Citations

PurposeMulti‐energy computed tomography (MECT) has a great potential to enable many novel clinical applications such as simultaneous multi‐contrast imaging. The purpose of this study was to implement triple‐beam MECT on a traditional energy‐integrating‐detector (EID) CT platform (EID‐MECT).MethodsThis was accomplished by mounting a z‐axis split‐filter (0.05 mm Au, 0.6 mm Sn) on Tube A of a dual‐source EID CT scanner. With the two split x‐ray beams from Tube A and the third beam from Tube B, three beams with different x‐ray spectra can be simultaneously acquired. With Tube B operated at 70 or 80 kV and Tube A at 120 or 140 kV, four different triple‐beam configurations were calibrated for MECT measurements: 70/Au120/Sn120, 80/Au120/Sn120, 70/Au140/Sn140, and 80/Au140/Sn140 kV. Iodine (I), gadolinium (Gd), bismuth (Bi) samples, and their mixtures were prepared for 2 three‐material‐decomposition tasks and 1 four‐material‐decomposition task. For each task, samples were placed in a water phantom and scanned using each of the four triple‐beam configurations. For comparison, the same phantom was also scanned using three other dual‐energy CT (DECT) or MECT technologies: twin‐beam DECT (TB‐DECT), dual‐source DECT (DS‐DECT), and photon‐counting‐detector CT (PCD‐CT), all with optimal x‐ray spectrum settings and at equal volume CT dose index (CTDIvol). The phantom for four‐material decomposition (I/Gd/Bi/Water imaging) was scanned using the PCD‐CT only (140 kV with 25, 50, 75, and 90 keV). Image‐based material decomposition was performed to acquire material‐specific images, on which the mean basis material concentrations and noise levels were measured and compared across all triple‐beam configurations in EID‐MECT and various DECT/MECT systems.ResultsThe optimal triple‐beam configuration was task‐dependent with 70/Au120/Sn120, 70/Au140/Sn140, and 70/Au120/Sn120 kV for I/Gd/Water, I/Bi/Water, and I/Gd/Bi/Water material decomposition tasks, respectively. At equal radiation dose level, EID‐MECT provided comparable or better quantification accuracy in material‐specific images for all three material decomposition tasks, compared to EID‐based DECT and PCD‐CT systems. In terms of noise level comparison, EID‐MECT‐derived material‐specific images showed lower noise levels than TB‐DECT and DS‐DECT, but slightly higher than that from PCD‐CT in I/Gd/Water imaging. For I/Bi/Water imaging, EID‐MECT showed a comparable noise level to DS‐DECT, and a much lower noise level than TB‐DECT and PCD‐CT in all material‐specific images. For the four‐material decomposition task involving I/Gd/Bi/Water, the bismuth‐specific image derived from EID‐MECT was slightly noisier, but both iodine‐ and gadolinium‐specific images showed much lower noise levels in comparison to PCD‐CT.ConclusionsFor the first time, an EID‐based MECT system that can simultaneously acquire three x‐ray spectra measurements was implemented on a clinical scanner, which demonstrated comparable or better imaging performance than existing DECT and MECT systems.

Abstract Context Hypoparathyroidism is characterized by low serum calcium, increased serum phosphorus, and inappropriately low or decreased serum parathyroid hormone, which may be associated with soft tissue calcification in the basal ganglia of the brain. Objective To assess the prevalence and factors involved in the pathophysiology of basal ganglia calcification (BGC) in the brain in chronic hypoparathyroidism and to evaluate proposed pathophysiologic mechanisms. Design Case-control study with retrospective review of medical records over 20 years. Setting Single academic medical center. Patients 142 patients with chronic hypoparathyroidism and computed tomography (CT) head scans followed between January 1, 2000 and July 9, 2020, and 426 age- and sex-matched controls with CT head scans over the same interval. Interventions None. Main Outcome Measures Demographic, biochemical, and CT head imaging findings, with semiquantitative assessment of volumetric BGC. Results The study found that 25.4% of 142 patients followed for a median of 17 years after diagnosis of chronic hypoparathyroidism had BGC, which developed at a younger age than in controls. BGC was 5.1-fold more common in nonsurgical patients and less common in postsurgical patients. Low serum calcium and low calcium/phosphate ratio correlated with BGC. Neither serum phosphorus nor calcium × phosphate product predicted BGC. Lower serum calcium was associated with greater volume of BGC. The extent of BGC varied widely, with nonsurgical patients generally having a greater volume and distribution of calcification. Conclusions BGC is associated with low serum calcium and low serum calcium/phosphate ratio, which may be related to severity of the disease, its etiology, or duration of treatment.

Objective This study aims to determine if low iodine dynamic computed tomography angiography performed after a fixed delay or test bolus acquisition demonstrates high concordance with clinical computed tomography angiography (using a routine amount of iodinated contrast) to display lower extremity peripheral arterial disease. Methods After informed consent, low iodine dynamic computed tomography angiography examination (using either a fixed delay or test bolus) using 50 ml of iodine contrast media was performed. A subsequent clinical computed tomography angiography using standard iodine dose (115 or 145 ml) served as the reference standard. A vascular radiologist reviewed dynamic and clinical computed tomography angiography images to categorize the lumen into “not opacified”, “<50% stenosis”, “ 50 ̶70% stenosis”, “>70% stenosis”, and “occluded” for seven arterial segments in each lower extremity. Concordance between low iodine dynamic computed tomography angiography and the routine iodine reference standard was calculated. The clinical utility of 4D volume-rendered images was also evaluated. Results Sixty-eight patients (average age 66.1 ± 12.3 years, male; female = 49: 19) were enrolled, with 34 patients each undergoing low iodine dynamic computed tomography angiography using fixed delay and test bolus techniques, respectively. One patient assigned to the test bolus group did not undergo low iodine computed tomography angiography due to unavailable delayed time. The fixed delay was 13 s, with test bolus acquisition resulting in a mean variable delay prior to image acquisition of 19.5 s (range; 8–32 s). Run-off to the ankle was observed using low iodine dynamic computed tomography angiography following fixed delay and test bolus acquisition in 76.4% (26/34) and 100% (33/33) of patients, respectively ( p = 0.005). Considering extremities with run-off to the ankle and without severe artifact, the concordance rate between low iodine dynamic computed tomography angiography and the routine iodine reference standard was 86.8% (310/357) using fixed delay and 97.9% (425/434) using test bolus ( p < 0.001). 4D volume-rendered images using fixed delay and test bolus demonstrated asymmetric flow in 57.7% (15/26) and 58.1% (18/31) ( p = 0.978) of patients, and collateral blood flow in 11.5% (3/26) and 22.6% (7/31) of patients ( p = 0.319), respectively. Conclusion Low iodine dynamic computed tomography angiography with test bolus acquisition has a high concordance with routine peripheral computed tomography angiography performed with standard iodine dose, resulting in improved run-off to the ankle compared to dynamic computed tomography angiography performed after a fixed delay. This method is useful for minimizing iodine dose in patients at risk for contrast-induced nephropathy. 4D volume-rendered computed tomography angiography images provide useful dynamic information.

AbstractIt is important to employ radiation dose reduction techniques in pediatric computed tomography (CT) to reduce potential risks of radiation‐induced malignancy. Automatic tube potential (kV) selection tools have been developed and become available on many CT scanners, which select the optimum kV based on the patient size and clinical task to improve the radiation dose efficiency. However, its use in pediatric CT has been mostly empirical, following manufacturer’s default recommendation without solid demonstration for quality improvement. This study aimed to implement an automatic tube potential tool (CAREkV, Siemens Healthcare) into routine pediatric CT practice, using the “Plan‐Do‐Study‐Act” quality improvement process, in place of an existing kV/mAs technique chart. The design of this quality improvement project involved Plan‐Do‐Study‐Act stages. Plan and Do stages identified the criteria for optimal automatic kV selection; a range of phantoms representing typical pediatric groups were scanned on a dual‐source 128‐slice scanner using a fast‐pitch scanning mode. The identified CAREkV settings were implemented into the CT protocol and evaluated after a 6‐month period. In the Study stage, an objective evaluation of the image metrics and radiation dose for two similar patient cohorts using CAREkV and the technique‐chart, respectively, were compared. The kV selected, image quality and radiation dose determined by CAREkV were comparable to those obtained while using the technique‐chart. The CAREkV was successfully implemented into our pediatric abdominopelvic CT practice. By utilizing the “PDSA” process optimal image quality and radiation dose reduction were achieved with an automatic kV selection tool to improve CT workflow.

PurposeTalbot‐Lau grating interferometry enables the use of polychromatic x‐ray sources, extending the range of potential applications amenable to phase contrast imaging. However, these sources introduce beam hardening effects not only from the samples but also from the gratings. As a result, grating inhomogeneities due to manufacturing imperfections can cause spectral nonuniformity artifacts when used with polychromatic sources. Consequently, the different energy dependencies of absorption, phase, and visibility contrasts impose challenges that so far have limited the achievable image quality. The purpose of this work was to develop and validate a correction strategy for grating‐based x‐ray imaging that accounts for beam hardening generated from both the imaged object and the gratings.MethodsThe proposed two‐variable polynomial expansion strategy was inspired by work performed to address beam hardening from a primary modulator. To account for the multicontrast nature of grating interferometry, this approach was extended to each contrast to obtain three sets of correction coefficients, which were determined empirically from a calibration scan. The method’s feasibility was demonstrated using a tabletop Talbot‐Lau grating interferometer micro‐computed tomography (CT) system using CT acquisitions of a water sample and a silicon sample, representing low and high atomic number materials. Spectral artifacts such as cupping and ring artifacts were quantified using mean squared error (MSE) from the beam‐hardening‐free target image and standard deviation within a reconstructed image of the sample. Finally, the model developed using the water sample was applied to a fixated murine lung sample to demonstrate robustness for similar materials.ResultsThe water sample’s absorption CT image was most impacted by spectral artifacts, but following correction to decrease ring artifacts, an 80% reduction in MSE and 57% reduction in standard deviation was observed. The silicon sample created severe artifacts in all contrasts, but following correction, MSE was reduced by 94% in absorption, 96% in phase, and 90% in visibility images. These improvements were due to the removal of ring artifacts for all contrasts and reduced cupping in absorption and phase images and reduced capping in visibility images. When the water calibration coefficients were applied to the lung sample, ring artifacts most prominent in the absorption contrast were eliminated.ConclusionsThe described method, which was developed to remove artifacts in absorption, phase, and normalized visibility micro‐CT images due to beam hardening in the system gratings and imaged object, reduced the MSE by up to 96%. The method depends on calibrations that can be performed on any system and does not require detailed knowledge of the x‐ray spectrum, detector energy response, grating attenuation properties and imperfections, or the geometry and composition of the imaged object.

Purpose To determine the accuracy of CT number and calcium score of a kV‐independent technique based on an artificial 120 kV reconstruction, and its potential to reduce radiation dose. Methods Anthropomorphic chest phantoms were scanned on a third‐generation dual‐source CT system equipped with the artificial 120 kV reconstruction. First, a phantom module containing a 20‐mm diameter hydroxyapatite (HA) insert was scanned inside the chest phantoms at different tube potentials (70–140 kV) to evaluate calcium CT number accuracy. Next, three small HA inserts (diameter/length = 5 mm) were inserted into a pork steak and scanned inside the phantoms to evaluate calcium score accuracy at different kVs. Finally, the same setup was scanned using automatic exposure control (AEC) at 120 kV, and then with automatic kV selection (auto‐kV). Phantoms were also scanned at 120 kV using a size‐dependent mA chart. CT numbers of soft tissue and calcium were measured from different kV images. Calcium score of each small HA insert was measured using commercial software. Results The CT number difference from 120 kV was small with tube potentials from 90 to 140 kV for both soft tissue and calcium (maximal difference of 4/5 HU, respectively). Consistent calcium scores were obtained from images of different kVs compared to 120 kV, with a relative difference <8%. Auto‐kV provided a 25–34% dose reduction compared to AEC alone. Conclusion A kV‐independent calcium scoring technique can produce artificial 120 kV images with consistent soft tissue and calcium CT numbers compared to standard 120 kV examinations. When coupled with auto‐kV, this technique can reduce radiation by 25–34% compared to that with AEC alone, while providing consistent calcium scores as that of standard 120 kV examinations.

Purpose To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. Acquisition and Validation Methods The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low‐dose noncontrast chest scans acquired to screen high‐risk patients for pulmonary nodules, and contrast‐enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise‐insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. Data Format and Usage Notes Projection datasets were converted into the previously developed DICOM‐CT‐PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM‐CT‐PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive ( https://doi.org/10.7937/9npb‐2637 ). Potential Applications This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task‐based diagnostic performance.

PurposeTo develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi‐energy computed tomography (CT) images without performing conventional material decomposition.MethodsThe proposed CNN (denoted as Incept‐net) followed the general framework of encoder–decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi‐energy CT. Incept‐net was implemented with a customized loss function, including an in‐house‐designed image‐gradient‐correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood–iodine mixture, and fat] were scanned using a research photon‐counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different‐configurations of full field‐of‐view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept‐net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least‐square‐based material decomposition (LS‐MD), total‐variation regularized material decomposition (TV‐MD), and U‐net‐based method.ResultsIncept‐net improved accuracy of the predicted mass density of basis materials compared with the U‐net, TV‐MD, and LS‐MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept‐net, U‐net, TV‐MD, and LS‐MD, respectively, across all iodine‐present inserts (2.0–24.0 mgI/cc). With the LS‐MD as the baseline, Incept‐net and U‐net achieved comparable noise reduction (both around 95%), both higher than TV‐MD (85%). The proposed IGC regularizer effectively helped both Incept‐net and U‐net to reduce image artifact. Incept‐net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept‐net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept‐net achieved relatively improved performance than the comparator U‐net, indicating that performance gain by Incept‐net was not achieved by simply increasing network learning capacity.ConclusionIncept‐net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept‐net generalized and performed well with unseen image structures and different material mass densities. This study provided preliminary evidence that the proposed CNN may be used to improve the material decomposition quality in multi‐energy CT.