用快速梯度计算替代传统脑部影像分析，提速6.3倍仍保持高精度。
Efficient Brain Imaging Analysis for Alzheimer's and Dementia Detection Using Convolution-Derivative Operations
- 用Sobel核角度差（SKAD）代替雅可比图，通过梯度局部分析量化体积变化
- 在多个数据集上速度比雅可比图快6.3倍，准确率相当
- 适合需要快速处理的大规模脑影像筛查，尤其适用于临床早期诊断
阿尔茨海默病（AD）以进行性神经退行性变导致大脑结构改变为特征，早期检测对干预至关重要。基于体素形态学（VBM）的空间配准生成的雅可比图在解释与AD相关的体积变化方面具有重要作用，但其计算成本高，限制了临床应用。本研究提出一种计算高效的替代方法——Sobel核角度差（SKAD），该方法为一种导数运算，通过局部梯度分析优化体积变化的量化。它能高效提取关键空间区域的梯度幅值变化，捕捉局部体积变异。在多个医学数据集上的评估表明，SKAD比雅可比图快6.3倍，同时保持相近的准确性。该方法在神经影像研究和临床实践中均展现出高效且具竞争力的潜力。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is characterized by progressive neurodegeneration and results in detrimental structural changes in human brains. Detecting these changes is crucial for early diagnosis and timely intervention of disease progression. Jacobian maps, derived from spatial normalization in voxel-based morphometry (VBM), have been instrumental in interpreting volume alterations associated with AD. However, the computational cost of generating Jacobian maps limits its clinical adoption. In this study, we explore alternative methods and propose Sobel kernel angle difference (SKAD) as a computationally efficient alternative. SKAD is a derivative operation that offers an optimized approach to quantifying volumetric alterations through localized analysis of the gradients. By efficiently extracting gradient amplitude changes at critical spatial regions, this derivative operation captures regional volume variations Evaluation of SKAD over various medical datasets demonstrates that it is 6.3x faster than Jacobian maps while still maintaining comparable accuracy. This makes it an efficient and competitive approach in neuroimaging research and clinical practice.
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