Citations

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.

Purpose Two-dimensional (2D) X-ray imaging is routinely used in radiotherapy for patient alignment but offers limited tumor visibility due to anatomical overlap. We propose a novel technique, prior information-based subtraction (PIBS) radiography, to enhance tumor visualization on 2D X-ray and facilitate more accurate patient alignment. Materials and methods PIBS X-ray improves tumor visualization on 2D X-ray by subtracting signal contributions from non-essential (NE) tissues, which can be obtained from the planning CT (prior information). To demonstrate the feasibility, we acquired four CT scans using a thorax phantom with a spherical lung target, representing the planning CT and three treatment-day CTs capturing the patient’s positions with the lung tumor at three different locations, respectively. NE tissues, defined as structures outside the thoracic cavity, were segmented from the planning CT. On treatment days, we aligned NE tissues based on bony structures via 3D/3D or 2D/2D registration and applied the calculated shifts so the patient’s position matched that in the planning CT. To simulate 2D X-rays, digitally reconstructed radiographs (DRRs) were generated from the planning CT of NE tissues and aligned with treatment-day CTs. Subtracting NE tissue contributions from conventional 2D X-ray yielded PIBS X-ray. PIBS X-ray images were compared with conventional X-ray images to assess tumor visibility. Results PIBS X-ray provided better tumor visualization across all projection angles and all tumor locations compared to conventional X-ray, even for certain cases where the tumor was completely indistinguishable on conventional X-ray. Both 3D/3D and 2D/2D registrations produced acceptable alignment of NE tissues because of the clear visibility of bony structures. Conclusion PIBS X-ray is a promising technique to enhance tumor visualization on 2D X-ray. In this study, we demonstrated its potential in improving radiotherapy on-board imaging using a thorax phantom. In the future, we will explore strategies for integrating this technology into clinical workflows and evaluate its clinical benefits in radiotherapy patients.

Objective: To examine the association of extent of otosclerotic plaque using high-resolution photon-counting computed tomography (PCCT) with hearing and surgical outcomes. Study design: Historical cohort. Setting: Large academic center. Patients: Patients with otosclerosis who completed PCCT imaging at time of initial evaluation. Interventions: Observation, surgery. Main outcome measures: Audiometric data. Results: A total of 53 ears were eligible for study. The mean age at PCCT was 51 years (SD: 14). The mean extent of oval window otosclerotic plaque measured in radial degrees was 112 (SD: 65) and the mean otosclerotic depth was 1.6mm (SD: 0.6). The mean air-bone gap pure-tone average was 18 dB (SD: 12) and the mean Carhart degree was 9 dB (SD: 10). The radial degrees of oval window extent were not significantly associated with air-bone gap (correlation coefficient: 0.14; P =0.3) or Carhart degree (correlation coefficient: 0.04; P =0.8). Likewise, otosclerotic plaque depth was not significantly associated with air-bone gap (correlation coefficient: −0.19; P =0.2) or Carhart degree (correlation coefficient: −0.08; P =0.6). After PCCT, 22 ears underwent surgery. In this subset, oval window extent and plaque depth were not significantly associated with the postoperative changes in air-bone gap or high-frequency thresholds. Conclusions: Despite enhanced visualization of subtle abnormalities with PCCT imaging, the extent of oval window otosclerotic plaque, measured in angular degrees and depth, was not associated with hearing loss at time of initial evaluation or postoperative audiologic changes in patients with otosclerosis.

Abstract Background Photon counting detector CT (PCD‐CT) has the potential to sort detected x‐ray photons into more than two energy bins to perform multi‐material decomposition using K‐edge materials without additional mathematical constraints/assumptions. The availability of multiple energy thresholds in PCD‐CT opens the possibility for unique clinical applications such as simultaneous multi‐contrast‐agent imaging. Purpose To evaluate the technical performance of a PCD‐CT system with four energy thresholds for conducting multi‐material decomposition of iodine, gadolinium, and water after a comprehensive system calibration procedure. Methods Experiments were performed on a commercial PCD‐CT scanner (NAEOTOM Alpha, Siemens Healthineers) using a research mode with four energy thresholds. Material calibration was performed by scanning contrast materials with known concentration placed in various sizes of cylindrical water‐equivalent phantoms at various tube currents and positions. From these data, per‐voxel contrast models were derived for each material by multiple linear regressions up to second order, where the predictor variables were combinations of tube current, phantom diameter, and distance to isocenter. After calibration, small (20 cm diameter) and large (30 cm × 40 cm) uniform multi‐energy CT phantoms and an anthropomorphic thorax phantom (31 cm × 39 cm), each containing bone, soft tissue, and several quantitative inserts of iodine (0.2–5.0 mg/ml), gadolinium (0.3–5.0 mg/ml), and a mixture of both (5.0 mg/ml each) were scanned using the four‐energy threshold mode at 140 kV with energy thresholds set at 20, 52, 75, and 82 keV. Images for each energy threshold were reconstructed with a filtered back‐projection algorithm and a quantitative kernel (Qr44). Subsequent image‐based material decomposition was performed on individual voxels of the energy threshold images using the models derived from the calibration process to generate water, iodine, and gadolinium basis images. Quantitative material concentrations and image noise magnitudes, and noise power spectra (NPS) were measured. Results Iodine and gadolinium concentrations above 1.25 mg/ml were visible in the material basis images in all phantoms. Averaged quantitative accuracy for iodine and gadolinium in the small, large, and thorax phantoms were 0.39 ± 0.30 mg/ml, 0.96 ± 0.53 mg/ml, and 0.53 ± 0.35 mg/ml, respectively. In the water basis images, accuracy was within 24 HU in all phantoms. Noise magnitudes in iodine and gadolinium basis images were 2.91 and 3.52 times that of the lowest energy threshold image for the small phantom, and were 6.92 and 7.17 times that of the lowest threshold image for the large phantom. Noise texture did not change between the energy threshold and material basis images for either size phantom as measured by the NPS. Conclusions After a comprehensive system calibration, a commercial PCD‐CT with four energy thresholds performed multiple material decomposition of iodine, gadolinium, and water for concentrations above 1.25 mg/ml in human‐sized phantoms. Noise amplification and quantitative accuracy from the decomposition greatly depended on object size.

Abstract Objectives Determine differences in quantitative stenosis severity measurements for ultra-high-resolution (UHR) photon-counting detector (PCD) coronary CT angiography (cCTA) relative to energy-integrating detector (EID) cCTA in a large patient cohort. Methods Adult participants seen between November 2022 and March 2023 underwent a clinical dual-source EID-CT cCTA and a research dual-source PCD-CT cCTA on the same day. Percent diameter stenosis (PDS) was measured, and stenosis severity was assigned based on the PDS of the most severe lesion per patient to determine a coronary artery disease reporting and data system (CAD-RADS) score. Agreement between EID-CT and PCD-CT for PDS and CAD-RADS was determined. Results A total of 112 participants were enrolled, yielding 82 subjects with at least 1 stenosis at PCD-CT (mean age, 68.0 ± 10.8 years; 69.5% [57/82] males). A total of 177 paired stenoses were quantified. The percent decrease in mean PDS from PCD-CT (24.3%) to EID-CT (29.4%) was 17.3% (median difference in PDS: −5.0%, 95% CI: −5.7% to −4.0%). The assigned CAD-RADS score changed in 31/82 subjects. In 11/31 subjects, the most severe stenosis was missed at EID-CT due to partial volume averaging of a small calcification, yet PCD clearly identified stenosis at those locations. In 20/31 subjects, the CAD-RADS score decreased at PCD-CT due to the decreased calcium blooming resulting from the improved spatial resolution. Conclusions UHR PCD cCTA decreases quantitative measures of stenosis severity when a stenosis is identified at EID, leading to decreases in CAD-RADS assignments. Advances in knowledge With its improved spatial resolution, PCD-CT identifies stenoses missed on EID-CT.

Background: The radiolunate joint is conventionally thought to be spared from radiocarpal osteoarthritis, but proximal lunate degenerative changes have been noted. An investigation of radiolunate arthrokinematics in ostensibly healthy wrists may improve understanding of posttraumatic and postoperative outcomes. We hypothesize that four-dimensional computed tomography (4DCT)-derived radiolunate arthrokinematics during motion will differ between wrists with type 1 and type 2 lunates. The clinical-translational relevance of this analysis is highlighted using a 4DCT case study of scaphoid excision with 4-corner arthrodesis (SEFCA). Methods: Four-dimensional computed tomography data were collected during flexion-extension and radioulnar deviation in 19 healthy wrists (mean [SD] age: 43.6 [11.7] years, 21.1% female) and 1 wrist 3 years after SEFCA (male, 52 years). Carpal osteokinematics and joint arthrokinematics were calculated to derive median radiolunate interosseous proximities at each 4DCT-captured timepoint. Results: Median radiolunate interosseous proximities during motion are well approximated by second-order regressions in uninjured wrists. In the arthrodesed wrist, closest proximities occurred at the extremes of motion except maximum radial deviation. Conclusions: This study quantifies a normative range of median radiolunate interosseous proximities during wrist motion. The normative 4DCT datasets provide a reference range for evaluating postoperative arthrokinematics, suggesting the clinical-translational potential of dynamic imaging. The case study highlights applications for patient-level postoperative evaluation, which can be expanded to other pathologies, surgeries, or conditions in the future.

Abstract Background To utilize high spatial resolution reconstructions for cardiac imaging at energy‐integrating detector CT (EID)‐CT with comparable noise to similar reconstructions at photon‐counting detector (PCD)‐CT, methods to control EID‐CT image noise are needed. Supervised convolutional neural networks (CNN) have shown promise for denoising, but a challenge remains to efficiently create high‐quality and unbiased estimates of noise without access to dedicated software or proprietary information, such that natural noise texture is retained in CNN‐denoised CT images. Purpose This study aims to develop and test image‐based noise estimation methods that can be used to train a CNN model, and to evaluate denoising performance and noise texture preservation for EID‐CT coronary CT angiography (cCTA) images reconstructed with high‐resolution kernels. Methods U‐net CNN models were trained for denoising. To supervise training, noise‐only images were estimated directly from high‐resolution kernel (Bv59) reconstructed EID‐CT (HR EID‐CT) patient images using two different methods: subtraction of low‐ and high‐strength iterative reconstruction (IR); subtraction of adjacent image slices with the same IR strength. The noise estimates from these methods contain differing noise texture and anatomical information. Networks were trained and validated separately for three data sets: the training data from each of the two noise‐estimation methods, and a 50%–50% partition of training data between the two methods. The trained models were applied to two sets of testing data: CT images of a uniform water phantom to measure noise power spectra (NPS), and an independent cohort of seven patient cCTA HR EID‐CT exams. The denoised patient images were compared to standard resolution EID‐CT reconstructions (Bv40). As a low‐noise reference, patient images acquired on the same day with a PCD‐CT and reconstructed using a similar kernel as HR EID‐CT were used for comparison. Results Models trained with each noise‐image estimation method denoised the HR EID‐CT images by 74%–79% to achieve a comparable noise magnitude to the HR PCD‐CT images. The peak, average, and 10% peak frequencies of the NPS of the input images (6.08, 6.24, and 12.0 cm −1 ) were better approximated by the model trained on adjacent slice subtraction (6.56, 5.87, and 11.5 cm −1 ) than by the model trained on subtraction of low‐ and high‐IR images (4.64, 5.44, and 11.3 cm −1 ). In cCTA images, the IR subtraction model images retained anatomic structures from input images but resulted in undesirable salt‐and‐pepper noise texture and CT number bias. The model trained on adjacent slice subtraction images had more natural texture and no significant bias, but the model sometimes removed small anatomic structures. The model trained on the mixed training dataset preserved both noise texture and anatomy from the model inputs and enabled visualization of small structures seen in PCD‐CT images that were previously unresolved by EID‐CT. Conclusions The noise texture and anatomical accuracy in CT images denoised with an image‐based supervised CNN are greatly influenced by the characteristics and partitioning of training data. With higher‐resolution reconstructions and noise texture‐preserving deep learning denoising, the quality of cCTA images from EID‐CT can be enhanced to enable resolvability of subtle anatomy similar to PCD‐CT.

Abstract Background Coronary computed tomography angiography (cCTA) is a widely used noninvasive diagnostic exam to assess patients for coronary artery disease (CAD). However, the spatial resolution of most CT scanners is limited due to the use of energy‐integrating detectors (EIDs). Purpose To develop a convolutional neural network ( I mproved LUMEN visualization through A rtificial super‐resolu T ion imag E s (ILUMENATE)) informed by photon‐counting‐detector (PCD)‐CT to improve EID‐CT image resolution and determine its impact on cCTA. Materials and methods With IRB approval, 30 patients undergoing clinically indicated cCTA were scanned with EID‐CT (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and subsequently with ultra‐high‐resolution (UHR) PCD‐CT (NAEOTOM Alpha, Siemens Healthineers) on the same day. ILUMENATE was trained on eight patient PCD‐CT datasets (67,890 patch pairs with 90% for training (61,101), 10% reserved for validation (6,789)) and applied to 22 unseen EID‐CT cases. Spatial resolution was evaluated using line profiles and percent diameter stenosis quantified with a severity score assigned. Two experienced radiologists, blinded to image type, selected preferred series and scored images for overall quality, sharpness, and noise comparing original EID‐CT and ILUMENATE output. Results Visual assessment and line profiles showed substantial resolution improvement with ILUMENATE. Percent diameter stenosis was significantly reduced (mean ± standard deviation: 4.42% ± 4.82%) using ILUMENATE ( p < 0.001) with nine lesions shifting down in severity score. Readers preferred ILUMENATE images in 22/22 cases and scored ILUMENATE superiorly for overall quality, sharpness, and noise ( p < 0.05). Conclusions ILUMENATE enhanced image resolution, resulting in improved overall image quality, reduced calcium blooming artifacts, and improved lumen visibility in cCTA exams performed using EID‐CT. This could potentially allow for improved accessibility to UHR image quality, allowing for more accurate assessment of CAD.