Change in spinal bone mineral density as estimated by Hounsfield units following osteoporosis treatment with romosozumab, teriparatide, denosumab, and alendronate: an analysis of 318 patients.

OBJECTIVE: The purpose of this study was to determine the effect of osteoporosis medications on opportunistic CT-based Hounsfield units (HU). METHODS: Spine and nonspine surgery patients were retrospectively identified who had been treated with romosozumab for 3 to 12 months, teriparatide for 3 to 12 months, teriparatide for > 12 months, denosumab for > 12 months, or alendronate for > 12 months. HU were measured in the L1-4 vertebral bodies. One-way ANOVA was used to compare the mean change in HU among the five treatment regimens. RESULTS: In total, 318 patients (70% women) were included, with a mean age of 69 years and mean BMI of 27 kg/m2. There was a significant difference in mean HU improvement (p < 0.001) following treatment with romosozumab for 3 to 12 months (n = 32), teriparatide for 3 to 12 months (n = 30), teriparatide for > 12 months (n = 44), denosumab for > 12 months (n = 123), and alendronate for > 12 months (n = 100). Treatment with romosozumab for a mean of 10.5 months significantly increased the mean HU by 26%, from a baseline of 85 to 107 (p = 0.012). Patients treated with teriparatide for > 12 months (mean 23 months) experienced a mean HU improvement of 25%, from 106 to 132 (p = 0.039). Compared with the mean baseline HU, there was no significant difference after treatment with teriparatide for 3 to 12 months (110 to 119, p = 0.48), denosumab for > 12 months (105 to 107, p = 0.68), or alendronate for > 12 months (111 to 113, p = 0.80). CONCLUSIONS: Patients treated with romosozumab for a mean of 10.5 months and teriparatide for a mean of 23 months experienced improved spinal bone mineral density as estimated by CT-based opportunistic HU. Given the shorter duration of effective treatment, romosozumab may be the preferred medication for optimization of osteoporotic patients in preparation for elective spine fusion surgery.

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.

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.

Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence.

Introduction: Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence. Methods: We collected a sample of positive and negative DECTs, reviewed twice-once with and once without the DL tool-with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed. Results: We included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL. Conclusions: The implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model.

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.

Staging of breast cancer in the breast and regional lymph nodes using contrast-enhanced photon-counting detector CT: accuracy and potential impact on patient management.

OBJECTIVES: To describe the feasibility and evaluate the performance of multiphasic photon-counting detector (PCD) CT for detecting breast cancer and nodal metastases with correlative dynamic breast MRI and digital mammography as the reference standard. METHODS: Adult females with biopsy-proven breast cancer undergoing staging breast MRI were prospectively recruited to undergo a multiphasic PCD-CT using a 3-phase protocol: a non-contrast ultra-high-resolution (UHR) scan and 2 intravenous contrast-enhanced scans with 50 and 180 s delay. Three breast radiologists compared CT characteristics of the index malignancy, regional lymphadenopathy, and extramammary findings to MRI. RESULTS: Thirteen patients underwent both an MRI and PCD-CT (mean age: 53 years, range: 36-75 years). Eleven of thirteen cases demonstrated suspicious mass or non-mass enhancement on PCD-CT when compared to MRI. All cases with metastatic lymphadenopathy (3/3 cases) demonstrated early avid enhancement similar to the index malignancy. All cases with multifocal or multicentric disease on MRI were also identified on PCD-CT (3/3 cases), including a 4 mm suspicious satellite lesion. Four of five patients with residual suspicious post-biopsy calcifications on mammograms were detected on the UHR PCD-CT scan. Owing to increased field-of-view at PCD-CT, a 5 mm thoracic vertebral metastasis was identified at PCD-CT and not with the breast MRI. CONCLUSIONS: A 3-phase PCD-CT scan protocol shows initial promising results in characterizing breast cancer and regional lymphadenopathy similar to MRI and detects microcalcifications in 80% of cases. ADVANCES IN KNOWLEDGE: UHR and spectral capabilities of PCD-CT may allow for comprehensive characterization of breast cancer and may represent an alternative to breast MRI in select cases.

A review of epidural and non-epidural contrast flow patterns during fluoroscopic and CT-guided epidural steroid injections.

Epidural steroid injections are commonly performed using fluoroscopic or CT guidance. With both modalities, the injection of contrast material is necessary before steroid administration to ensure adequate epidural flow and exclude non-epidural flow. While fluoroscopic guidance is conventional, CT is utilized at some centers and can be particularly helpful in the setting of challenging or postoperative anatomy. It is important for proceduralists to be adept at evaluating contrast media flow patterns under both modalities. The goal of this review article is to describe and provide examples of epidural and non-epidural flow patterns on both conventional fluoroscopy and CT. Specific non-epidural patterns discussed include intrathecal flow, intradural/subdural flow, vascular uptake, flow into the retrodural space of Okada, inadvertent facet joint flow, and intradiscal flow.

A Unique Case of Familial Expansile Osteolysis: Findings on 99mTc-MDP Bone Scan.

ABSTRACT: Familial expansile osteolysis is an exceedingly rare autosomal dominant bone dysplasia, which can have overlapping features with Paget disease and expansile skeletal hyperphosphatasia. We present a novel case of familial expansile osteolysis evaluated on 99mTc-MDP bone scan with correlative radiographs and CT.

Diagnostic Performance of Decubitus Photon-Counting Detector CT Myelography for the Detection of CSF-Venous Fistulas.

BACKGROUND AND PURPOSE: CSF-venous fistulas are a common cause of spontaneous intracranial hypotension. Lateral decubitus digital subtraction myelography and CT myelography are the diagnostic imaging standards to identify these fistulas. Photon-counting CT myelography has technological advantages that might improve CSF-venous fistula detection, though no large studies have yet assessed its diagnostic performance. We sought to determine the diagnostic yield of photon-counting detector CT myelography for detection of CSF-venous fistulas in patients with spontaneous intracranial hypotension. MATERIALS AND METHODS: We retrospectively searched our database for all decubitus photon-counting detector CT myelograms performed at our institution since the introduction of the technique in our practice. Per our institutional workflow, all patients had prior contrast-enhanced brain MR imaging and spine MR imaging showing no extradural CSF. Two neuroradiologists reviewed preprocedural brain MRIs, assessing previously described findings of intracranial hypotension (Bern score). Additionally, 2 different neuroradiologists assessed each myelogram for a definitive or equivocal CSF-venous fistula. The yield of photon-counting detector CT myelography was calculated and stratified by the Bern score using low-, intermediate-, and high-probability tiers. RESULTS: Fifty-seven consecutive photon-counting detector CT myelograms in 57 patients were included. A single CSF-venous fistula was definitively present in 38/57 patients. After we stratified by the Bern score, a definitive fistula was seen in 56.0%, 73.3%, and 76.5% of patients with low-, intermediate-, and high-probability brain MR imaging, respectively. CONCLUSIONS: Decubitus photon-counting detector CT myelography has an excellent diagnostic performance for the detection of CSF-venous fistulas. The yield for patients with intermediate- and high-probability Bern scores is at least as high as previously reported yields of decubitus digital subtraction myelography and CT myelography using energy-integrating detector scanners. The yield for patients with low-probability Bern scores appears to be greater compared with other modalities. Due to the retrospective nature of this study, future prospective work will be needed to compare the sensitivity of photon-counting detector CT myelography with other modalities.

Opportunistic CT-Based Hounsfield Units Strongly Correlate with Biomechanical CT Measurements in the Thoracolumbar Spine.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: Hounsfield units (HUs) are known to correlate with clinical outcomes, no study has evaluated how they correlate with BCT and DXA measurements. SUMMARY OF BACKGROUND: Low bone mineral density (BMD) represents a major risk factor for fracture and poor outcomes following spine surgery. Dual-energy x-ray absorptiometry (DXA) can provide regional BMD measurements but has limitations. Opportunistic HUs provide targeted BMD estimates; however, they are not formally accepted for diagnosing osteoporosis in current guidelines. More recently, biomechanical computed tomography (BCT) analysis has emerged as a new modality endorsed by the International Society for Clinical Densitometry (ISCD) for assessing bone strength. METHODS: Consecutive cases from 2017-2022 at a single institution were reviewed for patients who underwent BCT in the thoracolumbar spine. BCT-measured vertebral strength, trabecular BMD, and the corresponding American College of Radiology (ACR) Classification were recorded. DXA studies within three months of the BCT were reviewed. Pearson Correlation Coefficients were calculated, and receiver-operating characteristic curves were constructed to assess the predictive capacity of HUs. Threshold analysis was performed to identify optimal HU values for identifying osteoporosis and low BMD. RESULTS: Correlation analysis of 114 cases revealed a strong relationship between HUs and BCT vertebral strength (r=0.69; P<0.0001; R2=0.47) and trabecular BMD (r=0.76; P<0.0001; R2=0.58). However, DXA poorly correlated with opportunistic HUs and BCT measurements. HUs accurately predicted osteoporosis and low BMD (Osteoporosis: C=0.95, 95% CI 0.89-1.00; Low BMD: C=0.87, 95% CI 0.79-0.96). Threshold analysis revealed that 106 and 122 HUs represent optimal thresholds for detecting osteoporosis and low BMD. CONCLUSION: Opportunistic HUs strongly correlated with BCT-based measures, while neither correlated strongly with DXA-based BMD measures in the thoracolumbar spine. HUs are easy to perform at no additional cost and provide accurate BMD estimates at non-instrumented vertebral levels across all ACR-designated BMD categories.

Potential benefits of photon counting detector computed tomography in pediatric imaging.

Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows "always-on" dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

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.

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.

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%).

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.

Multi-energy CT material decomposition using Bayesian deep convolutional neural network with explicit penalty of uncertainty and bias.

Convolutional neural network (CNN)-based material decomposition has the potential to improve image quality (visual appearance) and quantitative accuracy of material maps. Most methods use deterministic CNNs with mean-square-error loss to provide point-estimates of mass densities. Point estimates can be over-confident as the reliability of CNNs is frequently compromised by bias and two major uncertainties - data and model uncertainties originating from noise in inputs and train-test data dissimilarity, respectively. Also, mean-square-error lacks explicit control of uncertainty and bias. To tackle these problems, a Bayesian dual-task CNN (BDT-CNN) with explicit penalization of uncertainty and bias was developed. It is a probabilistic CNN that concurrently conducts material classification and quantification and allows for pixel-wise modeling of bias, data uncertainty, and model uncertainty. CNN was trained with images of physical and simulated tissue-mimicking inserts at varying mass densities. Hydroxyapatite (nominal density 400mg/cc) and blood (nominal density 1095mg/cc) inserts were placed in different-sized body phantoms (30 - 45cm) and used to evaluate mean-absolute-bias (MAB) in predicted mass densities across different images at routine- and half-routine-dose. Patient CT exams were collected to assess generalizability of BDT-CNN in the presence of anatomical background. Noise insertion was used to simulate patient exams at half- and quarter-routine-dose. The deterministic dual-task CNN was used as baseline. In phantoms, BDT-CNN improved consistency of insert delineation, especially edges, and reduced overall bias (average MAB for hydroxyapatite: BDT-CNN 5.4mgHA/cc, baseline 11.0mgHA/cc and blood: BDT-CNN 8.9mgBlood/cc, baseline 14.0mgBlood/cc). In patient images, BDT-CNN improved detail preservation, lesion conspicuity, and structural consistency across different dose levels.

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.

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.

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.