Citations

Abstract Background Evaluation of middle ear prostheses requires high-resolution imaging to assess position and structural integrity. Purpose To assess the diagnostic performance of photon-counting-detector (PCD)-CT for middle ear prosthesis imaging in cadaveric specimens. Materials and Methods Nine different middle ear prostheses were implanted in five fresh-frozen whole-head specimens, both in correct anatomic position and displaced to mimic malposition. Specimens were scanned on PCD-CT (NAEOTOM Alpha, Siemens) and energy-integrating-detector (EID)-CT (SOMATOM Force, Siemens), reconstructed at 0.2mm and 0.4mm, respectively. Eighteen cases (9 prostheses, 2 positions) per scanner were evaluated by two neuroradiologists (36 readings), blinded to scanner and position, who scored prosthesis position, reader confidence, and overall image quality. Agreement with the reference standard and between readers was compared. Results PCD-CT showed higher diagnostic confidence compared with EID-CT, while accuracy and inter-reader agreement did not demonstrate significant differences between scanners. Correct readings, PCD-CT versus EID-CT: lateral contact, 31/36 (86.1%; 95% CI: 58.3%-94.4%) vs. 29/36 (80.6%; 55.6%-91.7%), p = 0.500; medial contact, 30/36 (83.3%; 55.6%-94.4%) vs. 26/36 (72.2%; 50.0%-83.3%), p = 0.250. PCD-CT identified all cases of complete contact loss (score 3), while EID-CT missed one reading per contact. Inter-reader agreement may be higher with PCD-CT vs EID-CT (lateral: κw: 0.804 [0.578-0.941] vs. 0.571 [0.302-0.778], p = 0.250; medial: 0.684 [0.191-0.934] vs. 0.481 [0.024-0.835], p = 0.250). Reader confidence was significantly higher with PCD-CT; mean paired differences for Readers 1 and 2 were 9.4 (1.8-17.1; Holm-adjusted p = 0.019) and 23.1 points (13.4-32.7; p < 0.001) for lateral contact, and 16.4 (10.0-22.8; p < 0.001) and 17.8 points (6.8-28.8; p = 0.007) for medial contact. Readers preferred PCD-CT image quality in 16/18 (88.9%; 65.3%-98.6%) and 17/18 cases (94.4%; 72.7%-99.9%), respectively. Conclusion PCD-CT was associated with significantly higher reader confidence and preferred image quality, without significant differences in diagnostic accuracy or inter-reader agreement. These findings suggest PCD-CT is a promising modality for postoperative middle ear evaluations that warrants further investigations.

Abstract Objective. Four-dimensional CT (4DCT) enables visualization of dynamic wrist joint pathology but may be compromised by motion artifacts. Deep learning (DL)-assisted motion correction has the potential to enhance wrist 4DCT quality, but model development is hindered by scarce training data. To address this, we developed a model-based motion simulation framework that generated realistic 4DCT data with paired static ground truth images to demonstrate wrist motion correction using a physics-informed DL model trained with simulated data. Approach. Wrist motion was simulated by synchronizing rotational and translational movements of carpal bones and connective tissues. Elastic deformation guided by analytically-derived motion vector fields was applied to static wrist scans at neutral positions to create artifact-free images at multiple angular positions. Virtual motion-corrupted 4DCT images were produced via forward projection and reconstruction with synchronized gantry and wrist motion. Experimental data from 4DCT cadaver scans were collected to validate simulation quality. Low-intensity region score (LIRS), positivity, and entropy were used to assess simulated motion artifacts. A physics-informed motion correction network was adapted exclusively using simulated training data and subsequently deployed on simulated, real cadaveric, and in vivo 4DCT scans. LIRS and structural similarity index (SSIM) were used to evaluate motion correction quality. Wilcoxon signed-rank tests were conducted ( α = 0.05). Results. The simulation method created realistic motion artifacts compared to real scan reference ( p > 0.05). Motion artifacts were effectively mitigated across testing cases demonstrating generalizability. In simulated scans, LIRS improved from 0.55 ± 0.12–0.75 ± 0.04 and SSIM from 0.87 ± 0.08–0.95 ± 0.03 at bone regions (each p < 0.01). In experimental cadaveric scans, LIRS improved from 0.05 ± 0.01–0.31 ± 0.17 in marker regions and from 0.58 ± 0.19–0.77 ± 0.14 in bone regions (each p < 0.01). Significance. Our simulation method facilitates training DL-assisted motion correction algorithms by providing high-quality training data, effectively supporting 4DCT image quality enhancement.

Purpose of review Photon-counting detector computed tomography (PCD-CT) represents a major technological advancement in computed tomography, offering improved spatial resolution, reduced image noise, and increased dose efficiency compared with conventional energy-integrating detector CT (EID-CT). Because many clinically relevant structures within the temporal bone are submillimeter in size, the temporal bone represents an ideal region for determining whether these technical improvements translate into meaningful clinical benefits. This review evaluates the emerging literature on PCD-CT in temporal bone imaging and examines whether higher spatial resolution meaningfully impacts clinical decision-making. Recent findings Recent studies demonstrate improved visualization of middle ear anatomy, ossicular prostheses, cochlear implant electrode arrays, and superior semicircular canal dehiscence with PCD-CT compared with conventional CT. Improved spatial resolution and reduced metal artifact have been shown to enhance assessment of prosthesis positioning, cochlear duct measurements, and differentiation of true versus near semicircular canal dehiscence. However, given the recent availability of PCD-CT, most studies to date have focused on cadaveric studies, phantom studies, or patient studies evaluating image quality rather than diagnostic accuracy or clinical outcomes. Summary PCD-CT provides substantially improved visualization of submillimeter temporal bone structures and may improve diagnostic confidence in select clinical scenarios. Nevertheless, evidence demonstrating improved clinical outcomes remains limited, and further clinical studies are needed to define the role of PCD-CT in routine temporal bone imaging.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Abstract Additive manufacturing (AM), commonly referred to as 3D printing, has been broadly used in the field of medical imaging and radiotherapy. In medical imaging applications, patient‐specific anatomic models have been used to aid in preoperative planning, patient communication, imaging technique assessment and optimization, and medical education. Within the field of radiation therapy, 3D printing finds significance in fabricating patient‐specific immobilization devices, custom bolus devices, anthropomorphic phantoms, brachytherapy devices, and electron therapy devices. Consistent with standard medical physics practices there is a need to establish quality assurance and quality control (QA/QC) protocols, tolerances, and testing frequencies. The diversity of 3D printing technologies and products poses challenges for medical adoption and thus requires guidelines in workflow standardization and establishment of testing procedures. This report is centered around providing comprehensive vendor‐neutral implementation guidelines and recommendations for QC and QA processes. The scope of this report encompasses diverse requirements within medical imaging and radiation therapy settings, serving both clinical and research purposes. Brief overview of 3D printing technology, common materials, and potential clinical applications is provided. QA team, infrastructure, measurement techniques and challenges are discussed. QA processes for each step in 3D printing are discussed in great detail and baseline and action limits are recommended. This report also touched on regulatory considerations. Key recommendations are summarized at the end of the report.

Abstract Photon-counting detector CT (PCD-CT) directly detects individual photons and measures their energies, enabling the acquisition of multi-energy data from a single x-ray source with higher spatial resolution, thereby allowing visualization of smaller arteries and veins and other important imaging features not seen at conventional CT. It also substantially reduces calcium and stent blooming artifacts. Appropriate patient selection and task-based optimization of acquisition, reconstruction, and interpretation are critical to realize these benefits. Vascular imaging examinations for which early evidence shows that PCD-CT demonstrates higher diagnostic performance compared to conventional CT include coronary CT angiography (CTA) for evaluation of partially calcified plaques or stents, peripheral runoff CTA, detection of cerebrospinal fluid-venous fistulas, and head CTA for evaluation of small intracranial vessels and aneurysms. Current limitations include excessive image noise with the sharpest reconstruction kernels and challenges when adapting conventional CT protocols (eg, requiring sharper kernels, thinner slices, higher matrices, tailored scan modes) to achieve time-efficient image reconstruction and hanging protocols. New solutions and directions will include deep-learning-based image noise reduction, improved spectral separation, strategic triage of specific patient populations to PCD-CT systems, and subspecialty clinical practice recommendations for this new technology.

Abstract Background Radiomics extracts quantitative imaging features from computed tomography (CT) data for clinical decision‐making. However, variations in acquisition parameters—particularly x‐ray tube voltage (kV)—introduce non‐biological variability in attenuation values, limiting the reproducibility of radiomic features across scanners, protocols, and institutions. Purpose To develop and evaluate a CT dAta harmoNiZAtion framework based on deep learNed vIrTual monoEnergetic imaging (TANZANITE), which leverages the keV flexibility of virtual monoenergetic images (VMIs) to enable cross‐kV scan translation and radiomics harmonization. Methods TANZANITE is a model hub consisting of multiple pre‐trained convolutional neural networks (CNNs), each designed to translate VMIs from 1 keV level to another. Phantom‐based calibration was first used to determine energy‐equivalent (Eff_E) keV levels corresponding to each tube potential (e.g., Eff_E(A) keV for source kV and Eff_E(B) keV for target kV). A CNN trained using 69 120 patches from seven patient cases to map VMIs from Eff_E(A) to Eff_E(B) was selected from the TANZANITE hub and applied directly to clinical CT images acquired at the source kV. This harmonized the images to match the attenuation characteristics of the target kV setting. Evaluation was conducted on independent dual‐energy CT datasets acquired at 100/Sn150 kV. Regions of interest (ROIs) were placed in the kidney, liver, and spine to assess CT number consistency and radiomic feature reproducibility. The concordance correlation coefficient (CCC) was calculated across 93 non‐shape radiomic features. Results After TANZANITE processing with 100 kV images, CT numbers in evaluated organs closely matched the Sn150 kV reference values in four testing patient cases. For example, mean kidney CT numbers changed from 320 HU (100 kV) to 156 HU (TANZANITE), approximating the Sn150 kV value of 160 HU. Similar changes were observed in the liver (157–105 HU vs. 104 HU reference) and spine (45–22 HU vs. 19 HU reference). Radiomic reproducibility improved substantially across organs: mean CCC increased from 0.590 to 0.995 in the liver, 0.300 to 0.970 in the kidney, and 0.630 to 0.968 in the spine. Post‐TANZANITE, over 98% of features exceeded the stability threshold (CCC ≥ 0.900) in all three representative organs. Conclusion TANZANITE provides a flexible, image‐domain harmonization framework by learning the keV‐to‐keV translation in the VMI domain and applying pre‐trained CNNs to clinical kV images. It improves CT number consistency and organ‐specific radiomic reproducibility without requiring raw projection data or scanner‐specific training. This approach supports consistent quantitative imaging across multiple‐kV acquisition protocols, enhancing radiomics reliability in clinical settings.