Dose of CT protocols acquired in clinical routine using a dual-layer detector CT scanner: A preliminary report.

PURPOSE: To assess the radiation dose associated with always-on dual-energy acquisitions in clinical practice over a broad range of clinical protocols using a dual-layer detector CT (DLCT; IQon spectral CT, Philips Healthcare) as compared to an otherwise technically equivalent single-layer detector CT (SLCT; Brilliance iCT, Philips healthcare). MATERIALS AND METHODS: Dose-length-product data for consecutive examinations over a six-month period acquired with DLCT were retrospectively collected and compared to consecutive examinations from an SLCT. Imaging protocols were optimized for diagnostic image quality for each system prior to data collection. Dose reports of CT protocols that were used at least 50 times on both systems were collected. After exclusion of statistical outliers, protocols were evaluated with regard to reported dose levels. RESULTS: In total, 4536 dose reports for DLCT and 5783 reports for SLCT were collected. All DLCT examinations were acquired at 120 kVp, enabling dual-energy analysis. With SLCT, 79% of examinations were acquired at 120 kVp, and 21% at 100/80 kVp. Protocols for 15 indications were used more than 50 times on both scanners. For seven protocols there was no significant difference between the two scanners (p > 0.05), whereas seven protocols were acquired with higher dose levels on SLCT compared to the DLCT (p < 0.03). For one protocol, the DLCT dose was significantly higher (p < 0.005) compared to the SLCT. CONCLUSION: Dual-layer detector CT enables acquisition of dual-energy information over a broad range of clinical indications without increasing radiation dose when compared to a conventional single-layer detector CT.

The Effects of Iodine Attenuation on Pulmonary Nodule Volumetry using Novel Dual-Layer Computed Tomography Reconstructions.

OBJECTIVES: To assess the effect of iodine attenuation on pulmonary nodule volumetry using virtual non-contrast (VNC) and mono-energetic reconstructions. METHODS: A consecutive series of patients who underwent a contrast-enhanced chest CT scan were included. Images were acquired on a novel dual-layer spectral CT system. Conventional reconstructions as well as VNC and mono-energetic images at different keV levels were used for nodule volumetry. RESULTS: Twenty-four patients with a total of 63 nodules were included. Conventional reconstructions showed a median (interquartile range) volume and diameter of 174 (87 - 253) mm3 and 6.9 (5.4 - 9.9) mm, respectively. VNC reconstructions resulted in a significant volume reduction of 5.5% (2.6 - 11.2%; p<0.001). Mono-energetic reconstructions showed a correlation between nodule attenuation and nodule volume (Spearman correlation 0.77, (0.49 - 0.94)). Lowering the keV resulted in increased volumes while higher keV levels resulted in decreased pulmonary nodule volumes compared to conventional CT. CONCLUSIONS: Novel dual-layer spectral CT offers the possibility to reconstruct VNC and mono-energetic images. Those reconstructions show that higher pulmonary nodule attenuation results in larger nodule volumes. This may explain the reported underestimation in nodule volume on non-contrast enhanced compared to contrast-enhanced acquisitions. KEY POINTS: • Pulmonary nodule volumes were measured on virtual non-contrast and mono-energetic reconstructions • Mono-energetic reconstructions showed that higher attenuation results in larger volumes • This may explain the reported nodule volume underestimation on non-contrast enhanced CT • Mostly metastatic pulmonary nodules were evaluated, results might differ for benign nodules.

Imaging of pediatric great vessel stents: Computed tomography or magnetic resonance imaging?

BACKGROUND: Complications might occur after great vessel stent implantation in children. Therefore follow-up using imaging is warranted. PURPOSE: To determine the optimal imaging modality for the assessment of stents used to treat great vessel obstructions in children. MATERIAL AND METHODS: Five different large vessel stents were evaluated in an in-vitro setting. All stents were expanded to the maximal vendor recommended diameter (20mm; n = 4 or 10mm; n = 1), placed in an anthropomorphic chest phantom and imaged with a 256-slice CT-scanner. MRI images were acquired at 1.5T using a multi-slice T2-weighted turbo spin echo, an RF-spoiled three-dimensional T1-weighted Fast Field Echo and a balanced turbo field echo 3D sequence. Two blinded observers assessed stent lumen visibility (measured diameter/true diameter *100%) in the center and at the outlets of the stent. Reproducibility of diameter measurements was evaluated using the intraclass correlation coefficient for reliability and 95% limits of agreement for agreement analysis. RESULTS: Median stent lumen visibility was 88 (IQR 86-90)% with CT for all stents at both the center and outlets. With MRI, the T2-weighted turbo spin echo sequence was preferred which resulted in 82 (78-84%) stent lumen visibility. Interobserver reliability and agreement was good for both CT (ICC 0.997, mean difference -0.51 [-1.07-0.05] mm) and MRI measurements (ICC 0.951, mean difference -0.05 [-2.52 --2.41] mm). CONCLUSION: Good in-stent lumen visibility was achievable in this in-vitro study with both CT and MRI in different great vessel stents. Overall reliability was good with clinical acceptable limits of agreement for both CT and MRI. However, common conditions such as in-stent stenosis and associated aneurysms were not tested in this in-vitro study, limiting the value of the in-vitro study.

New horizons in cardiac CT.

Until recently, cardiovascular computed tomography angiography (CCTA) was associated with considerable radiation doses. The introduction of tube current modulation and automatic tube potential selection as well as high-pitch prospective ECG-triggering and iterative reconstruction offer the ability to decrease dose with approximately one order of magnitude, often to sub-millisievert dose levels. In parallel, advancements in computational technology have enabled the measurement of fractional flow reserve (FFR) from CCTA data (FFRCT). This technique shows potential to replace invasively measured FFR to select patients in need of coronary intervention. Furthermore, developments in scanner hardware have led to the introduction of dual-energy and photon-counting CT, which offer the possibility of material decomposition imaging. Dual-energy CT reduces beam hardening, which enables CCTA in patients with a high calcium burden and more robust myocardial CT perfusion imaging. Future-generation CT systems will be capable of counting individual X-ray photons. Photon-counting CT is promising and may result in a substantial further radiation dose reduction, vastly increased spatial resolution, and the introduction of a whole new class of contrast agents.

Simulation of nodules and diffuse infiltrates in chest radiographs using CT templates.

A method is proposed to simulate nodules and diffuse infiltrates in chest radiographs. This allows creation of large annotated databases for training of both radiologists and computer aided diagnosis systems. Realistic nodules and diffuse infiltrates were generated from three-dimensional templates segmented from CT data. These templates are rescaled, rotated, projected and superimposed on a radiograph. This method was compared, in an observer study, to a previously published method that simulates pulmonary nodules as perfectly spherical objects. Results show that it is hard for human observers to distinguish real and simulated nodules when using templates (AUC-values do not significantly differ from .5, p > .05 for all observers). The method that produced spherical nodules performed slightly worse (AUC of one observer differs significantly from .5, p = .011). Simulation of diffuse infiltrates is challenging but also feasible (AUC = 0.67 for one observer).

A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification.

A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.

Filter learning: application to suppression of bony structures from chest radiographs.

A novel framework for image filtering based on regression is presented. Regression is a supervised technique from pattern recognition theory in which a mapping from a number of input variables (features) to a continuous output variable is learned from a set of examples from which both input and output are known. We apply regression on a pixel level. A new, substantially different, image is estimated from an input image by computing a number of filtered input images (feature images) and mapping these to the desired output for every pixel in the image. The essential difference between conventional image filters and the proposed regression filter is that the latter filter is learned from training data. The total scheme consists of preprocessing, feature computation, feature extraction by a novel dimensionality reduction scheme designed specifically for regression, regression by k-nearest neighbor averaging, and (optionally) iterative application of the algorithm. The framework is applied to estimate the bone and soft-tissue components from standard frontal chest radiographs. As training material, radiographs with known soft-tissue and bone components, obtained by dual energy imaging, are used. The results show that good correlation with the true soft-tissue images can be obtained and that the scheme can be applied to images from a different source with good results. We show that bone structures are effectively enhanced and suppressed and that in most soft-tissue images local contrast of ribs decreases more than contrast between pulmonary nodules and their surrounding, making them relatively more pronounced.