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Background A total of 40% of patients with severe aortic stenosis (AS) have low-gradient AS, raising uncertainty about AS severity. Aortic valve calcification, measured by computed tomography (CT), is guideline-endorsed to aid in such cases. The performance of different CT-derived aortic valve areas (AVAs) is less well studied. Methods and Results Consecutive adult patients with presumed moderate and severe AS based on echocardiography (AVA measured by continuity equation on echocardiography <1.5 cm 2 ) who underwent cardiac CT were identified retrospectively. AVAs, measured by direct planimetry on CT (AVA CT ) and by a hybrid approach (AVA measured in a hybrid manner with echocardiography and CT [AVA Hybrid ]), were measured. Sex-specific aortic valve calcification thresholds (≥1200 Agatston units in women and ≥2000 Agatston units in men) were applied to adjudicate severe or nonsevere AS. A total of 215 patients (38.0% women; mean±SD age, 78±8 years) were included: normal flow, 59.5%; and low flow, 40.5%. Among the different thresholds for AVA CT and AVA Hybrid , diagnostic performance was the best for AVA CT <1.2 cm 2 (sensitivity, 85%; specificity, 26%; and accuracy, 72%), with no significant difference by flow status. The percentage of patients with correctly classified AS severity (correctly classified severe AS+correctly classified moderate AS) was as follows; AVA measured by continuity equation on echocardiography <1.0 cm 2 , 77%; AVA CT <1.2 cm 2 , 73%; AVA CT <1.0 cm 2 , 58%; AVA Hybrid <1.2 cm 2 , 59%; and AVA Hybrid <1.0 cm 2 , 45%. AVA CT cut points of 1.52 cm 2 for normal flow and 1.56 cm 2 for low flow, provided 95% specificity for excluding severe AS. Conclusions CT-derived AVAs have poor discrimination for AS severity. Using an AVA CT <1.2-cm 2 threshold to define severe AS can produce significant error. Larger AVA CT thresholds improve specificity.

Purpose: Short-term improvements in retinal anatomy are known to occur in preclinical models of photoreceptor transplantation. However, correlative changes over the long term are poorly understood. We aimed to develop a quantifiable imaging biomarker grading scheme, using noninvasive multimodal confocal scanning laser ophthalmoscopy (cSLO) imaging, to enable serial evaluation of photoreceptor transplantation over the long term., Methods: Photoreceptor cell suspensions or sheets from rhodopsin-green fluorescent protein mice were transplanted subretinally, into either NOD.CB17-Prkdc scid /J or C3H/HeJ-Pde6b rd1 mice. Multimodal cSLO imaging was performed serially for up to three months after transplantation. Imaging biomarkers were scored, and a grade was defined for each eye by integrating the scores. Image grades were correlated with immunohistochemistry (IHC) data., Results: Multimodal imaging enabled the extraction of quantitative imaging biomarkers including graft size, GFP intensity, graft length, on-target graft placement, intra-graft lamination, hemorrhage, retinal atrophy, and periretinal proliferation. Migration of transplanted material was observed. Changes in biomarker scores and grades were detected in 14/16 and 7/16 eyes, respectively. A high correlation was found between image grades and IHC parameters., Conclusions: Serial evaluation of multiple imaging biomarkers, when integrated into a per-eye grading scheme, enabled comprehensive tracking of longitudinal changes in photoreceptor cell grafts over time. The application of systematic multimodal in vivo imaging could be useful in increasing the efficiency of preclinical retinal cell transplantation studies in rodents and other animal models., Translational Relevance: By allowing longitudinal evaluation of the same animal over time, and providing quantifiable biomarkers, non-invasive multimodal imaging improves the efficiency of retinal transplantation studies in animal models. Such assays will facilitate the development of cell therapy for retinal diseases., Competing Interests: Disclosure: Y.V. Liu, None; S.K. Sodhi, None; G. Xue, None; D. Teng, None; D. Agakishiev, None; M.M. McNally, None; S. Harris-Bookman, None; C. McBride, None; G.J. Konar, None; M.S. Singh, None, (Copyright 2020 The Authors.)