We present a pipeline for unbiased and robust multimodal registration of
neuroimaging modalities with minimal pre-processing. While typical multimodal
studies need to use multiple independent processing pipelines, with diverse
options and hyperparameters, we propose a single and structured framework to
jointly process different image modalities. The use of state-of-the-art
learning-based techniques enables fast inferences, which makes the presented
method suitable for large-scale and/or multi-cohort datasets with a diverse
number of modalities per session. The pipeline currently works with structural
MRI, resting state fMRI and amyloid PET images. We show the predictive power of
the derived biomarkers using in a case-control study and study the cross-modal
relationship between different image modalities. The code can be found in
https: //github.com/acasamitjana/JUMP.

利用最少的预处理提供无偏差和稳健的多模式神经影像注册，通过单一结构化框架联合处理不同图像模式，使用先进的基于学习的技术实现快速推论，适用于大规模和 / 或多队列数据集，目前可处理结构 MRI、静息状态 fMRI 和淀粉样 PET 图像，通过案例对照研究证明了所得生物标志物的预测能力，并研究了不同图像模式之间的跨模式关系。

JUMP: 一种用于神经影像的联合多模态注册流程的最小预处理方法

JUMP: A joint multimodal registration pipeline for neuroimaging with  minimal preprocessing

Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.

本研究提出了一种新的自动化方法来有效进行组织学和 MRI 之间的多模式图像配准，通过对图像进行级线表示以达到对比度不变性，并通过提取切线获得的形状元素进行投影不变性编码，以识别两幅图像之间的公共曲线段

利用形态元素对组织学和磁共振图像的局部到整体配准

Part-to-whole Registration of Histology and MRI using Shape Elements

Multimodal registration is a challenging problem in medical imaging due the
high variability of tissue appearance under different imaging modalities. The
crucial component here is the choice of the right similarity measure. We make a
step towards a general learning-based solution that can be adapted to specific
situations and present a metric based on a convolutional neural network. Our
network can be trained from scratch even from a few aligned image pairs. The
metric is validated on intersubject deformable registration on a dataset
different from the one used for training, demonstrating good generalization. In
this task, we outperform mutual information by a significant margin.

使用卷积神经网络构建了一种新的相似度度量方法，该方法可以应用于多模态医学图像的配准问题。在跨人注册任务上，该方法实现了比互信息更好的性能。