We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at doic https://w3id.org/synthmorph.

中文翻译：

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SynthMorph：在没有获取图像的情况下学习对比度不变配准


我们引入了一种无需获取成像数据即可学习图像配准的策略，从而产生与磁共振成像（MRI）引入的对比度无关的强大网络。虽然经典的配准方法准确地估计了图像之间的空间对应关系，但它们解决了每个新图像对的优化问题。基于学习的技术在测试时速度很快，但仅限于注册具有与训练期间看到的类似的对比度和几何内容的图像。我们建议通过利用多种合成标签图和图像的生成策略来消除对训练数据的依赖，使网络暴露于广泛的可变性中，迫使它们学习更多不变的特征。这种方法产生了强大的网络，可以准确地推广到广泛的 MRI 对比。我们以 3D 神经成像为重点进行了广泛的实验，表明即使网络在训练期间看不到目标对比度，该策略也能够稳健且准确地注册任意 MRI 对比度。我们使用单一模型证明了对比内部和对比之间的配准精度均超过了现有技术。至关重要的是，对从噪声分布合成的任意形状进行训练会产生有竞争力的性能，从而消除对获取的任何类型数据的依赖。此外，由于解剖标签图通常可用于感兴趣的解剖结构，因此我们表明，从这些图像合成图像可以显着提高性能，同时仍然避免对真实强度图像的需要。我们的代码可在 doic https://w3id.org/synthmorph 获取。