McCollough, Cynthia H.

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 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.

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.