RESUMEN
Objective: To develop and evaluate a Chinese version of the Symptom Questionnaire for Visual Dysfunctions (CSQVD) to quantify visual dysfunction symptoms in school-age children with various eye diseases, and to explore the relationship between ophthalmological disorders and visual dysfunction symptoms. Methods: Following standard scale adaptation procedures, the Symptom Questionnaire for Visual Dysfunctions (SQVD) was translated into Chinese (CSQVD). We employed random sampling to survey 198 outpatients aged 7-18 to assess the psychometric properties of the CSQVD. Using the reliable and validated questionnaire, we evaluated the determinants of visual dysfunction symptoms among 406 school-age patients at an eye center. The CSQVD scores were correlated with demographic and clinical variables, including gender, age, eye position, refractive power, and best-corrected visual acuity. Univariate analysis identified potential risk factors, followed by binary logistic regression and multiple linear regression analysis on factors with a P-value <0.05. Results: The CSQVD scale's critical ratio (CR) values ranged from 6.028 to 10.604. The Cronbach's Alpha coefficient was 0.779, and Spearman-Brown split-half reliability was also 0.779. The I-CVI varied from 0.83 to 1.000, the S-CVI/Ave was 0.857, and the KMO value was 0.821. Multifactorial regression analysis indicated that high myopia (OR â= â5.744, 95% CI [1.632, 20.218], P â= â0.006) and amblyopia (OR â= â9.302, 95% CI [1.878, 46.058], P â= â0.006) were significant predictors of CSQVD symptoms. Multiple linear regression analysis showed that BCVA of amblyopic eyes (B â= â-5.052, 95% CI [-7.779, 2.325], P â= â0.000) and SE power (B â= â-0.234, 95% CI [-0.375, 0.205], P â= â0.001) significantly affected the CSQVD scale scores. Conclusions: The Chinese version of the SQVD scale (CSQVD) demonstrates good feasibility, discriminatory power, validity, and reliability in assessing Chinese school-aged children. Furthermore, those who have severe myopia and amblyopia reported more visual dysfunction symptoms.
RESUMEN
Head motion correction is an essential component of brain PET imaging, in which even motion of small magnitude can greatly degrade image quality and introduce artifacts. Building upon previous work, we propose a new head motion correction framework taking fast reconstructions as input. The main characteristics of the proposed method are: (i) the adoption of a high-resolution short-frame fast reconstruction workflow; (ii) the development of a novel encoder for PET data representation extraction; and (iii) the implementation of data augmentation techniques. Ablation studies are conducted to assess the individual contributions of each of these design choices. Furthermore, multi-subject studies are conducted on an 18F-FPEB dataset, and the method performance is qualitatively and quantitatively evaluated by MOLAR reconstruction study and corresponding brain Region of Interest (ROI) Standard Uptake Values (SUV) evaluation. Additionally, we also compared our method with a conventional intensity-based registration method. Our results demonstrate that the proposed method outperforms other methods on all subjects, and can accurately estimate motion for subjects out of the training set. All code is publicly available on GitHub: https://github.com/OnofreyLab/dl-hmc_fast_recon_miccai2023.
RESUMEN
Head movement during long scan sessions degrades the quality of reconstruction in positron emission tomography (PET) and introduces artifacts, which limits clinical diagnosis and treatment. Recent deep learning-based motion correction work utilized raw PET list-mode data and hardware motion tracking (HMT) to learn head motion in a supervised manner. However, motion prediction results were not robust to testing subjects outside the training data domain. In this paper, we integrate a cross-attention mechanism into the supervised deep learning network to improve motion correction across test subjects. Specifically, cross-attention learns the spatial correspondence between the reference images and moving images to explicitly focus the model on the most correlative inherent information - the head region the motion correction. We validate our approach on brain PET data from two different scanners: HRRT without time of flight (ToF) and mCT with ToF. Compared with traditional and deep learning benchmarks, our network improved the performance of motion correction by 58% and 26% in translation and rotation, respectively, in multi-subject testing in HRRT studies. In mCT studies, our approach improved performance by 66% and 64% for translation and rotation, respectively. Our results demonstrate that cross-attention has the potential to improve the quality of brain PET image reconstruction without the dependence on HMT. All code will be released on GitHub: https://github.com/OnofreyLab/dl_hmc_attention_mlcn2023.