Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

Opportunistic Bone Mineral Density Measurement Using Photon-Counting Detector CT Spectral Localizer Images: A Prospective Study.

Background: Spectral localizer images from photon-counting detector (PCD) CT can be used for bone mineral density (BMD) evaluation given their 2D-projectional nature and material decomposition capability. As all CT examinations include localizer images, this approach could allow opportunistic osteoporosis screening in patients undergoing clinically indicated imaging by PCD CT. Objective: To assess the utility of PCD-CT spectral localizer images for opportunistic derivation of area BMD (aBMD) values and T-scores, using dual-energy X-ray absorptiometry (DXA) as the reference standard. Methods: This prospective study included patients ≥18 years old scheduled for clinically indicated lumbar spine CT between October 2023 and February 2024 and who underwent DXA within the 13 prior months or were scheduled for DXA within the subsequent 13 months. Participants underwent lumbar spine CT by PCD CT including spectral localizer images. Lumbar spine aBMD was extracted from clinical DXA reports. ROIs were placed on lumbar vertebral bodies and background soft tissues to extract areal densities from spectral localizer images using material decomposition; areal densities were used to derive lumbar spine aBMD values. The aBMD values were used to derive T-scores, which were classified as representing normal (≥-1) or abnormal (<-1) bone mass. DXA-derived and PCD-CT derived measurements were compared. Results: The study included 51 participants (mean age: 62 years [range, 28-83 years]; 31 female, 20 male). Mean DXA-derived T-score was 0.39±1.64; mean PCD-CT derived T-score was 0.28±1.77 (p=.29). Lin's concordance correlation coefficient between DXA-derived and PCD-CT T-scores was 0.90. The difference between DXA-derived and PCD-CT derived T-scores showed a small correlation with patient age (r=-0.13), absolute interval between DXA and PCD CT (r=.15), and BMI (r=0.28); this difference in scores did not show a significant difference between male and female patients (0.08 vs 0.13, respectively; p=.81). PCD-CT T-scores had sensitivity of 97%, specificity of 71%, PPV of 90%, and NPV of 91% for detecting abnormal bone mass using DXA-derived T-scores as the reference standard. Conclusion: PCD-CT spectral localizers showed clinical utility for deriving aBMD values and, consequently, T-scores. Clinical Impact: The T-score derived from PCD-CT spectral localizers may serve as an opportunistic screening tool for low bone mass and osteoporosis.

Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.

OBJECTIVE: To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM). MATERIALS AND METHODS: WBLDCTs of MM patients performed within a year of diagnosis were included. Cytogenetic assessments of clonal plasma cells via fluorescent in situ hybridization (FISH) were used to risk-stratify patients as high-risk (HR) or standard-risk (SR). Presence of any of del(17p), t(14;16), t(4;14), and t(14;20) on FISH was defined as HR. The dataset was evenly divided into five groups (folds) at the individual patient level for model training. Mean and standard deviation (SD) of the area under the receiver operating curve (AUROC) across the folds were recorded. RESULTS: One hundred fifty-one patients with MM were included in the study. The model performed best for t(4;14), mean (SD) AUROC of 0.874 (0.073). The lowest AUROC was observed for trisomies: AUROC of 0.717 (0.058). Two- and 5-year survival rates for HR cytogenetics were 87% and 71%, respectively, compared to 91% and 79% for SR cytogenetics. Survival predictions by the WBLDCT deep learning model revealed 2- and 5-year survival rates for patients with HR cytogenetics as 87% and 71%, respectively, compared to 92% and 81% for SR cytogenetics. CONCLUSION: A deep learning model trained on WBLDCT scans predicted the presence of cytogenetic abnormalities used for risk stratification in MM. Assessment of the model's performance revealed good to excellent classification of the various cytogenetic abnormalities.

Quantitative metrics of bone quality determined at the distal radius using photon-counting CT.

OBJECTIVE: To determine the accuracy of photon-counting-detector CT (PCD-CT) at deriving bone morphometric indices and demonstrate utility in vivo in the distal radius. METHODS: Ten cadaver wrists were scanned using PCD-CT and high-resolution peripheral quantitative CT (HRpQCT). Correlation between PCD-CT and HRpQCT morphometric indices was determined. Agreement was assessed by Lin's concordance correlation coefficient (Lin's CCC). Wrist PCD-CTs of patients between 02/2022 and 08/2023 were also evaluated for clinical utility. Morphometric indices of the in vivo distal radii were extracted and compared between patients with or without osteoporosis. RESULTS: In cadavers, strong correlation between PCD-CT and HRpQCT was observed for cortical thickness (Spearman correlation, ρ, 0.85), trabecular spacing (ρ = 0.98), and trabecular bone volume fraction (ρ = 0.68). Moderate negative correlation (ρ = - 0.49) was observed for trabecular thickness. PCD-CT shows good agreement to HRpQCT for cortical thickness, trabecular spacing, and trabecular bone volume fraction (Lin's CCC = 0.80, 0.94, and 0.86, respectively) but poor agreement (Lin's CCC = - 0.1) for trabecular thickness. In forty participants (31 adults and 9 pediatric), bone morphometrics indices for cortical thickness, trabecular thickness, trabecular spacing, and trabecular bone volume fraction were 0.99 mm (IQR, 0.89-1.06), 0.38 mm (IQR, 0.25-0.40), 0.82 mm (IQR, 0.72-1.05), and 0.28 (IQR, 0.25-0.33), respectively. Patients with osteoporosis had statistically significantly larger trabecular spacing (p = 0.025) and lower trabecular volumetric bone mineral density (p = 0.042). CONCLUSION: This study demonstrates the agreement of PCD-CT to HRpQCT in cadavers of most cortical and bone morphometrics examined and provide in vivo quantitative metrics of bone microarchitecture from routine clinical PCD-CT images of the distal radius.

Sarcopenia identified by computed tomography imaging using a deep learning-based segmentation approach impacts survival in patients with newly diagnosed multiple myeloma.

BACKGROUND: Sarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning-based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value. METHODS: Sarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae. These skeletal muscle measurements were further normalized by the height of the patient to obtain the skeletal muscle index for each patient to classify them as sarcopenic or not. RESULTS: The study cohort consisted of 322 patients of which 67 (28%) were categorized as having high risk (HR) fluorescence in situ hybridization (FISH) cytogenetics. A total of 171 (53%) patients were sarcopenic based on their peri-diagnosis standard-dose CT scan. The median overall survival (OS) and 2-year mortality rate for sarcopenic patients was 44 months and 40% compared to 90 months and 18% for those not sarcopenic, respectively (p < .0001 for both comparisons). In a multivariable model, the adverse prognostic impact of sarcopenia was independent of International Staging System stage, age, and HR FISH cytogenetics. CONCLUSIONS: Sarcopenia identified by a machine learning-based convolutional neural network algorithm significantly affects OS in patients with NDMM. Future studies using this machine learning-based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

Quantifying Uncertainty in Deep Learning of Radiologic Images.

In recent years, deep learning (DL) has shown impressive performance in radiologic image analysis. However, for a DL model to be useful in a real-world setting, its confidence in a prediction must also be known. Each DL model's output has an estimated probability, and these estimated probabilities are not always reliable. Uncertainty represents the trustworthiness (validity) of estimated probabilities. The higher the uncertainty, the lower the validity. Uncertainty quantification (UQ) methods determine the uncertainty level of each prediction. Predictions made without UQ methods are generally not trustworthy. By implementing UQ in medical DL models, users can be alerted when a model does not have enough information to make a confident decision. Consequently, a medical expert could reevaluate the uncertain cases, which would eventually lead to gaining more trust when using a model. This review focuses on recent trends using UQ methods in DL radiologic image analysis within a conceptual framework. Also discussed in this review are potential applications, challenges, and future directions of UQ in DL radiologic image analysis.

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT (P < .05 for all comparisons). The 0.6-mm PCD CT images with convolutional neural network denoising also demonstrated improvement in viewing all four pathologic abnormalities and detected one or more lytic lesions in 21 of 27 participants compared with the 2-mm EID CT images (P < .001). Conclusion Ultra-high-resolution photon-counting detector CT improved the visibility of multiple myeloma lesions relative to energy-integrating detector CT. © RSNA, 2022 Online supplemental material is available for this article.

Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions.

CT is one of the most widely used modalities for musculoskeletal imaging. Recent advancements in the field include the introduction of four-dimensional CT, which captures a CT image during motion; cone-beam CT, which uses flat-panel detectors to capture the lower extremities in weight-bearing mode; and dual-energy CT, which operates at two different x-ray potentials to improve the contrast resolution to facilitate the assessment of tissue material compositions such as tophaceous gout deposits and bone marrow edema. Most recently, photon-counting CT (PCCT) has been introduced. PCCT is a technique that uses photon-counting detectors to produce an image with higher spatial and contrast resolution than conventional multidetector CT systems. In addition, postprocessing techniques such as three-dimensional printing and cinematic rendering have used CT data to improve the generation of both physical and digital anatomic models. Last, advancements in the application of artificial intelligence to CT imaging have enabled the automatic evaluation of musculoskeletal pathologies. In this review, the authors discuss the current state of the above CT technologies, their respective advantages and disadvantages, and their projected future directions for various musculoskeletal applications.

Clinical applications of photon counting detector CT.

The X-ray detector is a fundamental component of a CT system that determines the image quality and dose efficiency. Until the approval of the first clinical photon-counting-detector (PCD) system in 2021, all clinical CT scanners used scintillating detectors, which do not capture information about individual photons in the two-step detection process. In contrast, PCDs use a one-step process whereby X-ray energy is converted directly into an electrical signal. This preserves information about individual photons such that the numbers of X-ray in different energy ranges can be counted. Primary advantages of PCDs include the absence of electronic noise, improved radiation dose efficiency, increased iodine signal and the ability to use lower doses of iodinated contrast material, and better spatial resolution. PCDs with more than one energy threshold can sort the detected photons into two or more energy bins, making energy-resolved information available for all acquisitions. This allows for material classification or quantitation tasks to be performed in conjunction with high spatial resolution, and in the case of dual-source CT, high pitch, or high temporal resolution acquisitions. Some of the most promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value. These include imaging of the inner ear, bones, small blood vessels, heart, and lung. This review describes the clinical benefits observed to date and future directions for this technical advance in CT imaging. KEY POINTS: • Beneficial characteristics of photon-counting detectors include the absence of electronic noise, increased iodine signal-to-noise ratio, improved spatial resolution, and full-time multi-energy imaging. • Promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value and applications requiring multi-energy data simultaneous with high spatial and/or temporal resolution. • Future applications of PCD-CT technology may include extremely high spatial resolution tasks, such as the detection of breast micro-calcifications, and quantitative imaging of native tissue types and novel contrast agents.

Photon-Counting Detector CT for Musculoskeletal Imaging: A Clinical Perspective.

Photon-counting detector (PCD) CT has emerged as a novel imaging modality that represents a fundamental shift in the way that CT systems detect x-rays. After pre-clinical and clinical investigations showed benefits of PCD CT for a range of imaging tasks, the U.S. FDA in 2021 approved the first commercial PCD CT system for clinical use. The technologic features of PCD CT are particularly well suited for musculo-skeletal imaging applications. Advantages of PCD CT compared with conventional energy-integrating detector (EID) CT include smaller detector pixels and excellent geometric dose efficiency that enable imaging of large joints and central skeletal anatomy at ultrahigh spatial resolution; advanced multienergy spectral postprocessing that allows quantification of gout deposits and generation of virtual noncalcium images for visualization of bone edema; improved metal artifact reduction for imaging of orthopedic implants; and higher CNR and suppression of electronic noise. Given substantially improved cortical and trabecular detail, PCD CT images more clearly depict skeletal abnormalities, including fractures, lytic lesions, and mineralized tumor matrix. The purpose of this article is to review, by use of clinical examples comparing EID CT and PCD CT, the technical features of PCD CT and their associated impact on musculoskeletal imaging applications.

Seeing More with Less: Clinical Benefits of Photon-counting Detector CT.

Photon-counting detector (PCD) CT is an emerging technology that has led to continued innovation and progress in diagnostic imaging after it was approved by the U.S. Food and Drug Administration for clinical use in September 2021. Conventional energy-integrating detector (EID) CT measures the total energy of x-rays by converting photons to visible light and subsequently using photodiodes to convert visible light to digital signals. In comparison, PCD CT directly records x-ray photons as electric signals, without intermediate conversion to visible light. The benefits of PCD CT systems include improved spatial resolution due to smaller detector pixels, higher iodine image contrast, increased geometric dose efficiency to allow high-resolution imaging, reduced radiation dose for all body parts, multienergy imaging capabilities, and reduced artifacts. To recognize these benefits, diagnostic applications of PCD CT in musculoskeletal, thoracic, neuroradiologic, cardiovascular, and abdominal imaging must be optimized and adapted for specific diagnostic tasks. The diagnostic benefits and clinical applications resulting from PCD CT in early studies have allowed improved visualization of key anatomic structures and radiologist confidence for some diagnostic tasks, which will continue as PCD CT evolves and clinical use and applications grow. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Ananthakrishnan in this issue.

Photon Counting Detector Computed Tomography: A New Frontier of Myeloma Bone Disease Evaluation.

Photon counting detector (PCD) computed tomography (CT) is a paradigm-shifting innovation in CT imaging which was recently granted approval for clinical use by the US Food and Drug Administration. PCD-CT allows the generation of multi-energy images with increased contrast and scanning speed or ultra-high spatial resolution (UHR) images with lower radiation doses, compared to the currently used energy integrating detector (EID) CT. Since the recognition of bone disease related to multiple myeloma is important for the diagnosis and management of patients, the advent of PCD-CT heralds a new era in superior diagnostic evaluation of myeloma bone disease. In a first-in-human pilot study, patients with multiple myeloma were imaged with UHR-PCD-CT to validate and establish the utility of this technology in routine imaging and clinical care. We describe 2 cases from that cohort to highlight the superior imaging performance and diagnostic potential of PCD-CT for multiple myeloma compared to clinical standard EID-CT. We also discuss how the advanced imaging capabilities from PCD-CT enhances clinical diagnostics to improve care and overall outcomes for patients.

Improved visualization of the wrist at lower radiation dose with photon-counting-detector CT.

OBJECTIVE: To compare the image quality of ultra-high-resolution wrist CTs acquired on photon-counting detector CT versus conventional energy-integrating-detector CT systems. MATERIALS AND METHODS: Participants were scanned on a photon-counting-detector CT system after clinical energy-integrating detector CTs. Energy-integrating-detector CT scan parameters: comb filter-based ultra-high-resolution mode, 120 kV, 250 mAs, Ur70 or Ur73 kernel, 0.4- or 0.6-mm section thickness. Photon-counting-detector CT scan parameters: non-comb-based ultra-high-resolution mode, 120 kV, 120 mAs, Br84 kernel, 0.4-mm section thickness. Two musculoskeletal radiologists blinded to CT system, scored specific osseous structures using a 5-point Likert scale (1 to 5). The Wilcoxon rank-sum test was used for statistical analysis of reader scores. Paired t-test was used to compare volume CT dose index, bone CT number, and image noise between CT systems. P-value < 0.05 was considered statistically significant. RESULTS: Twelve wrists (mean participant age 55.3 ± 17.8, 6 females, 6 males) were included. The mean volume CT dose index was lower for photon-counting detector CT (9.6 ± 0.1 mGy versus 19.0 ± 6.7 mGy, p < .001). Photon-counting-detector CT images had higher Likert scores for visualization of osseous structures (median score = 4, p < 0.001). The mean bone CT number was higher in photon-counting-detector CT images (1946 ± 77 HU versus 1727 ± 49 HU, p < 0.001). Conversely, there was no difference in the mean image noise of the two CT systems (63 ± 6 HU versus 61 ± 6 HU, p = 0.13). CONCLUSION: Ultra-high-resolution imaging with photon-counting-detector CT depicted wrist structures more clearly than conventional energy-integrating-detector CT despite a 49% radiation dose reduction.

Multimodality evaluation of transfascial muscle and other soft tissue herniations of the extremities.

This review illustrates the multimodality assessment of transfascial muscle and other soft tissue herniations of the extremities. Transfascial herniations of the extremities can develop from congenital or acquired disruptions of the deep fascia, resulting in herniation of the underlying muscle, nerve, or soft tissue tumor into the subcutaneous tissues. While most patients present with a painless subcutaneous nodule that may change in size with muscle activation, some may experience focal or diffuse extremity symptoms such as pain and paresthesias. Although the diagnosis may be clinically suspected, radiologic evaluation is useful for definitive diagnosis and characterization. Ultrasound is the preferred modality for initial workup through a focused and dynamic examination. Magnetic resonance imaging can be utilized for equivocal, complicated, and preoperative cases. Computed tomography is less useful in the evaluation of transfascial herniations in the extremities due to similarities in the attenuation between muscle and fascia, which can decrease the conspicuity of small defects.

A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT.

BACKGROUND: Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma. METHODS: Axial slices (2-mm section thickness) from whole-body low-dose CT scans of subjects with biochemically confirmed plasma cell dyscrasias were included in the study. Data were split into train and test sets at the patient level targeting a 90%/10% split. Two musculoskeletal radiologists annotated lytic lesions on the images with bounding boxes. Subsequently, we developed a two-step deep learning model comprising bone segmentation followed by lesion detection. Unet and "You Look Only Once" (YOLO) models were used as bone segmentation and lesion detection algorithms, respectively. Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUROC). RESULTS: Forty whole-body low-dose CTs from 40 subjects yielded 2193 image slices. A total of 5640 lytic lesions were annotated. The two-step model achieved a sensitivity of 91.6% and a specificity of 84.6%. Lesion detection AUROC was 90.4%. CONCLUSION: We developed a deep learning model that detects lytic bone lesions of multiple myeloma on whole-body low-dose CTs with high performance. External validation is required prior to widespread adoption in clinical practice.

Feasibility of photon-counting CT for femoroacetabular impingement syndrome evaluation: lower radiation dose and improved diagnostic confidence.

OBJECTIVE: The feasibility of low-dose photon-counting detector (PCD) CT to measure alpha and acetabular version angles of femoroacetabular impingement (FAI). MATERIAL AND METHODS: FAI patients undergoing an energy-integrating detector (EID) CT underwent an IRB-approved prospective ultra-high-resolution (UHR) PCD-CT between 5/2021 and 12/2021. PCD-CT was dose-matched to the EID-CT or acquired at 50% dose. Simulated 50% dose EID-CT images were generated. Two radiologists evaluated randomized EID-CT and PCD-CT images and measured alpha and acetabular version angles on axial image slices. Image quality (noise, artifacts, and visualization of cortex) and confidence in non-FAI pathology were rated on a 4-point scale (3 = adequate). Preference tests of standard dose PCD-CT, 50% dose PCD-CT, and 50% dose EID-CT relative to standard dose EID-CT were performed using Wilcoxon Rank test. RESULTS: 20 patients underwent standard dose EID-CT (~ CTDIvol, 4.5 mGy); 10 patients, standard dose PCD-CT (4.0 mGy); 10 patients, 50% PCD-CT (2.6 mGy). Standard dose EID-CT images were scored as adequate for diagnostic task in all categories (range 2.8-3.0). Standard dose PCD-CT images scored higher than the reference in all categories (range 3.5-4, p < 0.0033). Half-dose PCD-CT images also scored higher for noise and cortex visualization (p < 0.0033) and equivalent for artifacts and visualization of non-FAI pathology. Finally, simulated 50% EID-CT images scored lower in all categories (range 1.8-2.4, p < 0.0033). CONCLUSIONS: Dose-matched PCD-CT is superior to EID-CT for alpha angle and acetabular version measurement in the work up of FAI. UHR-PCD-CT enables 50% radiation dose reduction compared to EID while remaining adequate for the imaging task.

Incidence of complications related to shoulder arthroplasties identified on computed tomography.

PURPOSE: Determine incidence of shoulder arthroplasty complications identified on computed tomography (CT). MATERIALS AND METHODS: Retrospective institutional database review of patients with shoulder arthroplasties who underwent CT between 01/2006-11/2021 at a tertiary academic referral center with subspecialized orthopedic shoulder surgeons. CT reports were reviewed for arthroplasty type and complication. Data were stratified and summarized. Associations between complications and arthroplasty types were determined with Chi-squared goodness of fit test. RESULTS: Eight hundred twelve CTs in 797 unique patients were included (438 (53.9%) females and 374 (46.1%) males; mean age 67 ± 11 years). There were 403 total shoulder arthroplasties (TSA), 317 reverse total shoulder arthroplasties (rTSA), and 92 hemiarthroplasties (HA). Complications were present in 527/812 (64.9%) and incidences were: loosening/aseptic osteolysis 36.9%, periprosthetic failure 21.6%, periprosthetic fracture 12.3%, periprosthetic dislocation 6.8%, joint/pseudocapsule effusion 5.9%, prosthetic failure 4.8%, infection 3.8%, and periprosthetic collection 2.1%. Complications per arthroplasty were: 305/403 (75.7%) TSAs, 176/317 (55.5%) rTSAs, and 46/92 (50%) HAs (p < 0.001). Periprosthetic fracture (20.8%), prosthetic dislocation (9.8%), and prosthetic failure (7.9%) were highest in rTSAs (p < 0.001, p < 0.013, p < 0.001, respectively). Loosening/aseptic osteolysis most frequent in TSAs (54.1%) (p < 0.001). Periprosthetic failure most frequent in HA (32.6%) (p < 0.001). Significant associations were identified with joint/pseudocapsule effusion and loosening/aseptic osteolysis (p = 0.04) and prosthetic dislocation (p < .001). CONCLUSION: In this single tertiary academic referral center cohort, the incidence of shoulder arthroplasty complication identified on CT was 64.9% and the most commonly occurring complication was loosening/aseptic osteolysis (36.9%). TSA had the highest incidence of complication (75.7%).

First Clinical Photon-counting Detector CT System: Technical Evaluation.

Background The first clinical CT system to use photon-counting detector (PCD) technology has become available for patient care. Purpose To assess the technical performance of the PCD CT system with use of phantoms and representative participant examinations. Materials and Methods Institutional review board approval and written informed consent from four participants were obtained. Technical performance of a dual-source PCD CT system was measured for standard and high-spatial-resolution (HR) collimations. Noise power spectrum, modulation transfer function, section sensitivity profile, iodine CT number accuracy in virtual monoenergetic images (VMIs), and iodine concentration accuracy were measured. Four participants were enrolled (between May 2021 and August 2021) in this prospective study and scanned using similar or lower radiation doses as their respective clinical examinations performed on the same day using energy-integrating detector (EID) CT. Image quality and findings from the participants' PCD CT and EID CT examinations were compared. Results All standard technical performance measures met accreditation and regulatory requirements. Relative to filtered back-projection reconstructions, images from iterative reconstruction had lower noise magnitude but preserved noise power spectrum shape and peak frequency. Maximum in-plane spatial resolutions of 125 and 208 µm were measured for HR and standard PCD CT scans, respectively. Minimum values for section sensitivity profile full width at half maximum measurements were 0.34 mm (0.2-mm nominal section thickness) and 0.64 mm (0.4-mm nominal section thickness) for HR and standard PCD CT scans, respectively. In a 120-kV standard PCD CT scan of a 40-cm phantom, VMI iodine CT numbers had a mean percentage error of 5.7%, and iodine concentration had root mean squared error of 0.5 mg/cm3, similar to previously reported values for EID CT. VMIs, iodine maps, and virtual noncontrast images were created for a coronary CT angiogram acquired with 66-msec temporal resolution. Participant PCD CT images showed up to 47% lower noise and/or improved spatial resolution compared with EID CT. Conclusion Technical performance of clinical photon-counting detector (PCD) CT is improved relative to that of a current state-of-the-art CT system. The dual-source PCD geometry facilitated 66-msec temporal resolution multienergy cardiac imaging. Study participant images illustrated the effect of the improved technical performance. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Willemink and Grist in this issue.

Ultra-high-resolution imaging of the shoulder and pelvis using photon-counting-detector CT: a feasibility study in patients.

OBJECTIVE: To evaluate ultra-high-resolution (UHR) imaging of large joints using an investigational photon-counting detector (PCD) CT. MATERIALS AND METHODS: Patients undergoing clinical shoulder or pelvis energy-integrating-detector (EID) CT exam were scanned using the UHR mode of the PCD-CT system. Axial EID-CT images (1-mm sections) and PCD-CT images (0.6-mm sections) were reconstructed using Br62/Br64 and Br76 kernels, respectively. Two musculoskeletal radiologists rated visualization of anatomic structures using a 5-point Likert scale. Wilcoxon rank-sum test was used for statistical analysis of reader scores, and paired t-test was used for comparing bone CT numbers and image noise from PCD-CT and EID-CT. RESULTS: Thirty-two patients (17 shoulders and 15 pelvis) were prospectively recruited for this feasibility study. Mean age for shoulder exams was 67.3 ± 15.5 years (11 females) and 47.2 ± 15.8 years (11 females) for pelvis exams. The mean volume CT dose index was lower on PCD-CT compared to that on EID-CT (shoulders: 18 mGy vs. 34 mGy, pelvis: 11.6 mGy vs. 16.7 mGy). PCD-CT was rated significantly better than EID-CT (p < 0.001) for anatomic-structure visualization. Trabecular delineation in shoulders (mean score = 4.24 ± 0.73) and femoroacetabular joint visualization in the pelvis (mean score = 3.67 ± 1.03) received the highest scores. PCD-CT demonstrated significant increase in bone CT number (p < 0.001) relative to EID-CT; no significant difference in image noise was found between PCD-CT and EID-CT. CONCLUSION: The evaluated PCD-CT system provided improved visualization of osseous structures in the shoulders and pelvises at a 31-47% lower radiation dose compared to EID-CT. KEY POINTS: • A full field-of-view PCD-CT with 0.151 mm × 0.176 mm detector pixel size (isocenter) facilitates bilateral, high-resolution imaging of shoulders and pelvis. • The evaluated investigational PCD-CT system was rated superior by two musculoskeletal radiologists for anatomic structure visualization in shoulders and pelvises despite a 31-47% lower radiation dose compared to EID-CT. • PCD-CT demonstrated significantly higher bone CT number compared to EID-CT, while no significant difference in image noise was observed between PCD-CT and EID-CT despite a 31-47% dose reduction on PCD-CT.

Evolving Role of Dual-Energy CT in the Clinical Workup of Gout: A Retrospective Study.

BACKGROUND. Dual-energy CT (DECT) allows noninvasive detection of monosodium urate (MSU) crystal deposits and has become incorporated into the routine clinical evaluation for gout at many institutions over the past decade. OBJECTIVE. The purpose of this study was to compare two time periods over the past decade in terms of radiologists' interpretations of DECT examinations performed for the evaluation of gout and subsequent clinical actions. METHODS. This retrospective study included 100 consecutive adult patients who underwent DECT to evaluate for gout in each of two periods (one beginning in March 2013 and one beginning in September 2019). Examinations performed in 2013 were conducted using a second-generation DECT scanner (80 kV [tube A] and 140 kV [tube B] with a 0.4-mm tin filter), and those performed in 2019 were conducted using a third-generation DECT scanner (80 kV [tube A] and 150 kV [tube B] with a 0.6-mm tin filter) that provides improved spectral separation. Original DECT reports were classified as positive, negative, or equivocal for MSU crystals indicative of gout. Joint aspirations occurring after the DECT examinations were recorded on the basis of findings from medical record review. A single radiologist performed a post hoc retrospective blinded image review, classifying examinations as positive, negative, or equivocal. RESULTS. In 2013, 44.0% of DECT examinations were interpreted as positive, 23.0% as negative, and 33.0% as equivocal; in 2019, 37.0% were interpreted as positive, 47.0% as negative, and 16.0% as equivocal (p < .001). The frequency of joint aspiration after DECT was 14.0% in 2013 versus 2.0% in 2019 (p = .002), and that after DECT examinations with negative interpretations was 17.4% in 2013 versus 2.1% in 2019 (p = .02). In post hoc assessment by a single radiologist, the distribution of interpretations in 2013 was positive in 49.0%, negative in 22.0%, and equivocal in 29.0%, and in 2019 it was positive in 39.0%, negative in 50.0%, and equivocal in 11.0% (p < .001). CONCLUSION. When DECT examinations performed for gout in 2013 and 2019 were compared, the frequency of equivocal interpretations was significantly lower in 2019, possibly in relation to interval technologic improvements. Negative examinations were less frequently followed by joint aspirations in 2019, possibly reflecting increasing clinical acceptance of the DECT results. CLINICAL IMPACT. The findings indicate an evolving role for DECT in the evaluation of gout after an institution's routine adoption of the technology for this purpose.