose budget is split between two detectors, increasing the strength of a signal-dependent filter compared to a SS acquisition. To account for this effect, the modulation transfer function (MTF) for each modality was measured from a 0.05 mm tungsten wire. A linear spatial filter was applied to the SS images to match their MTF to that of the DS images. The resulting high- and low-energy images were input into an image-domain least squares material decomposition algorithm with iodine and water as the two basis materials. Iodine quantification accuracy and noise measured from the iodine basis images were used as figures of merit, and t-tests used to compare between modalities.

RESULTS

The 10% MTF cutoffs were 0.56, 0.57, 0.60, and 0.57 lp/mm for DS-EID-CT, DS-PCD-CT, SS-PCD-CT, and SS-PCD-CT after MTF-matching, respectively, with the SS-PCD-CT MTF cutoff dropping to 0.58 lp/mm at half-dose. Without accounting for the signal-dependent filter by matching the MTFs, the noise in iodine material basis images from SS-PCD-CT was 10% higher than that of DS-EID-CT. After matching the MTFs, the noise in the SS-PCD-CT iodine image was 9%-22% lower than that of the DS-EID-CT. The lowest iodine image noise was from the DS-PCD-CT, which was 39%-41% lower than the DS-EID-CT. The DS-PCD-CT noise magnitude was significantly different from the other modalities. Mean iodine quantification accuracy across all measured concentrations was within 5% for all modalities at full dose, but was only below 5% for the DS-PCD-CT at half-dose.

CONCLUSIONS

SS-PCD-CT with two energy thresholds outperformed DS-EID-CT in terms of image noise in iodine basis images when spatial resolution was matched. DS-PCD-CT gave the lowest noise due to the combination of PCD technology and improved spectral separation from the different x-ray spectra.

Quantification of iron in soft tissues through fast kV-switching dual-energy CT imaging: What calibration data are required?

PURPOSE

To provide recommendation on the type of calibration data needed for quantification of iron content in soft tissues through fast kV-switching dual-energy CT (DECT).

METHODS

Tissue-specific liquid surrogates mimicking human liver, spleen, kidney and muscles with iron concentration of 0-7 mg/ml were prepared and attached circumferentially to a 16-cm polymethylmethacrylate CT phantom. Soft tissue-equivalent gel boluses were employed to create different in size and configuration phantom-vials setups. Each phantom-vials setup was subjected to fast kV-switching DECT imaging with different acquisition protocols. The virtual iron concentration (VIC) in mg/ml was determined for each vial through the iron(water) material density images. VIC-to-true iron concentration (TIC) curves were derived for four phantom-vials setups and three different acquisition protocols. The applicability of derived VIC-to-TIC calibration data was tested in ten DECT examinations from our institution's archive.

RESULTS

A linear relationship between TIC and VIC values was observed for all tissue-surrogates and phantom-vials setups (R2 > 0.94). The VIC-to-TIC regression-lines derived for different tissues were found to differ significantly (p < 0.05). The regression-lines derived for the same tissue type, but different in size phantom-vials setups were also found to differ significantly (p < 0.05). The effects of DECT acquisition protocol and different vials positioning within the phantom-vials setup on derived regression-lines were found to be minor (p > 0.05).

CONCLUSIONS

Quantification of iron content through DECT imaging requires tissue- and patient size- specific calibration data. The presented DECT imaging-based method might be useful for monitoring iron levels in patients suspected of iron mis-regulation.

Influence of object size on beam hardening in dual energy images: A study using different dual-energy CT systems

INTRODUCTION

Dual-energy CT (DECT) enables material decomposition and artifact reduction. However, beam hardening effects, which vary by DECT system and object size, can impact measurement accuracy. This study investigates the influence of beam hardening across various DECT systems and object sizes.

METHODS

A polyethylene Mercury phantom with five diameters (16, 21, 26, 31, and 36 cm) was scanned using three DECT systems: fast kilovolt-switching CT (FKSCT), dual-source CT (DSCT), and dual-layer CT (DLCT). Measurements included CT numbers and standard deviations (SD) of virtual monochromatic images (VMI) at 70 keV for iodine inserts, iodine concentrations, and artifact indices (AI) to assess beam hardening artifacts.

RESULTS

CT numbers and iodine concentrations decreased with increasing phantom size for FKSCT and DLCT, with DLCT showing a larger decrease. DSCT exhibited relatively stable CT numbers and iodine concentrations across all sizes. Noise levels (SD) increased significantly with phantom size for DSCT and DLCT, while FKSCT showed a smaller increase. Beam hardening artifacts, as assessed by AI, were the lowest for FKSCT, while DSCT and DLCT exhibited greater artifacts compared to FKSCT, particularly at larger phantom sizes.

CONCLUSION

The effect of beam hardening varies among DECT systems. FKSCT demonstrated the most stable performance across object sizes, while DSCT and DLCT were more sensitive to object size, affecting measurement accuracy and stability. These findings emphasize the importance of understanding system-specific characteristics to ensure optimal DECT use.

IMPLICATIONS FOR PRACTICE

In clinical practice, when using DECT to measure CT numbers and iodine concentration, it is important to understand that the size of the object may be affected by beam hardening, depending on the DECT system.

Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study

Objectives: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. Methods: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. Results: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. Conclusions: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity.

Assessment of Photon-Counting Computed Tomography for Quantitative Imaging in Radiation Therapy

PURPOSE

To test a first-generation clinical photon-counting computed tomography (PCCT) scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes.

METHODS AND MATERIALS

A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear) was scanned on a NAEOTOM Alpha PCCT and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LACs) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via "Rho/Z"-algorithm was also used. Proton stopping-power ratios (SPRs) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations within the material regions were evaluated for LAC, RED/EAN, and SPR, respectively. Two example ray projections were also examined for LAC, SPR, and water-equivalent thickness, for illustrations of a more treatment-like scenario.

RESULTS

There was a tendency toward narrower distributions for PCCT compared with both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored root-mean-square deviations showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium, the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile, whereas the more standard projection showed similar results between the modalities.

CONCLUSIONS

This study shows promising results for tissue characterization in a human-like geometry for radiation therapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

Photon-counting versus energy-integrating CT of abdomen-pelvis: a phantom study on the potential for reducing iodine contrast media

BACKGROUND

To assess the potential of virtual monoenergetic images (VMIs) on a photon-counting computed tomography (PCCT) for reducing the amount of injected iodine contrast media compared to an energy-integrating CT (EICT).

METHODS

A multienergy phantom was scanned with a PCCT and EICT at 11 mGy with abdomen-pelvis examination parameters. VMIs were generated at 40 keV, 50 keV, 60 keV, and 70 keV. For all VMIs, the contrast-to-noise ratio (CNR) of iodine inserts with concentrations of 1 mg/mL, 2 mg/mL, 5 mg/mL, 10 mg/mL, and 15 mg/mL was calculated by dividing the signal difference between HU in iodine inserts versus solid water by the noise value assessed on solid water. The potential reduction in iodine media was calculated by the rate of reduction in iodine concentration with PCCT while maintaining the same CNR obtained with EICT for the reference concentration.

RESULTS

Significantly higher CNR values were found with PCCT at all VMI energy levels for iodine concentrations above 1 mg/mL. The highest reduction was observed at 40 keV, with a value of 48.9 ± 1.6% (mean ± standard deviation). It decreased as the energy level increased, by 38.5 ± 0.5%, and 30.8 ± 0.8% for 50 and 60 keV, respectively. For 70 keV, the potential reduction of 24.4 ± 1.1% was found for iodine concentrations above 1 mg/mL. This reduction reached 57 ± 2.3% at 40 keV with PCCT compared to 60 keV with EICT.

CONCLUSION

For abdomen-pelvis protocols, the use of VMIs with PCCT significantly improved the CNR of iodine, offering the potential to reduce the required contrast medium.

RELEVANCE STATEMENT

The use of VMIs with PCCT may reduce the quantity of iodine contrast medium to be injected compared with EICT, limiting costs, the risk of adverse effects, and the amount of contrast agent released into the wastewater.

KEY POINTS

PCCT improves the image quality of VMIs. PCCT offers the potential for reducing the amount of injected contrast medium. PCCT potential for reducing the injected contrast medium depends on energy level.

Multienergy cardiovascular CT imaging: current state and future

Multienergy cardiovascular CT imaging can be defined as data acquisition at 2 (dual-energy) or multiple X-ray energies. Multienergy cardiovascular CT imaging provides additional qualitative and quantitative information such as material maps or virtual monoenergetic images, which are supposed to further improve the quality and diagnostic yield of CT. Recently introduced photon-counting detector CT scanners further address some of the challenges and limitations of previous, conventional CT machines, hereby enhancing and extending the applications of CT for cardiovascular imaging. This review summarizes the technical principles of multienergy cardiovascular CT imaging and addresses the optimization of image quality and discusses the various dual-energy-based applications for coronary, valvular, and myocardial imaging. New developments in regard to k-edge imaging and new contrast media for multienergy cardiovascular CT imaging are being also discussed.

Contrast-enhanced photon-counting detector CT for discriminating local recurrence from postoperative changes after resection of pancreatic ductal adenocarcinoma

BACKGROUND

We evaluated the diagnostic capability of photon-counting detector computed tomography (PCD-CT) spectral variables in late arterial phase (LAP) and portal venous phase (PVP) to discriminate between local tumor recurrence (LTR) and postoperative changes (POC) after pancreatic ductal adenocarcinoma (PDAC) resection.

METHODS

Seventy-three consecutive PCD-CT scans in 73 patients with postoperative soft-tissue lesions (PSLs) were included, 42 with POC and 31 with LTR. Regions of interest were drawn in each PSL, and spectral variables were calculated: iodine concentration (IC), normalized IC (NIC), fat fraction, attenuation at 40, 70, and 90 keV, and slope of the spectral curve between 40-90 keV. Multivariable binary logistic regression models were constructed. Diagnostic performance was assessed for LAP and PVP using receiver operating characteristic analysis.

RESULTS

In LAP, all variables except fat fraction showed significant differences between LTR and POC (p ≤ 0.025). In PVP, all variables except NIC and fat fraction demonstrated significant differences between LTR and POC (p ≤ 0.005). Logistic regression analysis included NIC and 70 keV in the LAP-based model and IC and 90 keV in the PVP-based model. Both models achieved a higher area under the curve (AUC) than individual spectral variables in each phase. The LAP-based model achieved an AUC of 0.919 with 94% sensitivity, 84% specificity, and 87% accuracy, while the PVP-based model reached 0.820, 71%, 88%, and 81%, respectively.

CONCLUSION

Spectral variables from PCD-CT help distinguish between LTR and POC in LAP and PVP post-PDAC resection. Multivariable logistic regression improves diagnostic performance, especially in LAP.

RELEVANCE STATEMENT

Measuring normalized iodine concentration and attenuation at 70 keV in late arterial phase, or iodine concentration and attenuation at 90 keV in portal venous phase, and incorporating these values into a logistic regression model can help differentiate between local tumor recurrence and postoperative changes after pancreatic ductal adenocarcinoma resection.

KEY POINTS

Distinguishing recurrence from postoperative changes on CT after pancreatic ductal adenocarcinoma resection is challenging. PCD-CT spectral variable values differed significantly between local tumor recurrence (LTR) and postoperative changes (POC). Logistic regression of spectral variables can help distinguish LTR from POC. The late arterial phase-based model reached an AUC of 0.919 with 94% sensitivity and 84% specificity.

Misinterpretations about CT numbers, material decomposition, and elemental quantification

BACKGROUND

Quantitative CT imaging, particularly iodine and calcium quantification, is an important CT-based biomarker.

PURPOSE

This study quantifies sources of errors in quantitative CT imaging in both single-energy and spectral CT.

MATERIALS AND METHODS

This work examines the theoretical relationship between CT numbers, linear attenuation coefficient, and material quantification. We derive four understandings: (1) CT numbers are not proportional with element mass in vivo, (2) CT numbers are proportional with element mass only when contained in a voxel of pure water, (3) iodine-water material decomposition is never accurate in vivo, and (4) for error-free material decomposition a voxel must only consist of the basis decomposition vectors. Misinterpretation-based errors are calculated using the National Institute of Standards and Technology (NIST) XCOM database for: tissue chemical compositions, clinical concentrations of hydroxyapatite (HAP), and iodine. Quantification errors are also demonstrated experimentally using phantoms.

RESULTS

In single-energy CT, misinterpretation-induced errors for HAP density in adipose, muscle, lung, soft tissue, and blood ranged from 0-132%, i.e., a mass error of 0-749 mg/cm3. In spectral CT, errors with iodine in the same tissues resulted in a range of < 0.1-33% error, resulting in a mass error of < 0.1-1.2 mg/mL.

CONCLUSION

Our work demonstrates material quantification is fundamentally limited when measured in vivo due to measurement conditions differing from assumed and the errors are at or above detection limits for bone mineral density (BMD) and spectral iodine quantification. To define CT-derived biomarkers, the errors we demonstrate should either be avoided or built into uncertainty bounds.

CLINICAL RELEVANCE STATEMENT

Improving error bounds in quantitative CT biomarkers, specifically in iodine and BMD quantification, could lead to improvements in clinical care aspects based on quantitative CT.

KEY POINTS

CT numbers are only proportional with element mass only when contained in a voxel of pure water, therefore iodine-water material decomposition is never accurate in vivo. Misinterpretation-induced errors ranged from 0-132% for HAP density and < 0.1-33% in spectral CT with iodine. For error-free material decomposition, a voxel must only consist of the basis decomposition vectors.

Photon-counting CT systems: A technical review of current clinical possibilities

In recent years, computed tomography (CT) has undergone a number of developments to improve radiological care. The most recent major innovation has been the development of photon-counting detectors. By comparison with the energy-integrating detectors traditionally used in CT, these detectors offer better dose efficiency, eliminate electronic noise, improve spatial resolution and have intrinsic spectral sensitivity. These detectors also allow the energy of each photon to be counted, thus improving the sampling of the X-ray spectrum in multiple energy bins, to better distinguish between photoelectric and Compton attenuation coefficients, resulting in better spectral images and specific color K-edge images. The purpose of this article was to make the reader more familiar with the basic principles and techniques of new photon-counting CT systems equipped with photon-counting detectors and also to describe the currently available devices that could be used in clinical practice.

Quantitative multi-energy CT in oncology: State of the art and future directions

Multi-energy computed tomography (CT) involves acquisition of two or more CT measurements with distinct energy spectra. Using the differential attenuation of tissues and materials at different X-ray energies, multi-energy CT allows distinction of tissues and materials. Multi-energy technology encompasses different types of CT systems, such as dual-energy CT and photon-counting CT, that can use information from the energy and type of material present in acquired images to create multiple datasets. These scanners have overcome many of the limitations of conventional CT, making it possible to improve the diagnostic performance of CT and expand its use to new applications through better tissue characterization and multiple quantitative parameters. Quantitative imaging biomarkers based on multi-energy CT have enormous potential in oncologic imaging, from the diagnosis and characterization of tumor phenotypes to the evaluation of the response to treatment. Nevertheless, implementing these techniques in clinical practice remains challenging. This article reviews the basic principles underlying multi-energy CT and the most recent technical developments in these systems together with their advantages and limitations to establish the value of quantitative imaging derived from multi-energy CT in the field of oncology.

Predictive value of gemstone spectral imaging for chemotherapy response in colorectal cancer liver metastases: A retrospective study

Background

Accurate and timely assessment of tumor response after chemotherapy is crucial in clinical settings. The aim of this study was to explore the feasibility of Gemstone Spectral Imaging (GSI) for early assessment of chemotherapy responses in patients with colorectal cancer liver metastasis (CRCLM).

Materials and Methods

From October 2012 to October 2018, 46 patients (28 males and 18 females) with CRCLM received GSI followed by chemotherapy were retrospectively reviewed. The patients were divided into a response group (n = 32) and a nonresponse group (n = 14) according to the tumor response to chemotherapy. The iodine concentration images and virtual monoenergetic images (VMIs) with an optimal contrast-to-noise ratio at the arterial phase (AP) and portal venous phase (PVP) were obtained by GSI viewer. The iodine concentration value and computed tomography (CT) value on VMIs and slope of spectral attenuation curves of all lesions were compared. A logistic regression analysis was used to determine the predictor of chemotherapy response.

Results

The difference of extrahepatic metastasis (P = 0.001), CT value on 68 keV VMIs at the AP (P = 0.005) and PVP (P = 0.001), slope of CT value attenuation curves at the AP (P = 0.013) and PVP (P = 0.001), and iodine concentration value at PVP (P = 0.003) between the response and nonresponse groups were statistically significant. The CT value of the 68 keV VMIs (OR: 1.206; 95% confidence interval [CI]: 1.021-1.425, P = 0.027) and the iodine concentration value at PVP (OR: 1.952; 95% CI: 1.034-3.684, P = 0.039) were independent prognostic factors for predicting chemotherapy response.

Conclusion

Baseline GSI may help predict the response to chemotherapy and provide a good tumor-response indicator through single-energy CT value of 68 keV at the PVP and iodine concentration.

Impact of image formation factors on material discrimination in spectral CT

Objective.The accuracy of material decomposition in spectral computed tomography (CT) depends on the information quality captured in image acquisition, a factor that cannot be adequately assessed using conventional image quality metrologies due to the multi-energy nature of spectral CT. This work used metrologies specific to spectral CT to evaluate the impact of acquisition conditions on the quality of spectral CT images and accuracy of material decomposition techniques.Approach.Computational phantoms were created with cylindrical shapes and variable sizes (20-40 cm), containing inserts of iodine and gadolinium (1-8 mg ml-1). The phantoms were imaged using a validated CT simulator modeling a clinical photon-counting CT scanner. The acquisitions were done at different detector energy thresholds (50-90 keV) and tube currents (25-250 mAs). The images were used to develop and train a data-driven material identification and quantification algorithm. Two spectral metrologies, multivariate contrast-to-noise ratio (CNR) and separability index, were used to characterize the impact of energy threshold, tube current, phantom size, and material concentration on signal quality. The results were interpreted in terms of figures of merit of accuracy for classification and mean absolute error (MAE) and root mean squared error (RMSE) for regression.Main results. Signal quality for iodine and gadolinium was maximized with a low energy threshold, high tube current, and small phantom size. While conventional CNR terms predicted variable image quality for two-thirds of all conditions, multivariate CNR was above 10 for half of those. Separability index showed that for a phantom size greater than 30 cm, a minimum of 75-110 mAs is required to separate 2 mg ml-1of iodine and gadolinium. For both classification and regression tasks, a random forest model with a local statistics dataset provided the best performance. Across conditions, classification performance was 0.66-0.99 for I accuracy, 0.72-0.99 for Gd accuracy. Regression performance was 0.02-0.91 mg ml-1I and 0.02-0.59 mg ml-1Gd for MAE and 0.11-1.08 mg ml-1I and 0.07-0.76 mg ml-1Gd for RMSE.Significance.Multivariate CNR and separability index metrologies can predict material decomposition performance. Theses metrics demonstrated that the decomposition of iodine and gadolinium have higher separability when the acquisition is done at a lower energy threshold, with a higher tube current, and when the imaged object has a smaller size. Object size had the largest impact on metrics and decomposition performance.

The Best of Both Worlds: Ultra-high-pitch Pulmonary Angiography with Free-Breathing Technique by Means of Photon-Counting Detector CT for Diagnosis of Acute Pulmonary Embolism

RATIONALE AND OBJECTIVES

To assess image quality and radiation dose of ultra-high-pitch CT pulmonary angiography (CTPA) with free-breathing technique for diagnosis of pulmonary embolism using a photon-counting detector (PCD) CT compared to matched energy-integrating detector (EID)-based single-energy CTPA.

MATERIALS AND METHODS

Fifty-one PCD-CTPAs were prospectively compared to 51 CTPAs on a third-generation dual-source EID-CT. CTPAs were acquired with an ultra-high-pitch protocol with free-breathing technique (40 mL contrast medium, pitch 3.2) at 140 kV (PCD) and 70-100 kV (EID). Iodine maps were reconstructed from spectral PCD-CTPAs. Image quality of CTPAs and iodine maps was assessed independently by three radiologists. Additionally, CT attenuation numbers within pulmonary arteries as well as signal-to-noise and contrast-to-noise ratios (SNR, CNR) were compared. Administered radiation dose was compared.

RESULTS

CT attenuation was higher in the PCD-group (all P < 0.05). CNR and SNR were higher in lobar pulmonary arteries in PCD-CTPAs (P < 0.05), whereas no difference was ascertained within the pulmonary trunk (P > 0.05). Image quality of PCD-CTPA was rated best by all readers (excellent/good image quality in 96.1% of PCD-CTPAs vs. 50.9% of EID-CTPAs). PCD-CT produced no non-diagnostic scans vs. three non-diagnostic (5.9%) EID-CTPAs. Radiation dose was lower with PCD-CT than with EID-CT (effective dose 1.33 ± 0.47 vs. 1.80 ± 0.82 mSv; all P < 0.05).

CONCLUSION

Ultra-high-pitch CTPA with free-breathing technique with PCD-CT allows for superior image quality with significantly reduced radiation dose and full spectral information. With the ultra-high pitch, only PCD-CTPA enables reconstruction of iodine maps containing additional functional information.

Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT

Purpose

Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence. Even if accurate spectral correction is done, multi-material separation within a volume remains a challenge. Increasing the number of energy bins in material decomposition problems often comes with a significant noise penalty but with minimal decomposition benefits.

Approach

We begin with an empirical spectral correction method executed in the tomographic domain that accounts for distortions in estimated spectral attenuation for each voxel. This is followed by our proposed iterative clustering material decomposition (ICMD) where clustering of voxels is used to reduce the number of basis materials to be resolved for each cluster. Using a larger number of energy bins for the clustering step shows distinct advantages in excellent classification to a larger number of clusters with accurate cluster centers when compared with the National Institute of Standards and Technology attenuation values. The decomposition step is applied to each cluster separately where each cluster has fewer basis materials compared with the entire volume. This is shown to reduce the need for the number of energy bins required in each decomposition step for the clusters. This approach significantly increases the total number of materials that can be decomposed within the volume with high accuracy and with excellent noise properties.

Results

Utilizing a (cadmium telluride 1-mm-thick sensor) Medipix detector with a 55 - μ m pitch, we demonstrate the quantitatively accurate decomposition of several materials in a phantom study, where the sample includes mixtures of soft materials such as water and poly-methyl methacrylate along with contrast-enhancing materials. We show improved accuracy and lower noise when all five energy bins were used to yield effective classification of voxels into multiple accurate fundamental clusters which was followed by the decomposition step applied to each cluster using just two energy bins. We also show an example of biological sample imaging and separating three distinct types of tissue in mice: muscle, fat, and bone. Our experimental results show that the combination of effective and practical spectral correction and high-dimensional data clustering enhances decomposition accuracy and reduces noise in micro-CT.

Conclusions

This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.

Technical note: A GPU-based shared Monte Carlo method for fast photon transport in multi-energy x-ray exposures

BACKGROUND

The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time.

PURPOSE

This work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures.

METHODS

The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases.

RESULTS

The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation.

CONCLUSIONS

The proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.

Multivendor comparison of quantification accuracy of effective atomic number by Dual-Energy CT: A phantom study

PURPOSE

Our study aimed to compare the accuracy of the effective atomic number (Zeff) of five dual-energy CT (DECT) from three vendors and different generations under different scanning parameters.

METHODS

Zeff accuracy of five DECT scanners with twelve tube voltage configurations was evaluated by using the TomoTherapy cheese phantom. The potential dose dependence of the Zeff was investigated using three radiation dose (5, 15, and 25 mGy), and the robustness of Zeff was simulated for different organs of the body by placing the inserts at different positional depths. Bias and mean absolute percentage error (MAPE) were used to characterize the accuracy of Zeff. Data underwent analysis using one-way ANOVA, followed by the Turky and LSD post hoc tests, simple linear regression, and linear mixed models.

RESULTS

All tube voltage configurations had a bias of less than 1. Dual layer detector DECT (dl-DECT) -140 kV has the lowest MAPE (1.79 %±1.93 %). The third generation dual source DECT (ds-DECT) and the second generation rapid switch DECT (rs-DECT) have higher MAPE than their predecessor DECT. The results of the linear mixed model showed that tube voltage configuration (F=16.92, p < 0.001) and insert type (F=53.26, p < 0.001) significantly affect the MAPE. In contrast, radiation dose only has a significant effect on the MAPE of rs-DECT. The inserts position does not affect the final MAPE.

CONCLUSION

When scanning different inserts, Zeff accuracy varies by vendor and DECT generation. Of all the scanners, dl-DECT had the highest Zeff accuracy. Upgrading DECT generation doesn't lead to higher accuracy, or even lower.

Exploring dual energy CT synthesis in CBCT-based adaptive radiotherapy and proton therapy: application of denoising diffusion probabilistic models

Background.Adaptive radiotherapy (ART) requires precise tissue characterization to optimize treatment plans and enhance the efficacy of radiation delivery while minimizing exposure to organs at risk. Traditional imaging techniques such as cone beam computed tomography (CBCT) used in ART settings often lack the resolution and detail necessary for accurate dosimetry, especially in proton therapy.Purpose.This study aims to enhance ART by introducing an innovative approach that synthesizes dual-energy computed tomography (DECT) images from CBCT scans using a novel 3D conditional denoising diffusion probabilistic model (DDPM) multi-decoder. This method seeks to improve dose calculations in ART planning, enhancing tissue characterization.Methods.We utilized a paired CBCT-DECT dataset from 54 head and neck cancer patients to train and validate our DDPM model. The model employs a multi-decoder Swin-UNET architecture that synthesizes high-resolution DECT images by progressively reducing noise and artifacts in CBCT scans through a controlled diffusion process.Results.The proposed method demonstrated superior performance in synthesizing DECT images (High DECT MAE 39.582 ± 0.855 and Low DECT MAE 48.540± 1.833) with significantly enhanced signal-to-noise ratio and reduced artifacts compared to traditional GAN-based methods. It showed marked improvements in tissue characterization and anatomical structure similarity, critical for precise proton and radiation therapy planning.Conclusions.This research has opened a new avenue in CBCT-CT synthesis for ART/APT by generating DECT images using an enhanced DDPM approach. The demonstrated similarity between the synthesized DECT images and ground truth images suggests that these synthetic volumes can be used for accurate dose calculations, leading to better adaptation in treatment planning.

AAPM Task Group Report 299: Quality control in multi-energy computed tomography

Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial differences between single-energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

Simultaneous iodine and barium imaging with photon-counting CT

Objective.The objective of this study is to explore the capabilities of photon-counting computed tomography (PCCT) in simultaneously imaging and differentiating materials with close atomic numbers, specifically barium (Z= 56) and iodine (Z= 53), which is challenging for conventional computed tomography (CT).Approach.Experiments were conducted using a bench-top PCCT system equipped with a cadmium zinc telluride detector. Various phantom setups and contrast agent concentrations (1%-5%) were employed, along with a biological sample. Energy thresholds were tuned to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed using K-edge subtraction and principal component analysis (PCA) techniques to differentiate and quantify the contrast agents.Main results.The PCCT system successfully differentiated and accurately quantified barium and iodine in both phantom combinations and a biological sample, achieving high correlations (R2≈1) between true and reconstructed concentrations. PCA outperformed K-edge subtraction, particularly in the presence of calcium, by providing superior differentiation between barium and iodine.Significance.This study demonstrates the potential of PCCT for reliable, detailed imaging in both clinical and research settings, particularly for contrast agents with similar atomic numbers. The results suggest that PCCT could offer significant improvements in imaging quality over conventional CT, especially in applications requiring precise material differentiation.

Dual-Energy CTA Iodine Map Reconstructions Improve Visualization of Residual Cerebral Aneurysms following Endovascular Coiling

BACKGROUND AND PURPOSE

Material-specific reconstructions of dual-energy CTA (DECTA) can highlight iodinated contrast, subtract predefined materials, and reduce metal artifact. We present a technique to improve detection of residual aneurysms after endovascular coiling by which iodine-map DECTA (IM-DECTA) reconstructions subtract platinum coil artifacts in MIP images (MIP IM-DECTA) and assess if IM-DECTA offers improved detection over conventional CTA (CCTA) or monoenergetic DECTA.

MATERIALS AND METHODS

We included consecutive patients who underwent endovascular aneurysm coiling with follow-up DECTA and DSA within 24 months. DECTA was performed at 80- and 150-kVp tube voltages on a rapid kV-switching single-source Revolution scanner. CCTA and IM-DECTA series were reconstructed. Reference-standard DSA was compared with CCTA, 50- and 70-keV virtual monochromatic DECTA, IM-DECTA, and MIP IM-DECTA. Blinded to DSA data, cross-section images were reviewed in consensus by 3 neurointerventionalists for residual aneurysms and assigned modified Raymond-Roy classifications (mRRC). Sensitivity, specificity, and accuracy of each series is reported relative to DSA, and single-factor ANOVA and pair-wise Spearman correlation coefficients compared the accuracy of each series. Readers provided ROI measurements of HU deviation adjacent to the aneurysm neck for quantitative noise assessment and qualitatively scored each series on a 3-point Likert-style scale ranging from uninterpretable to excellent image quality.

RESULTS

Twenty-one patients with 25 coiled aneurysms were included. Mean time from DECTA to DSA was 286 ± 212 days. IM-DECTA and MIP IM-DECTA most sensitively (89% and 90%) and specifically (93% and 93%) detected residual aneurysms relative to CCTA (6% and 86%). Relative to DSA, IM-DECTA and MIP IM-DECTA most accurately detected (92% versus 28% for CCTA) and classified residual aneurysms by mRRC (ρC-CTA = -0.08; ρIM = 0.50; ρIM-MIP = 0.55; P < .001). Reader consensus reported the best image quality at the aneurysm neck with IM-DECTA and MIP IM-DECTA, with 56% of CCTAs considered uninterpretable versus 0% of IM-DECTAs, and image noise was significantly lower for IM-DECTA (27.9 ± 3.6 HU) or MIP IM-DECTA (26.8 ± 3.5 HU) than CCTA (103.2 ± 13.3 HU; P < .001).

CONCLUSIONS

MIP IM-DECTA can subtract coil mass artifact and is more sensitive and specific than CCTA for the detection of residual aneurysms after endovascular coiling.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography

BACKGROUND

Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images.

METHODS

To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images.

RESULTS

To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network.

CONCLUSION

Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

CT-based synthetic iodine map generation using conditional denoising diffusion probabilistic model

BACKGROUND

Iodine maps, derived from image-processing of contrast-enhanced dual-energy computed tomography (DECT) scans, highlight the differences in tissue iodine intake. It finds multiple applications in radiology, including vascular imaging, pulmonary evaluation, kidney assessment, and cancer diagnosis. In radiation oncology, it can contribute to designing more accurate and personalized treatment plans. However, DECT scanners are not commonly available in radiation therapy centers. Additionally, the use of iodine contrast agents is not suitable for all patients, especially those allergic to iodine agents, posing further limitations to the accessibility of this technology.

PURPOSE

The purpose of this work is to generate synthetic iodine map images from non-contrast single-energy CT (SECT) images using conditional denoising diffusion probabilistic model (DDPM).

METHODS

One-hundered twenty-six head-and-neck patients' images were retrospectively investigated in this work. Each patient underwent non-contrast SECT and contrast DECT scans. Ground truth iodine maps were generated from contrast DECT scans using commercial software syngo.via installed in the clinic. A conditional DDPM was implemented in this work to synthesize iodine maps. Three-fold cross-validation was conducted, with each iteration selecting the data from 42 patients as the test dataset and the remainder as the training dataset. Pixel-to-pixel generative adversarial network (GAN) and CycleGAN served as reference methods for evaluating the proposed DDPM method.

RESULTS

The accuracy of the proposed DDPM was evaluated using three quantitative metrics: mean absolute error (MAE) (1.039 ± 0.345 mg/mL), structural similarity index measure (SSIM) (0.89 ± 0.10) and peak signal-to-noise ratio (PSNR) (25.4 ± 3.5 db) respectively. Compared to the reference methods, the proposed technique showcased superior performance across the evaluated metrics, further validated by the paired two-tailed t-tests.

CONCLUSION

The proposed conditional DDPM framework has demonstrated the feasibility of generating synthetic iodine map images from non-contrast SECT images. This method presents a potential clinical application, which is providing accurate iodine contrast map in instances where only non-contrast SECT is accessible.

Dual-energy computed tomography for predicting cervical lymph node metastasis in laryngeal squamous cell carcinoma

Rationale and objectives

We constructed a dual-energy computed tomography (DECT)-based model to assess cervical lymph node metastasis (LNM) in patients with laryngeal squamous cell carcinoma (LSCC).

Materials and methods

We retrospectively analysed 164 patients with LSCC who underwent preoperative DECT from May 2019 to May 2023. The patients were randomly divided into training (n = 115) and validation (n = 49) cohorts. Quantitative DECT parameters of the primary tumours and their clinical characteristics were collected. A logistic regression model was used to determine independent predictors of LNM, and a nomogram was constructed along with a corresponding online model. Model performance was assessed using the area under the curve (AUC) and the calibration curve, and the clinical value was evaluated using decision curve analysis (DCA).

Results

In total, 64/164 (39.0 %) patients with LSCC had cervical LNM. Independent predictors of LNM included normalized iodine concentration in the arterial phase (odds ratio [OR]: 8.332, 95 % confidence interval [CI]: 2.813-24.678, P < 0.001), normalized effective atomic number in the arterial phase (OR: 5.518, 95 % CI: 1.095-27.818, P = 0.002), clinical T3-4 stage (OR: 5.684, 95 % CI: 1.701-18.989, P = 0.005), and poor histological grade (OR: 5.011, 95 % CI: 1.003-25.026, P = 0.049). These predictors were incorporated into the DECT-based nomogram and the corresponding online model, showing good calibration and favourable performance (training AUC: 0.910, validation AUC: 0.918). The DCA indicated a significant clinical benefit of the nomogram for estimating LNM.

Conclusions

DECT parameters may be useful independent predictors of LNM in patients with LSCC, and a DECT-based nomogram may be helpful in clinical decision-making.

CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model

Objective.The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients.Approach.A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans.Main Results.The performance of the C-DDPM was evaluated using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results showed MAE values of 27.37±3.35 Hounsfield Units (HU) for high-energy CT (H-CT) and 24.57±3.35HU for low-energy CT (L-CT), SSIM values of 0.74±0.22 for H-CT and 0.78±0.22 for L-CT, and PSNR values of 18.51±4.55 decibels (dB) for H-CT and 18.91±4.55 dB for L-CT.Significance.The study demonstrates the efficacy of the deep learning model in producing high-quality synthetic CE-DECT images, which significantly benefits radiation therapy planning. This approach provides a valuable alternative imaging solution for facilities lacking DECT scanners and for patients who are unsuitable for iodine contrast imaging, thereby enhancing the reach and effectiveness of advanced imaging in cancer treatment planning.

Dual-energy computed tomography iodine quantification combined with laboratory data for predicting microvascular invasion in hepatocellular carcinoma: a two-centre study

OBJECTIVES

Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction.

METHODS

This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score.

RESULTS

Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively.

CONCLUSIONS

The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively.

ADVANCES IN KNOWLEDGE

Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.

Stereotactic Body Radiation Therapy (SBRT) in prostate cancer in the presence of hip prosthesis - is it a contraindication? A narrative review

Hip replacement is a common orthopedic surgery in the aging population. With the rising incidence of prostate cancer, metallic hip prosthetics can cause considerable beam hardening and streak artifacts, leading to difficulty in identifying the target volumes and planning process for radiation treatment. The growing use of Stereotactic Body Radiation Therapy (SBRT) to treat prostate cancer is now well established. However, the use of this treatment modality in the presence of a hip prosthesis is poorly understood. There is enough literature on planning for external beam radiation treatment without any difficulties in the presence of hip prosthesis with conventional or Hypofractionated treatment. However, there is a shortage of literature on the impact of the prosthesis in SBRT planning, and there is a need for further understanding and measures to mitigate the obstacles in planning for SBRT in the presence of hip prosthesis. We present our review of the intricacies that need to be understood while considering SBRT in the presence of hip prostheses in prostate cancer treatment.

Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography

Objective.With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset.Approach.The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml-1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data.Main Results.Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms.Signficance.In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography

OBJECTIVE

To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems.

METHOD

A phantom with two iodine inserts (1 and 2 mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4 cm for first generation and 8 cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50  versus 150 keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported.

RESULTS

An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2 cm). With increased collimation (4 cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8 cm collimation width was comparable to a 4 cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45  and 0.33 mg/mL for the first and second-generation DLCT, respectively).

CONCLUSION

The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Photon-Counting Computed Tomography: Experience in Musculoskeletal Imaging

Since the emergence of the first photon-counting computed tomography (PCCT) system in late 2021, its advantages and a wide range of applications in all fields of radiology have been demonstrated. Compared to standard energy-integrating detector-CT, PCCT allows for superior geometric dose efficiency in every examination. While this aspect by itself is groundbreaking, the advantages do not stop there. PCCT facilitates an unprecedented combination of ultra-high-resolution imaging without dose penalty or field-of-view restrictions, detector-based elimination of electronic noise, and ubiquitous multi-energy spectral information. Considering the high demands of orthopedic imaging for the visualization of minuscule details while simultaneously covering large portions of skeletal and soft tissue anatomy, no subspecialty may benefit more from this novel detector technology than musculoskeletal radiology. Deeply rooted in experimental and clinical research, this review article aims to provide an introduction to the cosmos of PCCT, explain its technical basics, and highlight the most promising applications for patient care, while also mentioning current limitations that need to be overcome.

Development of a separability index for task specific characterization of spectral computed tomography

PURPOSE

In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions.

METHOD

Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated.

RESULTS

Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability.

CONCLUSION

The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

Coronary calcium scoring on virtual non-contrast and virtual non-iodine reconstructions compared to true non-contrast images using photon-counting computed tomography

OBJECTIVES

To compare coronary artery calcification (CAC) scores measured on virtual non-contrast (VNC) and virtual non-iodine (VNI) reconstructions computed from coronary computed tomography angiography (CCTA) using photon-counting computed tomography (PCCT) to true non-contrast (TNC) images.

METHODS

We included 88 patients (mean age = 59 years ± 13.5, 69% male) who underwent a TNC coronary calcium scan followed by CCTA on PCCT. VNC images were reconstructed in 87 patients and VNI in 88 patients by virtually removing iodine from the CCTA images. For all reconstructions, CAC scores were determined, and patients were classified into risk categories. The overall agreement of the reconstructions was analyzed by Bland-Altman plots and the level of matching classifications.

RESULTS

The median CAC score on TNC was 27.8 [0-360.4] compared to 8.5 [0.2-101.6] (p < 0.001) on VNC and 72.2 [1.3-398.8] (p < 0.001) on VNI. Bland-Altman plots depicted a bias of 148.8 (ICC = 0.82, p < 0.001) and - 57.7 (ICC = 0.95, p < 0.001) for VNC and VNI, respectively. Of all patients with CACTNC = 0, VNC reconstructions scored 63% of the patients correctly, while VNI scored 54% correctly. Of the patients with CACTNC > 0, VNC and VNI reconstructions detected the presence of coronary calcium in 90% and 92% of the patients. CACVNC tended to underestimate CAC score, whereas CACVNI overestimated, especially in the lower risk categories. According to the risk categories, VNC misclassified 55% of the patients, while VNI misclassified only 32%.

CONCLUSION

Compared to TNC images, VNC underestimated and VNI overestimated the actual CAC scores. VNI reconstructions quantify and classify coronary calcification scores more accurately than VNC reconstructions.

CLINICAL RELEVANCE STATEMENT

Photon-counting CT enables spectral imaging, which might obviate the need for non-contrast enhanced coronary calcium scoring, but optimization is necessary for the clinical implementation of the algorithms.

KEY POINTS

• Photon-counting computed tomography uses spectral information to virtually remove the signal of contrast agents from contrast-enhanced scans. • Virtual non-contrast reconstructions tend to underestimate coronary artery calcium scores compared to true non-contrast images, while virtual non-iodine reconstructions tend to overestimate the calcium scores. • Virtual non-iodine reconstructions might obviate the need for non-contrast enhanced calcium scoring, but optimization is necessary for the clinical implementation of the algorithms.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Utility of Dual-Energy Computed Tomography in Clinical Conundra

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

PURPOSE

To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC).

METHODS

Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model.

RESULTS

Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695).

CONCLUSION

The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Spectral performance evaluation of a second-generation spectral detector CT

PURPOSE

The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities.

METHODS

A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported.

RESULTS

Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively.

CONCLUSION

The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Multi-energy blended CBCT spectral imaging and scatter-decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS)

BACKGROUND

Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity.

PURPOSE

The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections.

METHODS

To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method.

RESULTS

Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values (E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, theE RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively.

CONCLUSIONS

We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

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.

Material decomposition maps based calibration of dual energy CT scanners for proton therapy planning: a phantom study

We introduce a new calibration method for dual energy CT (DECT) based on material decomposition (MD) maps, specifically iodine and water MD maps. The aim of this method is to provide the first DECT calibration based on MD maps. The experiments were carried out using a general electric (GE) revolution CT scanner with ultra-fast kV switching and used a density phantom by GAMMEX for calibration and evaluation. The calibration process involves several steps. First, we tested the ability of MD values to reproduce Hounsfield unit (HU) values of single energy CT (SECT) acquisitions and it was found that the errors were below 1%, validating their use for HU reproduction. Next, the different definitions of computedZvalues were compared and the robustness of the approach based on the materials' composition was confirmed. Finally, the calibration method was compared with a previous method by Bourqueet al, providing a similar level of accuracy and superior performance in terms of precision. Overall, this novel DECT calibration method offers improved accuracy and reliability in determining tissue-specific physical properties. The resulting maps can be valuable for proton therapy treatments, where precise dose calculations and accurate tissue differentiation are crucial for optimal treatment planning and delivery.

Assignment of the biological value of solid breast masses based on quantitative evaluations of spectral CT examinations using electron density mapping, Zeffective mapping and iodine mapping

OBJECTIVE

We aimed to asses, in a clinical setting, whether the newly available quantitative evaluation of electron density (ED) in spectral CT examinations of the breast provide information on the biological identity of solid breast masses and whether ED maps yield added value to the diagnostic information of iodine maps and Zeff maps calculated from the same CT image datasets.

METHODS

All patients at the University Breast Cancer Center who underwent a clinically indicated Dual Layer Computed Tomography (DLCT) examination for staging of invasive breast cancer from 2018 to 2020 were prospectively included. Iodine concentration maps, Zeff maps and ED maps were automatically reconstructed from the DLCT datasets. Region of interest (ROI) based evaluations in the breast target lesions and in the aorta were performed semi-automatically in identical anatomical positions using dedicated evaluation software. Case-by-case evaluations were carried independently by 2 of 4 radiologists for each examination, respectively. Statistical analysis derived from the ROIs was done by calculating ROC/AUC curves and Youden indices.

RESULTS

The evaluations comprised 166 DLCT examinations. In the ED maps the measurements in the breast target lesions yielded Youden cutpoints of 104.0% (reader 1) and 103.8% (reader 2) resulting in AUCs of 0.63 and 0.67 at the empirical cutpoints. The variables "Zeff" and "iodine content" derived from the target lesions showed superior diagnostical results, with a Youden cutpoint of 8.0 mg/ml in the iodine maps and cutpoints of 1.1/1.2 in the Zeff maps the AUCs ranging from 0.84 to 0.85 (p = 0.023 to <0.000). The computational combination of Zeff and ED measurements in the target lesions yielded a slight AUC increase (readers 1: 0.85-0.87; readers 2: 0.84-0.94). The ratios of the measured values in the target lesions normalized to the values measured in the aorta showed comparable results. The AUCs of ED derived from the cutpoints showed inferior results to those derived from the Zeff maps and iodine maps (ED: 0.64 and 0.66 for reader 1 and 2; Zeff: 0.86 for both readers; iodine content: 0.89 and 0.86 for reader 1 and 2, respectively). The computational combination of the ED results and the Zeff measurements did not lead to a clinically relevant diagnostic gain with AUCs ranging from 0.86 to 0.88.

CONCLUSIONS

Quantitative assessments of Zeff, iodine content and ED all targeting the physical and chemical aspects of iodine uptake in solid breast masses confirmed diagnostically robust cutpoints for the differentiation of benign and malignant findings (Zeff < 7.7, iodine content of <0.8 mg/ml). The evaluations of the ED did not indicate any added diagnostic value beyond the quantitative assessments of Zeff and iodine content. Further research is warranted to develop suitable clinical indications for the use of ED maps.

Prediction of proton stopping power ratios using dual-energy CT basis material decomposition

BACKGROUND

Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction.

PURPOSE

To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.

METHODS

A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups.

RESULTS

The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR.

CONCLUSIONS

The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

Dual-energy computed tomography quality control: Initial experiences with a semi-automatic analysis tool

PURPOSE

A quality control (QC) system for dual-energy CT (DECT) was developed. The scope of the QC system was to monitor both the constancy of the CT images and the software used in calculating the DECT derived maps. Longitudinal analysis was based on a standard imaging protocol, a commercial multi-energy phantom, and a semi-automatic analysis tool.

METHODS

The phantom consisted of an elliptical body section with round slots for interchangeable inserts. It was scanned with 90kVp/Sn150kVp, automatic tube current modulation, and 9.6 mGy CTDIvol. From the two conventional CT images, scanner manufacturer's software was used to provide virtual monoenergetic images at two different energies, effective atomic number (Zeff) maps, and iodine concentration maps. The images were analyzed using an open-source tool allowing user-selected statistics of interest. The means and standard deviations of the phantom background and the iodine, calcium, and water inserts were recorded. The QC tool is available at github.com/tomakela/dectqatool.

RESULTS

The obtained results were generally highly consistent over time, except for the smaller diameter iodine inserts. A small change inZeff was observed after a DECT software update. The developed QC tool aided the analysis robustness: the segmentations were modifiable when needed, and small rotations or air bubbles in the water insert were easily corrected.

CONCLUSION

The developed QC system provided easy-to-use workflow for constancy measurements. A small deviation due to change in the post-processing was detected. The proposed imaging protocol and analysis steps, and the reported measurement variations can aid in determining action levels for DECT QC.

Quantitative multi-energy micro-CT: A simulation and phantom study for simultaneous imaging of four different contrast materials using an energy integrating detector

Emerging from the development of single-energy Computed Tomography (CT) and Dual-Energy Computed Tomography, Multi-Energy Computed Tomography (MECT) is a promising tool allowing advanced material and tissue decomposition and thereby enabling the use of multiple contrast materials in preclinical research. The scope of this work was to evaluate whether a usual preclinical micro-CT system is applicable for the decomposition of different materials using MECT together with a matrix-inversion method and how different changes of the measurement-environment affect the results. A matrix-inversion based algorithm to differentiate up to five materials (iodine, iron, barium, gadolinium, residual material) by applying four different acceleration voltages/energy levels was established. We carried out simulations using different ratios and concentrations (given in fractions of volume units, VU) of the four different materials (plus residual material) at different noise-levels for 30 keV, 40 keV, 50 keV, 60 keV, 80 keV and 100 keV (monochromatic). Our simulation results were then confirmed by using region of interest-based measurements in a phantom-study at corresponding acceleration voltages. Therefore, different mixtures of contrast materials were scanned using a micro-CT. Voxel wise evaluation of the phantom imaging data was conducted to confirm its usability for future imaging applications and to estimate the influence of varying noise-levels, scattering, artifacts and concentrations. The analysis of our simulations showed the smallest deviation of 0.01 (0.003-0.15) VU between given and calculated concentrations of the different contrast materials when using an energy-combination of 30 keV, 40 keV, 50 keV and 100 keV for MECT. Subsequent MECT phantom measurements, however, revealed a combination of acceleration voltages of 30 kV, 40 kV, 60 kV and 100 kV as most effective for performing material decomposition with a deviation of 0.28 (0-1.07) mg/ml. The feasibility of our voxelwise analyses using the proposed algorithm was then confirmed by the generation of phantom parameter-maps that matched the known contrast material concentrations. The results were mostly influenced by the noise-level and the concentrations used in the phantoms. MECT using a standard micro-CT combined with a matrix inversion method is feasible at four different imaging energies and allows the differentiation of mixtures of up to four contrast materials plus an additional residual material.

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD).Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score.Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic.Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.

Quantitative dual-energy computed tomography with cesium as a novel contrast agent for localization of thermochemical ablation in phantoms and ex vivo models

BACKGROUND

Thermochemical ablation (TCA) is a minimally invasive therapy under development for hepatocellular carcinoma. TCA simultaneously delivers an acid (acetic acid, AcOH) and base (sodium hydroxide, NaOH) directly into the tumor, where the acid/base chemical reaction produces an exotherm that induces local ablation. However, AcOH and NaOH are not radiopaque, making monitoring TCA delivery difficult.

PURPOSE

We address the issue of image guidance for TCA by utilizing cesium hydroxide (CsOH) as a novel theranostic component of TCA that is detectable and quantifiable with dual-energy CT (DECT).

MATERIALS AND METHODS

To quantify the minimum concentration of CsOH that can be positively identified by DECT, the limit of detection (LOD) was established in an elliptical phantom (Multi-Energy CT Quality Assurance Phantom, Kyoto Kagaku, Kyoto, Japan) with two DECT technologies: a dual-source system (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and a split-filter, single-source system (SOMATOM Edge, Siemens Healthineers). The dual-energy ratio (DER) and LOD of CsOH were determined for each system. Cesium concentration quantification accuracy was evaluated in a gelatin phantom before quantitative mapping was performed in ex vivo models.

RESULTS

On the dual-source system, the DER and LOD were 2.94 and 1.36-mM CsOH, respectively. For the split-filter system, the DER and LOD were 1.41- and 6.11-mM CsOH, respectively. The signal on cesium maps in phantoms tracked linearly with concentration (R2  = 0.99) on both systems with an RMSE of 2.56 and 6.72 on the dual-source and split-filter system, respectively. In ex vivo models, CsOH was detected following delivery of TCA at all concentrations.

CONCLUSIONS

DECT can be used to detect and quantify the concentration of cesium in phantom and ex vivo tissue models. When incorporated in TCA, CsOH performs as a theranostic agent for quantitative DECT image-guidance.

Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method

Background

The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients. The proton dose calculation uncertainty of this approach is 2.5%-3.5% plus 1 mm margin. SECT is the major clinical modality for proton therapy treatment planning. It would be intriguing to enhance proton dose calculation accuracy using a deep learning (DL) approach centered on SECT.

Objectives

The purpose of this work is to develop a deep learning method to generate mass density and relative stopping power (RSP) maps based on clinical single-energy CT (SECT) data for proton dose calculation in proton therapy treatment.

Methods

Artificial neural networks (ANN), fully convolutional neural networks (FCNN), and residual neural networks (ResNet) were used to learn the correlation between voxel-specific mass density, RSP, and SECT CT number (HU). A stoichiometric calibration method based on SECT data and an empirical model based on dual-energy CT (DECT) images were chosen as reference models to evaluate the performance of deep learning neural networks. SECT images of a CIRS 062M electron density phantom were used as the training dataset for deep learning models. CIRS anthropomorphic M701 and M702 phantoms were used to test the performance of deep learning models.

Results

For M701, the mean absolute percentage errors (MAPE) of the mass density map by FCNN are 0.39%, 0.92%, 0.68%, 0.92%, and 1.57% on the brain, spinal cord, soft tissue, bone, and lung, respectively, whereas with the SECT stoichiometric method, they are 0.99%, 2.34%, 1.87%, 2.90%, and 12.96%. For RSP maps, the MAPE of FCNN on M701 are 0.85%, 2.32%, 0.75%, 1.22%, and 1.25%, whereas with the SECT reference model, they are 0.95%, 2.61%, 2.08%, 7.74%, and 8.62%.

Conclusion

The results show that deep learning neural networks have the potential to generate accurate voxel-specific material property information, which can be used to improve the accuracy of proton dose calculation.

Advances in knowledge

Deep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.

Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices.

Dual-Energy, Spectral and Photon Counting Computed Tomography for Evaluation of the Gastrointestinal Tract

The use of dual-energy computed tomography (CT) allows for reconstruction of energy- and material-specific image series. The combination of low-energy monochromatic images, iodine maps, and virtual unenhanced images can improve lesion detection and disease characterization in the gastrointestinal tract in comparison with single-energy CT.

Dual-energy head cone-beam CT using a dual-layer flat-panel detector: Hybrid material decomposition and a feasibility study

BACKGROUND

Flat panel detector (FPD) based cone-beam computed tomography (CT) has made tremendous progress in the last two decades, with many new and advanced medical and industrial applications keeping emerging from diagnostic imaging and image guidance for radiotherapy and interventional surgery. The current cone-beam CT (CBCT), however, is still suboptimal for head CT scan which requires a high standard of image quality. While the dual-layer FPD technology is under extensive development and is promising to further advance CBCT from qualitative anatomic imaging to quantitative dual-energy CT, its potential of enabling head CBCT applications has not yet been fully investigated.

PURPOSE

The relatively moderate energy separation from the dual-layer FPD and the overall low signal level especially at the bottom-layer detector, could raise significant challenges in performing high-quality dual-energy material decomposition (MD). In this work, we propose a hybrid, physics and model guided, MD algorithm that attempts to fully use the detected x-ray signals and prior-knowledge behind head CBCT using dual-layer FPD.

METHODS

Firstly, a regular projection-domain MD is performed as initial results of our approach and for comparison as conventional method. Secondly, based on the combined projection, a dual-layer multi-material spectral correction (dMMSC) is applied to generate beam hardening free images. Thirdly, the dMMSC corrected projections are adopted as a physics-model based guidance to generate a hybrid MD. A set of physics experiments including fan-beam scan and cone-beam scan using a head phantom and a Gammex Multi-Energy CT phantom are conducted to validate our proposed approach.

RESULTS

The combined reconstruction could reduce noise by about 10% with no visible resolution degradation. The fan-beam studies on the Gammex phantom demonstrated an improved MD performance, with the averaged iodine quantification error for the 5-15 mg/ml iodine inserts reduced from about 5.6% to 3.0% by the hybrid method. On fan-beam scan of the head phantom, our proposed hybrid MD could significantly reduce the streak artifacts, with CT number nonuniformity (NU) in the selected regions of interest (ROIs) reduced from 23 Hounsfield Units (HU) to 4.2 HU, and the corresponding noise suppressed from 31 to 6.5 HU. For cone-beam scan, after scatter correction (SC) and cone-beam artifact reduction (CBAR), our approach can also significantly improve image quality, with CT number NU in the selected ROI reduced from 24.2 to 6.6 HU and the noise level suppressed from 22.1 to 8.2 HU.

CONCLUSIONS

Our proposed physics and model guided hybrid MD for dual-layer FPD based head CBCT can significantly improve the robustness of MD and suppress the low-signal artifact. This preliminary feasibility study also demonstrated that the dual-layer FPD is promising to enable head CBCT spectral imaging.

CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT

BACKGROUND. Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. OBJECTIVE. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers. METHODS. A phantom with polyethylene rings of varying sizes (mimicking patient sizes) as well as inserts of different materials was scanned on an EID CT scanner in single-energy (SE) mode (120-kV images) and in rapid-kilovoltage-switching dual-energy (DE) mode (70-keV images) and on a prototype deep silicon PCD CT scanner (70-keV images). ROIs were placed to measure the CT numbers of the materials. Slopes of CT number as a function of object size were computed. Materials' ideal CT number at 70 keV was computed using the National Institute of Standards and Technology XCOM Photon Cross Sections Database. The root mean square error (RMSE) between measured and ideal numbers was calculated across object sizes. RESULTS. Slope (expressed as Hounsfield units per centimeter) was significantly closer to zero (i.e., less variation in CT number as a function of size) for PCD CT than for SE EID CT for air (1.2 vs 2.4 HU/cm), water (-0.3 vs -1.0 HU/cm), iodine (-1.1 vs -4.5 HU/cm), and bone (-2.5 vs -10.1 HU/cm) and for PCD CT than for DE EID CT for air (1.2 vs 2.8 HU/cm), water (-0.3 vs -1.0 HU/cm), polystyrene (-0.2 vs -0.9 HU/cm), iodine (-1.1 vs -1.9 HU/cm), and bone (-2.5 vs -6.2 HU/cm) (p < .05). For all tested materials, PCD CT had the smallest RMSE, indicating CT numbers closest to ideal numbers; specifically, RMSE (expressed as Hounsfield units) for SE EID CT, DE EID CT, and PCD CT was 32, 44, and 17 HU for air; 7, 8, and 3 HU for water; 9, 10, and 4 HU for polystyrene; 31, 37, and 13 HU for iodine; and 69, 81, and 20 HU for bone, respectively. CONCLUSION. For numerous materials, deep silicon PCD CT, in comparison with SE EID CT and DE EID CT, showed lower CT number variability as a function of size and CT numbers closer to ideal numbers. CLINICAL IMPACT. Greater reliability of CT numbers for PCD CT is important given the dependence of diagnostic pathways on CT numbers.