Clinicians compare breast DCE-MRI after neoadjuvant chemotherapy (NAC) with
pre-treatment scans to evaluate the response to NAC. Clinical evidence supports
that accurate longitudinal deformable registration without deforming treated
tumor regions is key to quantifying tumor changes. We propose a conditional
pyramid registration network based on unsupervised keypoint detection and
selective volume-preserving to quantify changes over time. In this approach, we
extract the structural and the abnormal keypoints from DCE-MRI, apply the
structural keypoints for the registration algorithm to restrict large
deformation, and employ volume-preserving loss based on abnormal keypoints to
keep the volume of the tumor unchanged after registration. We use a clinical
dataset with 1630 MRI scans from 314 patients treated with NAC. The results
demonstrate that our method registers with better performance and better volume
preservation of the tumors. Furthermore, a local-global-combining biomarker
based on the proposed method achieves high accuracy in pathological complete
response (pCR) prediction, indicating that predictive information exists
outside tumor regions. The biomarkers could potentially be used to avoid
unnecessary surgeries for certain patients. It may be valuable for clinicians
and/or computer systems to conduct follow-up tumor segmentation and response
prediction on images registered by our method. Our code is available on
https://github.com/fiy2W/Treatment-aware-Longitudinal-Registration.

通过无监督关键点检测和选择性体积保持的有条件金字塔式注册网络，我们提出了一种用于评估新辅助化疗（NAC）后乳腺 DCE-MRI 的长期变形注册，以精确量化肿瘤变化。在这种方法中，我们从 DCE-MRI 中提取结构和异常关键点，将结构关键点应用于注册算法以限制大变形，并使用基于异常关键点的体积保持损失，在注册后保持肿瘤体积不变。使用 314 名接受 NAC 治疗的患者的 1630 个 MRI 扫描的临床数据集验证了我们方法具有更好的注册性能和更好的肿瘤体积保持。此外，基于我们提出的方法的局部 - 全局结合生物标志物在病理完全缓解（pCR）预测中取得了高准确性，表明预测信息存在于肿瘤区域之外。这些生物标志物有可能用于避免对某些患者进行不必要的手术。对于临床医生和 / 或计算机系统来说，使用我们的注册方法对图像进行随访肿瘤分割和响应预测可能非常有价值。我们的代码可在 https://github.com/fiy2W/Treatment-aware-Longitudinal-Registration 获得。

不变形还是变形：基于无监督关键点检测的新辅助化疗乳腺动态对比增强磁共振成像的治疗感知纵向配准

To deform or not: treatment-aware longitudinal registration for breast  DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection

Riemannian manifold Hamiltonian Monte Carlo is traditionally carried out
using the generalized leapfrog integrator. However, this integrator is not the
only choice and other integrators yielding valid Markov chain transition
operators may be considered. In this work, we examine the implicit midpoint
integrator as an alternative to the generalized leapfrog integrator. We discuss
advantages and disadvantages of the implicit midpoint integrator for
Hamiltonian Monte Carlo, its theoretical properties, and an empirical
assessment of the critical attributes of such an integrator for Hamiltonian
Monte Carlo: energy conservation, volume preservation, and reversibility.
Empirically, we find that while leapfrog iterations are faster, the implicit
midpoint integrator has better energy conservation, leading to higher
acceptance rates, as well as better conservation of volume and better
reversibility, arguably yielding a more accurate sampling procedure.

本文比较了广义 leapfrog integrator 和 implicit midpoint integrator 在 Hamiltonian Monte Carlo 中的优缺点和理论性质，并通过实验证明 implicit midpoint integrator 不仅能更好地保持能量和体积守恒，而且采样效果更准确。