Registration Of Abdominal Images Research Articles

Enhanced multi-modal abdominal image registration via structural awareness and region-specific optimization

Multi-modal abdominal image registration is a critical step in achieving accurate diagnosis and treatment. However, the large field of view, complex organ structures, and significant anatomical variability in the abdominal region pose substantial challenges to precise multi-modal registration. To address these challenges, we propose a novel framework based on structural awareness and region-specific optimization, which progressively enhances registration accuracy through a hierarchical strategy. The core innovation lies in introducing the Modality Independent Structural Awareness (MISA) loss, which comprises two key components: first, it ensures structural consistency across modalities by deeply understanding the critical anatomical structures; second, it employs a region-specific weighting strategy to prioritize key anatomical regions, such as organ boundaries, thereby achieving high-precision, region-specific constraints in multi-modal registration. Experimental results demonstrate that our method outperforms state-of-the-art approaches on two public multi-modal abdominal datasets, particularly in accurately capturing complex anatomical structures and maintaining tissue consistency. Further analysis confirms that the MISA loss and its two innovative components are pivotal in enhancing multi-modal abdominal image registration accuracy.

OTMorph: Unsupervised Multi-domain Abdominal Medical Image Registration Using Neural Optimal Transport.

Deformable image registration is one of the essential processes in analyzing medical images. In particular, when diagnosing abdominal diseases such as hepatic cancer and lymphoma, multi-domain images scanned from different modalities or different imaging protocols are often used. However, they are not aligned due to scanning times, patient breathing, movement, etc. Although recent learning-based approaches can provide deformations in real-time with high performance, multi-domain abdominal image registration using deep learning is still challenging since the images in different domains have different characteristics such as image contrast and intensity ranges. To address this, this paper proposes a novel unsupervised multi-domain image registration framework using neural optimal transport, dubbed OTMorph. When moving and fixed volumes are given as input, a transport module of our proposed model learns the optimal transport plan to map data distributions from the moving to the fixed volumes and estimates a domain-transported volume. Subsequently, a registration module taking the transported volume can effectively estimate the deformation field, leading to deformation performance improvement. Experimental results on multi-domain image registration using multi-modality and multi-parametric abdominal medical images demonstrate that the proposed method provides superior deformable registration via the domain-transported image that alleviates the domain gap between the input images. Also, we attain the improvement even on out-of-distribution data, which indicates the superior generalizability of our model for the registration of various medical images. Our source code is available at https://github.com/boahK/OTMorph.

Weighted window attention and recover feature resolution-based network for deformable abdominal image registration

The Swin Transformer has attracted attention in the field of medical image analysis since its introduction in 2021 owing to its computational efficiency and long-range modeling capabilities. Because of these characteristics, the Swin Transformer can establish distant relationships between corresponding voxels at distant locations for complex abdominal image registration tasks. Transformer-based registration methods model the features of multiple fused voxels and output coarse-grained features of the same size. To enable the Transformer to output fine-grained information and strengthen the contribution of the Transformer in the registration model, we propose the recover feature resolution network (RFRNet), which converts the features outputted by the Transformer into fine-grained spatial information. In addition, fixed-window partitioning restricts the Transformer from modeling the global connection of semantic information with uncertain distances. Therefore, we proposed a weighted window attenuation (WWA) mechanism to achieve automatic global-scale interaction of window information after a window partitioning operation. Based on these improvements, we proposed a single-modal unsupervised deformable abnormal image registration model called RFR-WWANet. Qualitative and quantitative results showed that RFR-WWANet achieved significant improvements compared with the current state-of-the-art methods. Ablation experiments demonstrated the effectiveness of the RFRNet and WWA designs. Our code is available at https://github.com/MingR-Ma/RFR-WWANet.

DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR.

Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image Normalization (SyN). Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor .

Unsupervised Learning-Based CBCT-CT Deformable Image Registration for CBCT-Guided Abdominal Radiotherapy

Daily Cone beam CT (CBCT) imaging provides necessary anatomical information for accurate patient setup. Image quality of CBCT is usually far inferior to simulation CT scans. A workaround is to register the CT to the CBCT such that the contours and Hounsfield Unit (HU) values of the CT can be propagated to the CBCT. However, the inconsistent HU values across CT and CBCT make it less effective to use conventional image similarity measures. We aim to develop an unsupervised registration network to overcome this challenge in multimodal CT-CBCT image registration.We propose to integrate directional local structural similarity into an unsupervised learning framework to perform abdominal CT-CBCT image registration. Directional local structural similarity measures the image's self-similarity which reflects the underlying structural similarity regardless of the modality in use. The CBCT and CT images were separately processed to extract directional local structural similarity feature maps in different directions. We concatenated the directional local structural similarity feature maps and the original images as network input. Taking both the original images and their respective structural similarity feature maps as input allows the network to fully explore the potential correlations between CBCT and CT for accurate deformation vector field (DVF) prediction. Salient features learnt through previous iterations were highlighted by attention gates across layers to expedite the learning process. A 3D bicubic interpolation was used to up-sample and smooth the predicted DVF. We performed a leave-one-out cross validation with an image dataset of 45 patients to evaluate the proposed registration method. Normalized cross correlation (NCC) and target registration error (TRE) between CBCT and deformed CT were calculated to quantify the registration accuracy.Our results show that the alignment between the abdominal soft tissues has been greatly improved after registration for all patients. The mean and standard deviation of NCC and TRE were 0.97 (range 0.95-0.99) and 1.88 (range 1.03-2.67) mm. The proposed network allows for many datasets to be used as training datasets since ground truth DVF is not needed for the training process. The proposed network can predict a final DVF via a single forward prediction, which is faster than the conventional iterative registration algorithms.We have developed a novel unsupervised multimodal image registration method for CT-CBCT abdominal image registration, which does not need ground truth DVF for training, and demonstrated its feasibility. Taking both CBCT and CT images and their respective directional local structural similarity features as input, the proposed network performs direct DVF prediction to register the abdominal CT to CBCT images. This tool will be useful for future CBCT-guided radiotherapy of abdominal malignancies.

EFFICIENT AND AUTOMATIC ABDOMINAL IMAGE REGISTRATION BASED ON ACTIVE CONTOUR

Image registration is critical for making diagnostic decision and essential for image-guided surgery. To improve the quality and accuracy of healthcare, efficient registration is highly demanded. However, because of the non-rigid deformations caused by heartbeat and breath, abdominal image registration remains a challenging task. To address these issues, an automatic and elastic registration for abdominal images is proposed. The algorithm is divided into three steps: efficient non-iterative affine registration; elastic motion field extraction based on active contours; elastic registration based on motion field. The validation of the method on monomodality and multimodality abdominal images has demonstrated that the algorithm is reliable and efficient.