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

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 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.

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.

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.