2 research outputs found
Data_Sheet_1_Age-related differences in auditory spatial processing revealed by acoustic change complex.pdf
ObjectivesThe auditory spatial processing abilities mature throughout childhood and degenerate in older adults. This study aimed to compare the differences in onset cortical auditory evoked potentials (CAEPs) and location-evoked acoustic change complex (ACC) responses among children, adults, and the elderly and to investigate the impact of aging and development on ACC responses.DesignOne hundred and seventeen people were recruited in the study, including 57 typically-developed children, 30 adults, and 30 elderlies. The onset-CAEP evoked by white noise and ACC by sequential changes in azimuths were recorded. Latencies and amplitudes as a function of azimuths were analyzed using the analysis of variance, Pearson correlation analysis, and multiple linear regression model.ResultsThe ACC N1β-P2β amplitudes and latencies in adults, P1β-N1β amplitudes in children, and N1β amplitudes and latencies in the elderly were correlated with angles of shifts. The N1β-P2β and P2β amplitudes decreased in the elderly compared to adults. In Children, the ACC P1β-N1β responses gradually differentiated into the P1β-N1β-P2β complex. Multiple regression analysis showed that N1β-P2β amplitudes (R2β=β0.33) and P2β latencies (R2β=β0.18) were the two most variable predictors in adults, while in the elderly, N1β latencies (R2β=β0.26) explained most variances. Although the amplitudes of onset-CAEP differed at some angles, it could not predict angle changes as effectively as ACC responses.ConclusionThe location-evoked ACC responses varied among children, adults, and the elderly. The N1β-P2β amplitudes and P2β latencies in adults and N1β latencies in the elderly explained most variances of changes in spatial position. The differentiation of the N1β waveform was observed in children. Further research should be conducted across all age groups, along with behavioral assessments, to confirm the relationship between aging and immaturity in objective ACC responses and poorer subjective spatial performance.SignificanceACCs evoked by location changes were assessed in adults, children, and the elderly to explore the impact of aging and development on these differences.</p
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Dual output feature fusion networks for femoral segmentation and quantitative analysis of the knee joint
BackgroundMagnetic resonance imaging (MRI) is the preferred imaging modality for diagnosing knee disease. Segmentation of the knee MRI images is essential for subsequent quantification of clinical parameters and treatment planning for knee prosthesis replacement. However, the segmentation remains difficult due to individual differences in anatomy, the difficulty of obtaining accurate edges at lower resolutions, and the presence of speckle noise and artifacts in the images. In addition, radiologists must manually measure the knee's parameters which is a laborious and time-consuming process.PurposeAutomatic quantification of femoral morphological parameters can be of fundamental help in the design of prosthetic implants for the repair of the knee and the femur. Knowledge of knee femoral parameters can provide a basis for femoral repair of the knee, the design of fixation materials for femoral prostheses, and the replacement of prostheses.MethodsThis paper proposes a new deep network architecture to comprehensively address these challenges. A dual output model structure is proposed, with a high and low layer fusion extraction feature module designed to extract rich features through the cross-fusion mechanism. A multi-scale edge information extraction spatial feature module is also developed to address the boundary-blurring problem.ResultsBased on the precise automated segmentation results, 10 key clinical parameters were automatically measured for a knee femoral prosthesis replacement program. The correlation coefficients of the quantitative results of these parameters compared to manual results all achieved at least 0.92. The proposed method was extensively evaluated with MRIs of 78 patientsβ knees, and it consistently outperformed other methods used for segmentation.ConclusionsThe automated quantization process produced comparable measurements to those manually obtained by radiologists. This paper demonstrates the viability of automatic knee MRI image segmentation and quantitative analysis with the proposed method. This provides data to support the accuracy of assessing the progression and biomechanical changes of osteoarthritis of the knee using an automated process, thus saving valuable time for the radiologists and surgeons.</p