Citations

PurposeDemonstrate realistic simulation of grating‐based x‐ray phase‐contrast imaging (GB‐XPCI) using wave optics and the four‐dimensional Mouse Whole Body (MOBY) phantom defined with non‐uniform rational B‐splines (NURBS).MethodsWe use a full‐wave approach, which uses wave optics for x‐ray wave propagation from the source to the detector. This forward imaging model can be directly applied to NURBS‐defined numerical phantoms such as MOBY. We assign the material properties (attenuation coefficient and electron density) of each model part using the data for adult human tissues. The Poisson noise is added to the simulated images based on the calculated photon flux at each pixel.ResultsWe simulate the intensity images of the MOBY phantom for eight different grating positions. From the simulated images, we calculate the absorption, differential phase, and normalized visibility contrast images. We also predict how the image quality is affected by different exposure times.ConclusionsGB‐XPCI can be simulated with the full‐wave approach and a realistic numerical phantom defined with NURBS.

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT), AI holds the promise of enabling further reductions in patient radiation dose through automation and optimisation of data acquisition processes, including patient positioning and acquisition parameter settings. Subsequent to data collection, optimisation of image reconstruction parameters, advanced reconstruction algorithms, and image denoising methods improve several aspects of image quality, especially in reducing image noise and enabling the use of lower radiation doses for data acquisition. Finally, AI-based methods to automatically segment organs or detect and characterise pathology have been translated out of the research environment and into clinical practice to bring automation, increased sensitivity, and new clinical applications to patient care, ultimately increasing the benefit to the patient from medically justified CT examinations. In summary, since the introduction of CT, a large number of technical advances have enabled increased clinical benefit and decreased patient risk, not only by reducing radiation dose, but also by reducing the likelihood of errors in the performance and interpretation of medically justified CT examinations.

In x‐ray computed tomography (CT), materials with different elemental compositions can have identical CT number values, depending on the mass density of each material and the energy of the detected x‐ray beam. Differentiating and classifying different tissue types and contrast agents can thus be extremely challenging. In multienergy CT, one or more additional attenuation measurements are obtained at a second, third or more energy. This allows the differentiation of at least two materials. Commercial dual‐energy CT systems (only two energy measurements) are now available either using sequential acquisitions of low‐ and high‐tube potential scans, fast tube‐potential switching, beam filtration combined with spiral scanning, dual‐source, or dual‐layer detector approaches. The use of energy‐resolving, photon‐counting detectors is now being evaluated on research systems. Irrespective of the technological approach to data acquisition, all commercial multienergy CT systems circa 2020 provide dual‐energy data. Material decomposition algorithms are then used to identify specific materials according to their effective atomic number and/or to quantitate mass density. These algorithms are applied to either projection or image data. Since 2006, a number of clinical applications have been developed for commercial release, including those that automatically (a) remove the calcium signal from bony anatomy and/or calcified plaque; (b) create iodine concentration maps from contrast‐enhanced CT data and/or quantify absolute iodine concentration; (c) create virtual non‐contrast‐enhanced images from contrast‐enhanced scans; (d) identify perfused blood volume in lung parenchyma or the myocardium; and (e) characterize materials according to their elemental compositions, which can allow in vivo differentiation between uric acid and non‐uric acid urinary stones or uric acid (gout) or non‐uric acid (calcium pyrophosphate) deposits in articulating joints and surrounding tissues. In this report, the underlying physical principles of multienergy CT are reviewed and each of the current technical approaches are described. In addition, current and evolving clinical applications are introduced. Finally, the impact of multienergy CT technology on patient radiation dose is summarized.

Background Low tube potential-high tube current computed tomography (CT) imaging allows reduction in iodine-based contrast dose and may extend the benefit of routine contrast-enhanced CT exams to patients at risk of nephrotoxicity. Purpose To determine the ability of an iodine contrast reduction algorithm to maintain diagnostic image quality for contrast-enhanced abdominal CT. Material and Methods CT exams with iodine contrast reduction were prescribed for patients at risk for renal dysfunction. The iodine contrast reduction algorithm combines weight-based contrast volume reduction with patient width-based low tube potential selection and bolus-tracking. Control exams with routine iodine dose were selected based on weight, width, and scan protocol. Three radiologists evaluated image quality and diagnostic confidence using a 4-point scale (<2 acceptable). Another radiologist assessed contrast reduction indications and measured portal vein and liver contrast-to-noise ratios. Results Forty-six contrast reduction algorithm and control exams were compared (mean creatinine 1.6 vs. 1.2 mg/dL, P ≤ 0.0001). Thirty-nine contrast reduction patients had an eGFR <60 mL/min/1.73m 2 and 15 had single or transplanted kidney. Mean iodine contrast dose was lower in the contrast reduction group (20.9 vs. 39.4 g/mL, P < 0.0001). Diagnostic confidence was rated as acceptable in 95% (131/138) of contrast reduction and 100% of control exams (1.18–1.28 vs. 1.02–1.13, respectively; P > 0.06). Liver attenuation and contrast-to-noise ratio (CNR) were similar ( P = 0.08), but portal vein attenuation and CNR were lower with contrast-reduction (mean 176 vs. 198 HU, P = 0.02; 13 vs. 16, P = 0.0002). Conclusion This size-based contrast reduction algorithm using low kV and bolus tracking reduced iodine contrast dose by 50%, while achieving acceptable image quality in 95% of exams.

Objective The aim of this study was to grade cartilage damage in a swine model of osteoarthritis using a whole-body photon-counting detector (PCD) CT. Materials and Methods A multienergy phantom containing gadolinium (Gd) (2, 4, 8, and 16 mg/mL) and hydroxyapatite (200 and 400 mg/cc) was scanned using a PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs, D50 reconstruction kernel) to serve as calibration for material decomposition and to assess quantification accuracy. Osteoarthritis was induced in Yucatan miniswine (n = 8) using 1.2 mg monoiodoacetate (MIA) injected into a randomized knee, whereas the contralateral control knee received saline. Twenty-one days later, a contrast bolus (gadoterate meglumine, 4 mL/knee) was intra-articularly administered into both knees. The knees were simultaneously scanned on the PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs). Multienergy images were reconstructed with a sharp “V71” kernel and a quantitative “D50” kernel. Image denoising was applied to the V71 images before grading cartilage damage, and an iterative material decomposition technique was applied to D50 images to generate the Gd maps. Two radiologists blinded to the knee injection status graded the cartilage integrity based on a modified International Cartilage Repair Society scoring system. Histology was performed on excised cartilage using methylene blue/basic fuchsin. Statistical analysis of grade distribution was performed using an exact test of omnibus symmetry with P < 0.05 considered significant. Results Material decomposed images from the multienergy phantom scan showed delineation and quantification of Gd and hydroxyapatite with a root-mean-squared error of 0.3 mg/mL and 18.4 mg/cc, respectively. In the animal cohort, the radiologists reported chondromalacia in the MIA knees with International Cartilage Repair Society scores ranging from grade 1 (cartilage heterogeneity, n = 4 knees) to grade 3 (up to 100% cartilage loss, n = 4 knees). Grade 1 was characterized by cartilage heterogeneity and increased joint space in the patellofemoral compartment, whereas grade 3 was characterized by cartilage erosion and bone-on-bone articulation in the patellofemoral compartment. All control knees were scored as grade 0 (normal cartilage). Significant difference (P = 0.004) was observed in the grade distribution between the MIA and control knees. Gross examination of the excised knees showed cartilage lesions in the grade 3 MIA knees. The Gd maps from material decomposition showed lower contrast levels in the joint space of the MIA knee compared with the contralateral control knee due to joint effusion. Histology revealed chondrocyte loss in the MIA knee cartilage confirming the chondrotoxic effects of MIA on cartilage matrix. Conclusions We demonstrated a high-resolution and quantitative PCD-CT arthrography technique for grading cartilage damage in a large animal model of osteoarthritis. Photon-counting detector CT offers simultaneous high-resolution and multienergy imaging capabilities that allowed morphological assessment of cartilage loss and quantification of contrast levels in the joint as a marker of joint disease. Cartilage damage in the MIA knees was graded using PCD-CT images, and the image-based findings were further confirmed using histology and gross examination of the excised knees.

Objective The aim of this study was to quantitatively demonstrate radiation dose reduction for sinus and temporal bone examinations using high-resolution photon-counting detector (PCD) computed tomography (CT) with an additional tin (Sn) filter. Materials and Methods A multienergy CT phantom, an anthropomorphic head phantom, and a cadaver head were scanned on a research PCD-CT scanner using ultra-high-resolution mode at 100-kV tube potential with an additional tin filter (Sn-100 kV) and volume CT dose index of 10 mGy. They were also scanned on a commercial CT scanner with an energy-integrating detector (EID) following standard clinical protocols. Thirty patients referred to clinically indicated sinus examinations, and two patients referred to temporal bone examinations were scanned on the PCD-CT system after their clinical scans on an EID-CT. For the sinus cohort, PCD-CT scans were performed using Sn-100 kV at 4 dose levels at 10 mGy (n = 9), 8 mGy (n = 7), 7 mGy (n = 7), and 6 mGy (n = 7), and the clinical EID-CT was performed at 120 kV and 13.7 mGy (mean CT volume dose index). For the temporal bone scans, PCD-CT was performed using Sn-100 kV (10.1 mGy), and EID-CT was performed at 120 kV and routine clinical dose (52.6 and 66 mGy). For both PCD-CT and EID-CT, sinus images were reconstructed using H70 kernel at 0.75-mm slice thickness, and temporal bone images were reconstructed using a U70 kernel at 0.6-mm slice thickness. In addition, iterative reconstruction with a dedicated sharp kernel (V80) was used to obtain high-resolution PCD-CT images from a sinus patient scan to demonstrate improved anatomic delineation. Improvements in spatial resolution from the dedicated sharp kernel was quantified using modulation transfer function measured with a wire phantom. A neuroradiologist assessed the H70 sinus images for visualization of critical anatomical structures in low-dose PCD-CT images and routine-dose EID-CT images using a 5-point Likert scale (structural detection obscured and poor diagnostic confidence, score = 1; improved anatomic delineation and diagnostic confidence, score = 5). Image contrast and noise were measured in representative regions of interest and compared between PCD-CT and EID-CT, and the noise difference between the 2 acquisitions was used to estimate the dose reduction in the sinus and temporal bone patient cohorts. Results The multienergy phantom experiment showed a noise reduction of 26% in the Sn-100 kV PCD-CT image, corresponding to a total dose reduction of 56% compared with EID-CT (clinical dose) without compromising image contrast. The PCD-CT images from the head phantom and the cadaver scans demonstrated a dose reduction of 67% and 83%, for sinus and temporal bone examinations, respectively, compared with EID-CT. In the sinus cohort, PCD-CT demonstrated a mean dose reduction of 67%. The 10- and 8-mGy sinus patient images from PCD-CT were significantly superior to clinical EID-CT for visualization of critical sinus structures (median score = 5 ± 0.82 and P = 0.01 for lesser palatine foramina, median score = 4 ± 0.68 and P = 0.039 for nasomaxillary sutures, and median score = 4 ± 0.96 and P = 0.01 for anterior ethmoid artery canal). The 6- and 7-mGy sinus patient images did not show any significant difference between PCD-CT and EID-CT. In addition, V80 (sharp kernel, 10% modulation transfer function = 18.6 cm −1 ) PCD-CT images from a sinus patient scan increased the conspicuity of nasomaxillary sutures compared with the clinical EID-CT images. The temporal bone patient images demonstrated a dose reduction of up to 85% compared with clinical EID-CT images, whereas visualization of inner ear structures such as the incudomalleolar joint were similar between EID-CT and PCD-CT. Conclusions Phantom and cadaver studies demonstrated dose reduction using Sn-100 kV PCD-CT compared with current clinical EID-CT while maintaining the desired image contrast. Dose reduction was further validated in sinus and temporal bone patient studies. The ultra-high resolution capability from PCD-CT allowed improved anatomical delineation for sinus imaging compared with current clinical standard.

PurposeFiltering measured projections with a particular convolutional kernel is an essential step in analytic reconstruction of computed tomography (CT) images. A tradeoff between noise and spatial resolution exists for different choices of reconstruction kernel. In a clinical setting, this often requires producing multiple images reconstructed with different kernels for a single CT exam, which increases the burden of computation, networking, archival, and reading. We address this problem by training a deep convolutional neural network (CNN) to synthesize multiple input images into a single output image which exhibits low noise while also preserving features in images reconstructed with the sharpest kernels.MethodsA CNN architecture consisting of repeated blocks of residual units containing a total of 20 convolutional layers was used to combine features. The CNN inputs consisted of two images produced with different reconstruction kernels, one smooth and one sharp, which were stacked in the channel dimension. The network was trained using supervised learning with both full‐dose and simulated quarter‐dose abdominal CT images. After training, the performance was evaluated using a reserved set of full‐dose scans that were not used for network optimization. Noise reduction performance was measured by comparing root mean square (RMS) measurements in uniform regions. Spatial resolution was compared using line profiles of anatomic features.ResultsFor the regions tested, the synthetic images feature noise levels slightly below those of the smooth input images, while maintaining the resolution of anatomic details found in the sharp input images.ConclusionsA deep CNN can be used combine features from CT images reconstructed with different kernels to produce a single synthesized image series that exhibits both low noise and high spatial resolution. This approach has implications for improving image quality, reducing radiation dose, and simplifying the clinical workflow for CT imaging.

PurposeTo evaluate the quantitative accuracy and dose efficiency of simultaneous imaging of two contrast agents using dual‐energy computed tomography (DECT), two imaging tasks each representing one potential clinical application were investigated in a phantom study: biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth.MethodsTo separate and quantify mixtures of two contrast agents using a single DECT scan, mixed iodine and gadolinium samples were prepared with the contrast enhancement values corresponding to the late arterial (iodine) and the portal‐venous (gadolinium) phase for biphasic liver imaging. Mixed iodine and bismuth samples were prepared mimicking the arterial (iodine) and the enteric (bismuth) enhancement for small bowel imaging. For comparison to the reference condition of performing two single‐energy CT (SECT) scans, contrast samples were prepared separately to mimic separate scans in the arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water tank and scanned using a third‐generation dual‐source DECT scanner with three tube potential pairs: 80/Sn150, 90/Sn150, and 100/Sn150 kV, all with default dose partitioning between two x‐ray beams to acquire DECT data. The same scanner operated in a single‐energy mode acquired SECT data (120 kV). Total radiation dose (CTDIvol) was matched for the single‐scan DECT and the two‐scan SECT protocols. The DECT protocol was followed by a generic image‐based three‐material decomposition method to determine the material‐specific images, based on which concentrations of each basis material were quantified and noise levels were measured. To compare with the SECT images directly acquired with the SECT protocol, the concentration values in each contrast‐specific image were converted to CT numbers at 120 kV (i.e., virtual SECT (vSECT) images). The noise level and noise power spectra differences between the SECT and vSECT images were compared to evaluate the dose efficiency of the single‐scan DECT protocol. The impact of dose partitioning in the DECT protocol on quantitative dual‐contrast imaging performance was also studied.ResultsFor each imaging task, contrast materials were accurately quantified against the nominal concentrations using the DECT data with strong correlation (R2 ≥ 0.98 for both imaging tasks). Compared to the SECT protocol, the DECT protocol was not dose efficient. With the optimal x‐ray tube potential pair 80/Sn150 kV, the noise level in vSECT images increased by 401%/488% (arterial/portal‐venous) for the biphasic liver imaging task and by 10%/41% (arterial/enteric) for the small bowel imaging task compared to that in SECT images. The corresponding radiation dose increase is 2410%/3357% for the biphasic liver imaging task and 21%/99% for the small bowel imaging task, respectively, to achieve the same noise as that in SECT images. This could be improved by adjusting the dose partitioning in DECT.ConclusionsDECT can be used to simultaneously separate and quantify two contrast materials. However, compared to a two‐scan SECT protocol, much higher radiation dose is needed in a single‐scan DECT protocol to achieve the same image noise, especially for tasks involving the dual contrast of iodine and gadolinium.