Citations

Background Super‐resolution is a promising technique to create isotropic image volumes from stacks of two‐dimensional (2D) motion‐corrupted images in fetal magnetic resonance imaging (MRI). Purpose To determine an acquisition quality metric and correlate that metric with radiologist perception of three‐dimensional (3D) image quality. Study Type Retrospective. Subjects Eighty‐seven patients, mean gestational age 29 ± 6 weeks. Field Strength/Sequence 1.5 T/ 2D fast spin‐echo. Assessment Four radiologists (L.G., D.M.E.B., P.C., and J.V.; 31, 21, 7, and 7 years' experience, respectively) graded reconstructions on a 0 to 4 scale for overall appearance and visibility of specific anatomy. During reconstruction, slices were labeled as inliers based on correlation between a simulated vs. actual acquisition. The fraction of brain voxels in inlier slicers vs. total brain voxels was measured for each acquisition. Statistical Tests Paired sample t test, Pearson's correlation, intra‐class correlation. Results The average brain mask inlier fraction for all acquisitions was 0.8. There was a statistically significant correlation (0.71) between overall reconstruction appearance and number of acquisitions with inlier fraction above 0.73. There was low correlation (0.21, P = 0.05) between the number of acquisitions used in the reconstruction and overall score when no data quality measure was considered. Similar results were found for ratings of specific anatomy. Statistically significant differences in overall perception of image quality were found when using three vs. four, four vs. five, and three vs. five high‐quality acquisitions in the reconstruction. Five high‐quality acquisitions were sufficient to yield an average radiologist rating of 3.59 out of 4.0 for overall image quality. Data Conclusion Reconstruction quality can be reliably predicted using the brain mask inlier fraction. Real‐time super‐resolution protocols could exploit this to terminate acquisition when enough high‐quality acquisitions have been collected. To achieve consistent 3D image quality it may be necessary to acquire more than five scans to compensate for severely motion‐corrupted acquisitions. Level of Evidence 3 Technical Efficacy 1

Abstract This International Consensus Classification and Nomenclature for the congenital bicuspid aortic valve condition recognizes 3 types of bicuspid valves: 1. The fused type (right-left cusp fusion, right-non-coronary cusp fusion and left-non-coronary cusp fusion phenotypes); 2. The 2-sinus type (latero-lateral and antero-posterior phenotypes); and 3. The partial-fusion (forme fruste) type. The presence of raphe and the symmetry of the fused type phenotypes are critical aspects to describe. The International Consensus also recognizes 3 types of bicuspid valve-associated aortopathy: 1. The ascending phenotype; 2. The root phenotype; and 3. Extended phenotypes.

Abstract This International evidence-based nomenclature and classification consensus on the congenital bicuspid aortic valve and its aortopathy recognizes 3 types of bicuspid aortic valve: 1. Fused type, with 3 phenotypes: right-left cusp fusion, right-non cusp fusion and left-non cusp fusion; 2. 2-sinus type with 2 phenotypes: Latero-lateral and antero-posterior; and 3. Partial-fusion or forme fruste. This consensus recognizes 3 bicuspid-aortopathy types: 1. Ascending phenotype; root phenotype; and 3. extended phenotypes.

Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual‐energy CT (DECT) and photon‐counting detector CT (PCD‐CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD‐CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi‐energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image‐guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.

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.

Abstract Familial studies were among the first to indicate that breast cancer prognosis has a heritable component. Subsequently many variants associated with prognosis have been identified using a range of techniques including genome-wide association studies (GWAs). Despite these advances, much of the heritability remains unexplained. In young women, breast cancer is characterised by a higher incidence of adverse pathological features, unique tumour gene expression profiles and worse survival. In addition, the association of risk with conventional epidemiological exposures is less clear in women with early onset. We hypothesise that some of these difference between early and late onset could be influenced by germline variation. To identify additional variants that influence breast cancer prognosis we conducted a two stage meta-analysis of four GWAs consisting of 6,042 patients from the UK (POSH), Finland (HEBCS), Germany (SUCCESS-A) and Australia (ABCFS). Cox-regression analyses were used to investigate overall survival (OS, n=1,101 events) and disease-free survival (DFS, n=1,316 events) with correction for oestrogen status (ER). These survival analyses were repeated in a subset of patients with early onset (aged ≤40 at diagnosis, n=2,315 patients, OS n=604 events, DFS n=716 events). Meta-analysis identified two intronic SNPs in ADAMTSL1 that were associated exclusively with early onset DFS, rs715212 (Pmeta=3.54x10-5) and rs10963755 (Pmeta=3.91x10-4) without heterogeneity between cohorts. Multivariable Cox-regression demonstrated that the effect of these SNPs were independent of the classical prognostic factors. Most importantly, rs715212 reached genome-wide significance (Pmultivariate=5.37x10-8) in the multivariate model. ADAMTSL1 encodes a glycoprotein that forms part of the extracellular matrix (ECM) and may function in cell-cell or cell-matrix interactions or may regulate other ADAMTS proteases. Previous studies have shown that ADAMTSL1 is hypermethylated in ER positive breast cancer tumours. Using GTEx to perform eQTL analysis, we found that rs715212 is associated with the expression of several biologically relevant genes AREG (P=0.035), TNF (P=0.015), FASLG (P=0.0031) and EGF (P=0.0018) in breast mammary tissue. Interestingly, separate studies have shown that AREG is overexpressed in ER-positive breast tumours from pre-menopausal women versus post-menopausal women. Furthermore, AREG is differentially expressed between parous and non-parous mammary glands and is persistently downregulated by parity, which suggests it may contribute to the susceptibility of the nulliparous gland to breast cancer. We conclude that rs715212 is associated with an increased risk of disease progression in patients with early onset and speculate that this could be due to an interaction with the expression of AREG. Note: This abstract was not presented at the meeting. Citation Format: Latha Kadalayil, Sofia Khan, Heli Nevanlinna, Peter A. Fasching, Fergus J. Couch, John Hopper, Jianjun Liu, Tom Maishman, Lorraine Durcan, Carl Bloomqvist, Andy Collins, Dianna Eccles, William Tapper. A meta-analysis of genome-wide association studies identifies novel loci that influence breast cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1322. doi:10.1158/1538-7445.AM2017-1322

PurposeLow contrast (LC) detectability is a common test criterion for diagnostic radiologic quality control (QC) programs. Automation of this test is desirable in order to reduce human variability and to speed up analysis. However, automation is challenging due to the complexity of the human visual perception system and the ability to create algorithms that mimic this response. This paper describes the development and testing of an automated LC detection algorithm for use in the analysis of magnetic resonance (MR) images of the American College of Radiology (ACR) QC phantom.MethodsThe detection algorithm includes fuzzy logic decision processes and various edge detection methods to quantify LC detectability. Algorithm performance was first evaluated using a single LC phantom MR image with the addition of incremental zero mean Gaussian noise resulting in a total of 200 images. A c‐statistic was calculated to determine the role of CNR to indicate when the algorithm would detect ten spokes. To evaluate inter‐rater agreement between experienced observers and the algorithm, a blinded observer study was performed on 196 LC phantom images acquired from nine clinical MR scanners. The nine scanners included two MR manufacturers and two field strengths (1.5 T, 3.0 T). Inter‐rater and algorithm‐rater agreement was quantified using Krippendorff's alpha.ResultsFor the Gaussian noise added data, CNR ranged from 0.519 to 11.7 with CNR being considered an excellent discriminator of algorithm performance (c‐statistic = 0.9777). Reviewer scoring of the clinical phantom data resulted in an inter‐rater agreement of 0.673 with the agreement between observers and algorithm equal to 0.652, both of which indicate significant agreement.ConclusionsThis study demonstrates that the detection of LC test patterns for MR imaging QC programs can be successfully developed and that their response can model the human visual detection system of expert MR QC readers.