Locally Adaptive Total p-Variation Regularization for Non-Rigid Image Registration With Sliding Motion

Generalized div-curl based regularization for physically constrained deformable image registration

Variational image registration methods commonly employ a similarity metric and a regularization term that renders the minimization problem well-posed. However, many frequently used regularizations such as smoothness or curvature do not necessarily reflect the underlying physics that apply to anatomical deformations. This, in turn, can make the accurate estimation of complex deformations particularly challenging. Here, we present a new highly flexible regularization inspired from the physics of fluid dynamics which allows applying independent penalties on the divergence and curl of the deformations and/or their nth order derivative. The complexity of the proposed generalized div-curl regularization renders the problem particularly challenging using conventional optimization techniques. To this end, we develop a transformation model and an optimization scheme that uses the divergence and curl components of the deformation as control parameters for the registration. We demonstrate that the original unconstrained minimization problem reduces to a constrained problem for which we propose the use of the augmented Lagrangian method. Doing this, the equations of motion greatly simplify and become managable. Our experiments indicate that the proposed framework can be applied on a variety of different registration problems and produce highly accurate deformations with the desired physical properties.

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.

Collision-constrained deformable image registration framework for discontinuity management

Topological changes like sliding motion, sources and sinks are a significant challenge in image registration. This work proposes the use of the alternating direction method of multipliers as a general framework for constraining the registration of separate objects with individual deformation fields from overlapping in image registration. This constraint is enforced by introducing a collision detection algorithm from the field of computer graphics which results in a robust divide and conquer optimization strategy using Free-Form Deformations. A series of experiments demonstrate that the proposed framework performs superior with regards to the combination of intersection prevention and image registration including synthetic examples containing complex displacement patterns. The results show compliance with the non-intersection constraints while simultaneously preventing a decrease in registration accuracy. Furthermore, the application of the proposed algorithm to the DIR-Lab data set demonstrates that the framework generalizes to real data by validating it on a lung registration problem.

Volumetric multiphase ventilation imaging based on four-dimensional computed tomography for functional lung avoidance radiotherapy

PURPOSE

Current computed tomography (CT)-based lung ventilation imaging (CTVI) techniques derive a static ventilation image without temporal information. This research aims to develop a four-dimensional CT (4DCT)-based multiphase dynamic ventilation imaging framework capable of recovering the entire ventilation process throughout the breathing cycle for functional lung avoidance radiotherapy (FLART).

METHODS

A total of 15 free-breathing thoracic 4DCT scans of lung or esophageal cancer patients were collected from the public datasets. The lung region of each phase image was first delineated, and then the mask-free isotropic total variation image registration algorithm was used to derive the deformation vector fields between the end-expiration (EE) phase and other phases. As a surrogate of ventilation, the voxel-wise local expansion ratio of each phase relative to the EE phase was estimated using the parameterized Integrated Jacobian Formulation method in the EE phase coordinate. Lastly, the dynamic ventilation images were generated by warping these phase-specific local expansion distributions with a same geometry into their respective breathing phases. Quantitative analysis, including interphase Spearman correlation coefficients, voxel-wise, and regional-wise expansion/contraction tracking, were performed to indirectly validate the proposed method.

RESULTS

The proposed method maintains the physiological meaning of ventilation on each phase and enables to recover the dynamic lung ventilation process. The mean interphase Spearman correlations ranged between 0.23 ± 0.20 and 0.93 ± 0.04 and decreased near the EE phase. Only 26.2% (2.59E + 6 out of 9.89E + 6) of lung voxels exhibited the same expansion/contraction pattern as the global lung. Qualitative and quantitative evaluations of the interphase ventilation distribution difference show that ventilation spatiotemporal heterogeneities generally exist during respiration.

CONCLUSIONS

In contrast to conventional CTVI metrics, our method enables to extract additional phase-resolved respiration-correlated information and reflects the generally existed ventilation spatiotemporal heterogeneities. Subsequent studies with quantitative phase-by-phase cross-modality evaluations will further explore its potential to deepen our understanding of lung function and respiration mechanics and also to facilitate more accurate implementation of FLART.

Hierarchical anatomical structure-aware based thoracic CT images registration

Accurate thoracic CT image registration remains challenging due to complex joint deformations and different motion patterns in multiple organs/tissues during breathing. To combat this, we devise a hierarchical anatomical structure-aware based registration framework. It affords a coordination scheme necessary for constraining a general free-form deformation (FFD) during thoracic CT registration. The key is to integrate the deformations of different anatomical structures in a divide-and-conquer way. Specifically, a deformation ability-aware dissimilarity metric is proposed for complex joint deformations containing large-scale flexible deformation of the lung region, rigid displacement of the bone region, and small-scale flexible deformation of the rest region. Furthermore, a motion pattern-aware regularization is devised to handle different motion patterns, which contain sliding motion along the lung surface, almost no displacement of the spine and smooth deformation of other regions. Moreover, to accommodate large-scale deformation, a novel hierarchical strategy, wherein different anatomical structures are fused on the same control lattice, registers images from coarse to fine via elaborate Gaussian pyramids. Extensive experiments and comprehensive evaluations have been executed on the 4D-CT DIR and 3D DIR COPD datasets. It confirms that this newly proposed method is locally comparable to state-of-the-art registration methods specializing in local deformations, while guaranteeing overall accuracy. Additionally, in contrast to the current popular learning-based methods that typically require dozens of hours or more pre-training with powerful graphics cards, our method only takes an average of 63 s to register a case with an ordinary graphics card of RTX2080 SUPER, making our method still worth promoting. Our code is available at https://github.com/heluxixue/Structure_Aware_Registration/tree/master.