Carter, Rickey

Abstract Aims Left atrial (LA) function is impaired in heart failure with preserved ejection fraction (HFpEF) and predicts disease severity and risk of adverse outcome. We sought to evaluate whether LA strain could enhance diagnosis of HFpEF. Methods and results Consecutive patients with unexplained exertional dyspnoea and image quality suitable for measuring LA strain underwent invasive cardiopulmonary exercise testing to ascertain the presence of HFpEF or non-cardiac causes of dyspnoea (NCD). LA reservoir strain was measured in all patients, while LA conduit and booster strain were measured in patients with sinus rhythm. LA strain was measurable in 363 of 378 examinations (96.5%; HFpEF: 238, NCD: 125). LA reservoir strain (29 ± 16% vs. 40 ± 13%, P < 0.0001) and conduit strain (18 ± 10% vs. 22 ± 10%, P = 0.0001) were significantly impaired in HFpEF compared to NCD. Of all echocardiographic indices, LA reservoir strain best discriminated HFpEF from NCD [area under the curve (AUC) 0.719, P < 0.0001], outperforming E/e' (AUC difference +0.117, P < 0.0001), LA enlargement (+0.090, P = 0.001), tricuspid regurgitation velocity > 2.8 m/s (+0.082, P = 0.0085), left ventricular hypertrophy (+0.0159, P < 0.0001) and left ventricular global longitudinal strain (+0.0198, P < 0.0001). Indexing LA reservoir strain to estimated LA pressure (E/e') as a surrogate for LA compliance further improved diagnostic performance (AUC 0.772, P < 0.0001; +0.053 compared to LA reservoir strain alone, P = 0.003). Conclusion Left atrial reservoir strain may provide enhanced diagnostic accuracy beyond conventional echocardiographic measures to discriminate HFpEF from NCD. Further study is required to verify the diagnostic utility of LA strain.

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.