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

Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT

PURPOSE

To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).

METHODS

This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV). Fully automated algorithms for quantifying CT number (HU) in abdominal fat (subcutaneous and visceral), skeletal muscle, bone, calcium (abdominal Agatston score), and organ size (area or volume) were applied. Biomarker median difference relative to 70 keV and interquartile range were reported by energy level to characterize variation. Linear regression was performed to calibrate non-70 keV data and to estimate their equivalent 70 keV biomarker attenuation values.

RESULTS

Relative to 70 keV, absolute median differences in attenuation-based biomarkers (excluding Agatston score) ranged 39-358, 12-102, 5-48, 9-75 HU for 40, 55, 85, 100 keV, respectively. For area-based biomarkers, differences ranged 6-15, 3-4, 2-7, 0-5 cm2 for 40, 55, 85, 100 keV. For volume-based biomarkers, differences ranged 12-34, 8-68, 12-52, 1-57 cm3 for 40, 55, 85, 100 keV. Agatston score behavior was more spurious with median differences ranging 70-204 HU. In general, VMI < 70 keV showed more variation in median biomarker measurement than VMI > 70 keV.

CONCLUSION

This study characterized the behavior of a fully automated AI CT biomarker toolkit across varying VMI levels obtained with DECT. The data showed relatively little biomarker value change when measured at or greater than 70 keV. Lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70 keV, a level considered equivalent to conventional 120 kVp exams.

Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits

Photon-counting computed tomography (PCCT) marks a significant advancement over conventional Energy-integrating detector CT systems. This review highlights PCCT's superior spatial and contrast resolution, reduced radiation dose, and multi-energy imaging capabilities, which address key challenges in radiotherapy, such as accurate tumor delineation, precise dose calculation, and treatment response monitoring. PCCT's improved anatomical clarity enhances tumor targeting while minimizing damage to surrounding healthy tissues. Additionally, Metal artifact reduction and quantitative imaging capabilities optimize workflows, enabling ART and radiomics-driven personalized treatment. Emerging clinical applications in brachytherapy and radiopharmaceutical therapy show promising outcomes, although challenges like high costs and limited software integration remain. With advancements in artificial intelligence and dedicated radiotherapy packages, PCCT is poised to transform precision, safety, and efficacy in cancer radiotherapy, marking it as a pivotal technology for future clinical practice.

Quantifying photon counting detector (PCD) performance using PCD-CT images

BACKGROUND

Photon counting detector CTs (PCD-CTs) have recently been introduced to clinical imaging. This development creates a new need for end-users to quantify and monitor the physical performance of PCDs. Traditionally, the characterization of PCD performance relied on detector counts, which are typically accessible to the manufacturer but are not usually available to clinical end-users.

PURPOSE

The goal of this work was to develop a new method for quantifying PCD performance using reconstructed PCD-CT images, without requiring access to the PCD counts.

METHODS

The proposed method is based on a set of closed-form relationships that connect PCD-CT image noise, the PCD deadtime ( τ $\tau$ ), and the zero-frequency detective quantum efficiency (DQE 0 ${\rm DQE}_0$ ) of PCDs. At a low tube current (mA) level, the mean output counts of the PCD were estimated by fitting the measured PCD-CT noise power spectrum (NPS) to a parametric model.DQE 0 ${\rm DQE}_0$ was then calculated by normalizing the estimated mean detector counts to the expected input x-ray photon number. To estimate τ $\tau$ , the image variance of PCD-CT was measured at different mA levels. A novel quantitative relationship between PCD-CT image variance, τ $\tau$ , and mA was employed to estimate τ $\tau$ through parametric fitting. The method was validated using both simulated and experimental PCD-CT data, covering a range of τ $\tau$ ,DQE 0 ${\rm DQE}_0$ , and system geometries.

RESULTS

For the simulated curved-detector PCD-CT, the estimation errors forDQE 0 ${\rm DQE}_0$ and deadtime were -3.7% and 0.5%, respectively. For the simulated collinear-detector PCD-CT, the estimation errors forDQE 0 ${\rm DQE}_0$ and deadtime were -3.3% and -1.0%, respectively. For the experimental collinear-detector PCD-CT, the estimation errors forDQE 0 ${\rm DQE}_0$ and deadtime were -2.6% and 1.6%, respectively.

CONCLUSIONS

By analyzing the variance and NPS of PCD-CT images,DQE 0 ${\rm DQE}_0$ and deadtime of scanner's PCD can be accurately estimated, without access to raw detector counts or projection data.

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 Detector CT Applications in Musculoskeletal Radiology

ABSTRACT

Photon-counting detectors (PCDs) have emerged as one of the most influential technical developments for medical imaging in recent memory. Surpassing conventional systems with energy-integrating detector technology in many aspects, PCD-CT scanners provide superior spatial resolution and dose efficiency for all radiological subspecialities. Demanding detailed display of trabecular microarchitecture and extensive anatomical coverage frequently within the same scan, musculoskeletal (MSK) imaging in particular can be a beneficiary of PCD-CT's remarkable performance. Since PCD-CT provides users with a plethora of customization options for both image acquisition and reconstruction, however, MSK radiologists need to be familiar with the scanner to unlock its full potential. From filter-based spectral shaping for artifact reduction over full field-of-view ultra-high-resolution scans to postprocessing of single- or dual-source multienergy data, almost every imaging task can be met with an optimized approach in PCD-CT. The objectives of this review were to give an overview of the most promising applications of PCD-CT in MSK imaging to date, to state current limitations, and to highlight directions for future research and developments.

Effect of Patient Positioning on CT Number Accuracy: A Phantom Study Comparing Energy Integrating and Deep Silicon Photon Counting Detector CT

OBJECTIVE

Patient positioning during clinical practice can be challenging, and mispositioning leads to a change in CT number. CT number fluctuation was assessed in single-energy (SE) EID, dual-energy (DE) EID, and deep silicon photon-counting detector (PCD) CT over water-equivalent diameter (WED) with different mispositions.

METHODS

A phantom containing five clinically relevant inserts (Mercury Phantom, Gammex) was scanned on a clinical EID CT and a deep silicon PCD CT prototype at vertical positions of 0, 4, 8, and 12 cm. EID scans used 120 kV and rapid kV-switching DE techniques. CT number was calculated for air, water, polystyrene, iodine 10 mg/mL, and bone. Ideal CT numbers were calculated using the NIST XCOM database toolkit. Comparison of measured to ideal CT number utilized relative root mean square error (RMSE). Trends in CT number versus WED were compared using linear regression and statistical comparisons to test for differences in slope.

RESULTS

No statistical difference of CT number with mispositioning was seen between acquisition modes. CT number fluctuation was larger due to WED than mispositioning for all material inserts. Water, iodine, and bone, for deep silicon PCD CT had statistically significant ( P  < 0.05) smaller slopes compared to EIDof CT number over WED for all tested mispositions. The accuracy of deep silicon PCD CT was higher than either SE or DE EID CT for all materials at all mispositions except for polystyrene.

CONCLUSIONS

WED (ie, patient size) contributes to CT number fluctuation more than mispositioning. The change in CT number was significantly smaller, and CT number accuracy was higher for deep silicon PCD CT in this phantom study.

Accuracy and consistency of effective atomic number over object size using deep silicon photon-counting detector CT

PURPOSE

Photon-counting detector (PCD) CT is the newest generation of CT detector technology. It is critical to characterize its performance in measuring important biomarkers used in quantitative CT including effective atomic number (Zeff). More accurate Zeff measurements could be beneficial in tissue classification and proton therapy tasks.

METHODS

A phantom of varying water-equivalent diameter (WED) containing clinically relevant inserts was scanned using a prototype deep silicon PCD CT and a dual-energy (DE) energy integrating detector (EID) CT. Zeff maps were generated. Measured Zeff values were compared across WEDs and to theoretical values.

RESULTS

The measured Zeff of the polystyrene, solid water, iodine, and bone (50% CaCO3) inserts differed from the theoretical value by a maximum of -14.0%, 4.6%, 8.4% and 13.0% respectively on EID vs 4.5%, 5.2%, 2.3% and 7.2% on PCD. The maximum variation in Zeff over the WED range on EID was 0.71, 0.27, 0.66, and 1.22 vs 0.47, 0.64, 0.1 and 0.22 on PCD for polystyrene, solid water, iodine, and bone (50% CaCO3) respectively.

CONCLUSION

This is the first study to evaluate Zeff measurements made using a prototype whole body PCD CT system. We found that PCD CT outperformed the EID CT in terms of Zeff accuracy and consistency over the WED range on most of the insert materials. Similarly, PCD CT outperformed most previous study's findings using EID CT. The high consistency and accuracy of measured Zeff using deep silicon PCD CT could make quantitative CT increasingly possible over a large range of patient sizes.

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.

Spatial Resolution Fidelity Comparison Between Energy Integrating and Deep Silicon Photon Counting CT: Implications for Pulmonary Imaging

PURPOSE

We investigated spatial resolution loss away from isocenter for a prototype deep silicon photon-counting detector (PCD) CT scanner and compare with a clinical energy-integrating detector (EID) CT scanner.

MATERIALS AND METHODS

We performed three scans on a wire phantom at four positions (isocenter, 6.7, 11.8, and 17.1 cm off isocenter). The acquisition modes were 120 kV EID CT, 120 kV high-definition (HD) EID CT, and 120 kV PCD CT. HD mode used double the projection view angles per rotation as the "regular" EID scan mode. The diameter of the wire was calculated by taking the full width of half max (FWHM) of a profile drawn over the radial and azimuthal directions of the wire. Change in wire diameter appearance was assessed by calculating the ratio of the radial and azimuthal diameter relative to isocenter. t tests were used to make pairwise comparisons of the wire diameter ratio with each acquisition and mean ratios' difference from unity.

RESULTS

Deep silicon PCD CT had statistically smaller ( P <0.05) changes in diameter ratio for both radial and azimuthal directions compared with both regular and HD EID modes and was not statistically different from unity ( P <0.05). Maximum increases in FWMH relative to isocenter were 36%, 12%, and 1% for regular EID, HD EID, and deep silicon PCD, respectively.

CONCLUSION

Deep silicon PCD CT exhibits less change in spatial resolution in both the radial and azimuthal directions compared with EID CT.

Chances and challenges of photon-counting CT in musculoskeletal imaging

In musculoskeletal imaging, CT is used in a wide range of indications, either alone or in a synergistic approach with MRI. While MRI is the preferred modality for the assessment of soft tissues and bone marrow, CT excels in the imaging of high-contrast structures, such as mineralized tissue. Additionally, the introduction of dual-energy CT in clinical practice two decades ago opened the door for spectral imaging applications. Recently, the advent of photon-counting detectors (PCDs) has further advanced the potential of CT, at least in theory. Compared to conventional energy-integrating detectors (EIDs), PCDs provide superior spatial resolution, reduced noise, and intrinsic spectral imaging capabilities. This review briefly describes the technical advantages of PCDs. For each technical feature, the corresponding applications in musculoskeletal imaging will be discussed, including high-spatial resolution imaging for the assessment of bone and crystal deposits, low-dose applications such as whole-body CT, as well as spectral imaging applications including the characterization of crystal deposits and imaging of metal hardware. Finally, we will highlight the potential of PCD-CT in emerging applications, underscoring the need for further preclinical and clinical validation to unleash its full clinical potential.

Approaches, advantages, and challenges to photon counting detector and multi-energy CT

Photon counting detector CT (PCD-CT) is the newest major development in CT technology and has been commercially available since 2021. It offers major technological advantages over current standard-of-care energy integrating detector CT (EID-CT) including improved spatial resolution, improved iodine contrast to noise ratio, multi-energy imaging, and reduced noise. This article serves as a foundational basis to the technical approaches and concepts of PCD-CT technology with primary emphasis on detector technology in direct comparison to EID-CT. The article also addresses current technological challenges to PCD-CT with particular attention to cross talk and its causes (e.g., Compton scattering, fluorescence, charge sharing, K-escape) as well as pile-up.

Standardizing technical parameters and terms for abdominopelvic photon-counting CT: laying the groundwork for innovation and evidence sharing

Photon-counting detector CT (PCD-CT) is a new technology that has multiple diagnostic benefits including increased spatial resolution, iodine signal, and radiation dose efficiency, as well as multi-energy imaging capability, but which also has unique challenges in abdominal imaging. The purpose of this work is to summarize key features, technical parameters, and terms, which are common amongst current abdominopelvic PCD-CT systems and to propose standardized terminology (where none exists). In addition, user-selectable protocol parameters are highlighted to facilitate both scientific evaluation and early clinical adoption. Unique features of PCD-CT systems include photon-counting detectors themselves, energy thresholds and bins, and tube potential considerations for preserved spectral separation. Key parameters for describing different PCD-CT systems are reviewed and explained. While PCD-CT can generate multi-energy images like dual-energy CT, there are new types of images such as threshold images, energy bin images, and special spectral images. The standardized terms and concepts herein build upon prior interdisciplinary consensus and have been endorsed by the newly created Society of Abdominal Radiology Photon-counting CT Emerging Technology Commission.

Photon-counting Detector CT for Liver Fat Quantification: Validation across Protocols in Metabolic Dysfunction-associated Steatotic Liver Disease

Background Traditional energy-integrating detector CT has limited utility in accurately quantifying liver fat due to protocol-induced CT value shifts, but this limitation can be addressed by using photon-counting detector (PCD) CT, which allows for a standardized CT value. Purpose To develop and validate a universal CT to MRI fat conversion formula to enhance fat quantification accuracy across various PCD CT protocols relative to MRI proton density fat fraction (PDFF). Materials and Methods In this prospective study, the feasibility of fat quantification was evaluated in phantoms with various nominal fat fractions. Five hundred asymptomatic participants and 157 participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) were enrolled between September 2023 and March 2024. Participants were randomly assigned to six groups with different CT protocols regarding tube voltage (90, 120, or 140 kVp) and radiation dose (standard or low). Of the participants in the 120-kVp standard-dose asymptomatic group, 51% (53 of 104) were designated as the training cohort, with the rest of the asymptomatic group serving as the validation cohort. A CT to MRI fat quantification formula was derived from the training cohort to estimate the CT-derived fat fraction (CTFF). CTFF agreement with PDFF and its error were evaluated in the asymptomatic validation cohort and subcohorts stratified by tube voltage, radiation dose, and body mass index, and in the MASLD cohort. The factors influencing CTFF error were further evaluated. Results In the phantoms, CTFF showed excellent agreement with nominal fat fraction (intraclass correlation coefficient, 0.98; mean bias, 0.2%). A total of 412 asymptomatic participants and 122 participants with MASLD were included. A CT to MRI fat conversion formula was derived as follows: MRI PDFF (%) = -0.58 · CT (HU) + 43.1. Across all comparisons, CTFF demonstrated excellent agreement with PDFF (mean bias values < 1%). CTFF error was not influenced by tube voltage, radiation dose, body mass index, or PDFF. Agreement between CTFF and PDFF was also found in the MASLD cohort (mean bias, -0.2%). Conclusion Standardized CT value from PCD CT showed a robust and remarkable agreement with MRI PDFF across various protocols and may serve as a precise alternative for liver fat quantification. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Wildman-Tobriner in this issue.

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.

Deep silicon photon-counting CT: A first simulation-based study for assessing perceptual benefits across diverse anatomies

OBJECTIVES

To assess perceptual benefits provided by the improved spatial resolution and noise performance of deep silicon photon-counting CT (Si-PCCT) over conventional energy-integrating CT (ECT) using polychromatic images for various clinical tasks and anatomical regions.

MATERIALS AND METHODS

Anthropomorphic, computational models were developed for lungs, liver, inner ear, and head-and-neck (H&N) anatomies. These regions included specific abnormalities such as lesions in the lungs and liver, and calcified plaques in the carotid arteries. The anatomical models were imaged using a scanner-specific CT simulation platform (DukeSim) modeling a Si-PCCT prototype and a conventional ECT system at matched dose levels. The simulated polychromatic projections were reconstructed with matched in-plane resolutions using manufacturer-specific software. The reconstructed pairs of images were scored by radiologists to gauge the task-specific perceptual benefits provided by Si-PCCT compared to ECT based on visualization of anatomical and image quality features. The scores were standardized as z-scores for minimizing inter-observer variability and compared between the systems for evidence of statistically significant improvement (one-sided Wilcoxon rank-sum test with a significance level of 0.05) in perceptual performance for Si-PCCT.

RESULTS

Si-PCCT offered favorable image quality and improved visualization capabilities, leading to mean improvements in task-specific perceptual performance over ECT for most tasks. The improvements for Si-PCCT were statistically significant for the visualization of lung lesion (0.08 ± 0.89 vs. 0.90 ± 0.48), liver lesion (-0.64 ± 0.37 vs. 0.95 ± 0.55), and soft tissue structures (-0.47 ± 0.90 vs. 0.33 ± 1.24) and cochlea (-0.47 ± 0.80 vs. 0.38 ± 0.62) in inner ear.

CONCLUSIONS

Si-PCCT exhibited mean improvements in task-specific perceptual performance over ECT for most clinical tasks considered in this study, with statistically significant improvement for 6/20 tasks. The perceptual performance of Si-PCCT is expected to improve further with availability of spectral information and reconstruction kernels optimized for high resolution provided by smaller pixel size of Si-PCCT. The outcomes of this study indicate the positive potential of Si-PCCT for benefiting routine clinical practice through improved image quality and visualization capabilities.