Dufresne Eléonore, Fortun Denis, Kremer Stéphane, Noblet Vincent
ICube UMR 7357, Université de Strasbourg, CNRS, Strasbourg, France.
Hôpitaux Universitaires de Strasbourg, Strasbourg, France.
Front Neuroimaging. 2022 Dec 22;1:1008128. doi: 10.3389/fnimg.2022.1008128. eCollection 2022.
Registration is a crucial step in the design of automatic change detection methods dedicated to longitudinal brain MRI. Even small registration inaccuracies can significantly deteriorate the detection performance by introducing numerous spurious detections. Rigid or affine registration are usually considered to align baseline and follow-up scans, as a pre-processing step before applying a change detection method. In the context of multiple sclerosis, using deformable registration can be required to capture the complex deformations due to brain atrophy. However, non-rigid registration can alter the shape of appearing and evolving lesions while minimizing the dissimilarity between the two images. To overcome this issue, we consider registration and change detection as intertwined problems that should be solved jointly. To this end, we formulate these two separate tasks as a single optimization problem involving a unique energy that models their coupling. We focus on intensity-based change detection and registration, but the approach is versatile and could be extended to other modeling choices. We show experimentally on synthetic and real data that the proposed joint approach overcomes the limitations of the sequential scheme.
配准是致力于纵向脑磁共振成像的自动变化检测方法设计中的关键步骤。即使是很小的配准误差也会通过引入大量虚假检测而显著降低检测性能。刚性或仿射配准通常被视为在应用变化检测方法之前对齐基线扫描和后续扫描的预处理步骤。在多发性硬化症的背景下，可能需要使用可变形配准来捕捉由于脑萎缩引起的复杂变形。然而，非刚性配准会改变出现和演变的病变形状，同时最小化两幅图像之间的差异。为了克服这个问题，我们将配准和变化检测视为应该共同解决的相互交织的问题。为此，我们将这两个单独的任务表述为一个涉及唯一能量的单一优化问题，该能量对它们的耦合进行建模。我们专注于基于强度的变化检测和配准，但该方法具有通用性，可以扩展到其他建模选择。我们在合成数据和真实数据上通过实验表明，所提出的联合方法克服了顺序方案的局限性。
