Search Results (145)

Background/Objective: Multimodal image registration plays a critical role in modern medical imaging, enabling the integration of complementary modalities such as positron emission tomography (PET) and computed tomography (CT). This study compares the performance of three widely used image registration techniques—Demons Image Registration with Modality Transformation, Free-Form Deformation using the Medical Image Registration Toolbox (MIRT), and MATLAB Intensity-Based Registration—in terms of improving PET/CT image alignment. Methods: A total of 100 matched PET/CT image slices from a clinical scanner were analysed. Preprocessing techniques, including histogram equalisation and contrast enhancement (via imadjust and adapthisteq), were applied to minimise intensity discrepancies. Each registration method was evaluated under varying parameter conditions with regard to sigma fluid (range 4–8), histogram bins (100 to 256), and interpolation methods (linear and cubic). Performance was assessed using quantitative metrics: root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), the Pearson correlation coefficient (PCC), and standard deviation (STD). Results: Demons registration achieved optimal performance at a sigma fluid value of 6, with an RMSE of 0.1529, and demonstrated superior computational efficiency. The MIRT showed better adaptability to complex anatomical deformations, with an RMSE of 0.1725. MATLAB Intensity-Based Registration, when combined with contrast enhancement, yielded the highest accuracy (RMSE = 0.1317 at alpha = 6). Preprocessing improved registration accuracy, reducing the RMSE by up to 16%. Conclusions: Each registration technique has distinct advantages: the Demons algorithm is ideal for time-sensitive tasks, the MIRT is suited to precision-driven applications, and MATLAB-based methods offer flexible processing for large datasets. This study provides a foundational framework for optimising PET/CT image registration in both research and clinical environments. Full article

Conventional projection systems typically require a fixed spatial configuration relative to the projection surface, with strict control over distance and angle. In contrast, UAV-mounted projectors overcome these constraints, enabling dynamic, large-scale projections onto non-planar and complex environments. However, such flexible scenarios introduce a key challenge: severe geometric distortions caused by intricate surface geometry and continuous camera–projector motion. To address this, we propose a novel image registration method based on global dense matching, which estimates the real-time optical flow field between the input projection image and the target surface. The estimated flow is used to pre-warp the image, ensuring that the projected content appears geometrically consistent across arbitrary, deformable surfaces. The core idea of our method lies in reformulating the geometric distortion correction task as a global feature matching problem, effectively reducing 3D spatial deformation into a 2D dense correspondence learning process. To support learning and evaluation, we construct a hybrid dataset that covers a wide range of projection scenarios, including diverse lighting conditions, object geometries, and projection contents. Extensive simulation and real-world experiments show that our method achieves superior accuracy and robustness in correcting geometric distortions in dynamic UAV projection, significantly enhancing visual fidelity in complex environments. This approach provides a practical solution for real-time, high-quality projection in UAV-based augmented reality, outdoor display, and aerial information delivery systems. Full article

In image-guided radiation therapy (IGRT), deformable image registration between computed tomography (CT) and cone beam computed tomography (CBCT) images remain challenging due to the computational cost of iterative algorithms and the data dependence of supervised deep learning methods. Implicit neural representation (INR) provides a promising alternative, but conventional multilayer perceptron (MLP) might struggle to efficiently represent complex, nonlinear deformations. This study introduces a novel INR-based registration framework that models the deformation as a continuous, time-varying velocity field, parameterized by a Kolmogorov–Arnold Network (KAN) constructed using Jacobi polynomials. To our knowledge, this is the first integration of KAN into medical image registration, establishing a new paradigm beyond standard MLP-based INR. For improved efficiency, the KAN estimates low-dimensional principal components of the velocity field, which are reconstructed via inverse principal component analysis and temporally integrated to derive the final deformation. This approach achieves a ~70% improvement in computational efficiency relative to direct velocity field modeling while ensuring smooth and topology-preserving transformations through velocity regularization. Evaluation on a publicly available pelvic CT–CBCT dataset demonstrates up to 6% improvement in registration accuracy over traditional iterative methods and ~3% over MLP-based INR baselines, indicating the potential of the proposed method as an efficient and generalizable alternative for deformable registration. Full article

Mueller matrix polarimetry is recently attracting more and more attention for its diagnostic potentials. However, for prevalently used division of time Mueller matrix polarimeter based on dual-rotating retarder scheme, beam drift induced by rotating polarizers and waveplates introduces spatial misalignment and pseudo-edge artifacts in imaging results, hindering following accurate microstructural features characterization. In this paper, we quantitatively analyze the beam drift phenomenon in dual-rotating retarder Mueller matrix microscopy and its impact on linear retardance measurement, which is frequently used to reflect tissue fiber arrangement. It is demonstrated that polarizer rotation induces larger beam drift than waveplate rotation due to surface non-uniformity and stress deformation. Furthermore, for waveplates rotated constantly in dual-rotating retarder scheme, their tilt within polarization state analyzer can result in more drift and throughput loss than those within polarization state generator. Finally, phantom and tissue experiments confirm that beam drift, rather than inherent optical path changes, dominates the systematic overestimation of linear retardance in boundary image regions. The findings highlight beam drift as a dominant error source for quantifying linear retardance, necessitating careful optical design alignment and a reliable registration algorithm to obtain highly accurate polarization data for training machine learning models of pathological diagnosis using Mueller matrix microscopy. Full article

Accurate registration of MRI and TRUS images is crucial for effective prostate cancer diagnosis and biopsy guidance, yet modality differences and non-rigid deformations pose significant challenges, especially in dynamic imaging. This study presents a novel cross-modal MRI-TRUS registration framework, leveraging a dual-encoder architecture with an Enhanced Cross-Modal Channel Attention (E-CMCA) module and a LSTM-Based Spatial Deformation Modeling Module. The E-CMCA module efficiently extracts and integrates multi-scale cross-modal features, while the LSTM-Based Spatial Deformation Modeling Module models temporal dynamics by processing depth-sliced 3D deformation fields as sequential data. A VecInt operation ensures smooth, diffeomorphic transformations, and a FuseConv layer enhances feature integration for precise alignment. Experiments on the $\mu$-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model significantly improves registration accuracy and performs robustly in both static 3D and dynamic 4D registration tasks. Experiments on the $\mu$-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model achieves a DSC of 0.865, RDSC of 0.898, TRE of 2.278 mm, and RTRE of 1.293, surpassing state-of-the-art methods and performing robustly in both static 3D and dynamic 4D registration tasks. Full article

Objectives: This study aimed to assess the precision of dose delivery to the target in adaptive carbon ion radiotherapy (CIRT) for locally advanced non-small cell lung cancer (LA-NSCLC) in cumulative dosimetry. Methods: Forty-six patients who received CIRT were included (64 Gy[relative biological effectiveness, RBE] in 16 fractions) with treatment plan computed tomography (CT) and weekly CT scans. Offline adaptive radiotherapy (ART) was administered if the dose distribution significantly worsened. Daily doses were calculated from weekly CTs and integrated into plan CT scans using deformable image registration. The dosimetry parameters were compared between the as-scheduled plan and adaptive replan in patients receiving ART. Survival outcomes and toxicity were compared between the ART and non-ART groups. Results: ART was implemented for 27 patients in whom adaptive replans significantly increased the median V_98% of the clinical tumor volume from 96.5% to 98.1% and D_98% from 60.5 to 62.7 Gy(RBE) compared with the as-scheduled plans (p < 0.001). The conformity and uniformity of the dose distribution improved (p < 0.001), with no significant differences in the doses to normal tissues (lungs, heart, esophagus, and spinal cord) from the as-scheduled plans (p > 0.05). The ART and non-ART groups demonstrated comparable local control, progression-free survival, and overall survival (p > 0.05). No grade 3 or higher radiation-related toxicities were observed. Conclusions: ART enhanced target dose coverage while maintaining acceptable normal tissue exposure, supporting weekly CT monitoring integration during CIRT for the timely intervention for anatomical variations, ensuring precise dose delivery in LA-NSCLC. Full article

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

Accurate image registration is essential for synthetic aperture radar (SAR) applications such as change detection, image fusion, and deformation monitoring. However, SAR image registration faces challenges including speckle noise, low-texture regions, and the geometric transformation caused by topographic relief due to side-looking radar imaging. To address these issues, this paper proposes a novel two-stage registration method, consisting of pre-registration and fine registration. In the pre-registration stage, the scale-invariant feature transform for the synthetic aperture radar (SAR-SIFT) algorithm is integrated into an iterative optimization framework to eliminate large-scale geometric discrepancies, ensuring a coarse but reliable initial alignment. In the fine registration stage, a novel similarity measure is introduced by combining frequency-domain phase congruency and spatial-domain gradient features, which enhances the robustness and accuracy of template matching, especially in edge-rich regions. For the topographic relief in the SAR images, an adaptive local stretching transformation strategy is proposed to correct the undulating areas. Experiments on five pairs of SAR images containing flat and undulating regions show that the proposed method achieves initial alignment errors below 10 pixels and final registration errors below 1 pixel. Compared with other methods, our approach obtains more correct matching pairs (up to 100+ per image pair), higher registration precision, and improved robustness under complex terrains. These results validate the accuracy and effectiveness of the proposed registration framework. Full article

Cross-modal image registration for unmanned aerial vehicle (UAV) platforms presents significant challenges due to large-scale deformations, distinct imaging mechanisms, and pronounced modality discrepancies. This paper proposes a novel multi-scale cascaded registration network based on style transfer that achieves superior performance: up to 67% reduction in mean squared error (from 0.0106 to 0.0068), 9.27% enhancement in normalized cross-correlation, 26% improvement in local normalized cross-correlation, and 8% increase in mutual information compared to state-of-the-art methods. The architecture integrates a cross-modal style transfer network (CSTNet) that transforms visible images into pseudo-infrared representations to unify modality characteristics, and a multi-scale cascaded registration network (MCRNet) that performs progressive spatial alignment across multiple resolution scales using diffeomorphic deformation modeling to ensure smooth and invertible transformations. A self-supervised learning paradigm based on image reconstruction eliminates reliance on manually annotated data while maintaining registration accuracy through synthetic deformation generation. Extensive experiments on the LLVIP dataset demonstrate the method’s robustness under challenging conditions involving large-scale transformations, with ablation studies confirming that style transfer contributes 28% MSE improvement and diffeomorphic registration prevents 10.6% performance degradation. The proposed approach provides a robust solution for cross-modal image registration in dynamic UAV environments, offering significant implications for downstream applications such as target detection, tracking, and surveillance. Full article

Deformable lung CT registration plays a crucial role in clinical applications such as respiratory motion tracking, disease progression analysis, and radiotherapy planning. While voxel-based registration has traditionally dominated this domain, it often suffers from high computational costs and sensitivity to intensity variations. In this work, we propose a novel point-based deformable registration framework tailored to the unique challenges of lung CT alignment. Our approach combines geometric keypoint attention at coarse resolutions to enhance the global correspondence with attention-based refinement modules at finer scales to accurately model subtle anatomical deformations. Furthermore, we adopt a bi-directional training strategy that enforces forward and backward consistency through cycle supervision, promoting anatomically coherent transformations. We evaluate our method on the large-scale Lung250M benchmark and achieve state-of-the-art results, significantly surpassing the existing voxel-based and point-based baselines in the target registration accuracy. These findings highlight the potential of sparse geometric modeling for complex respiratory motion and establish a strong foundation for future point-based deformable registration in thoracic imaging. Full article

Brain medical image registration is a fundamental premise for the computer-assisted treatment of brain diseases. The brain is one of the most important and complex organs of the human body, and it is very challenging to perform accurate and fast registration on it. Aiming at the problem of voxel folding in the deformation field and low registration accuracy when facing complex and fine objects, this paper proposed a fully convolutional multi-constraint cascaded attention network (MCANet). The network is composed of two registration sub-network cascades and performs coarse-to-fine registration of input image pairs in an iterative manner. The registration subnetwork is called the dilated self-attention network (DSNet), which incorporates dilated convolution combinations with different dilation rates and attention gate modules. During the training of MCANet, a double regularization constraint was applied to punish, in a targeted manner, the excessive deformation problem, so that the network can generate relatively smooth deformation while having high registration accuracy. Experimental results on the Mindboggle101 dataset showed that the registration accuracy of MCANet was significantly better than several existing advanced registration methods, and the network can complete relatively smooth registration. Full article

Transformer-based image registration methods have achieved notable success, but they still face challenges, such as difficulties in representing both global and local features, the inability of standard convolution operations to focus on key regions, and inefficiencies in restoring global context using the decoder. To address these issues, we extended the Swin-UNet architecture and incorporated dynamic snake convolution (DSConv) into the model, expanding it into three dimensions. This improvement enables the model to better capture spatial information at different scales, enhancing its adaptability to complex anatomical structures and their intricate components. Additionally, multi-scale dense skip connections were introduced to mitigate the spatial information loss caused by downsampling, enhancing the model’s ability to capture both global and local features. We also introduced a novel optimization-based weakly supervised strategy, which iteratively refines the deformation field generated during registration, enabling the model to produce more accurate registered images. Building on these innovations, we proposed OSS DSC-STUNet+ (Swin-UNet+ with 3D dynamic snake convolution). Experimental results on the IXI, OASIS, and LPBA40 brain MRI datasets demonstrated up to a 16.3% improvement in Dice coefficient compared to five classical methods. The model exhibits outstanding performance in terms of registration accuracy, efficiency, and feature preservation. Full article

Ethos adaptive radiotherapy is employed frequently in the pelvis to improve treatment accuracy by adapting to daily anatomical changes. The use of this CBCT-guided platform for abdominal treatments is made challenging by motion-related image artifacts that are detrimental to the Ethos auto-contouring process. We present a preliminary in silico study enabled by synthetic CBCT data of Ethos adaptive radiotherapy for pancreatic cancer. Simulation CT and daily CBCT images were collected from nonadaptive patients treated on Ethos. Contoured CBCTs drove structure-guided deformable registration from the CT to daily CBCTs, providing an approximate daily CT used to produce synthetic CBCT data. Two adaptive workflows were simulated using an Ethos emulator. Over 70 fractions across 10 patients in a solely deformation-based workflow, PTV prescription coverage increased by $23.3\pm 9.4\%$ through plan adaptation. Point doses to the stomach were reduced by $10.2\pm 9.3\%$. Ultimately, un-adapted plans satisfied target coverage and OAR constraints in $0\%$ and $6\%$ of fractions while adapted plans did so in $80\%$ of fractions. Anatomical variation led to poor performance in rigidly aligned un-adapted plans, illustrating the promise of Ethos adaptive radiotherapy in this region. This promise is balanced by the need for artifact reduction and questions regarding auto-contouring performance in the abdomen. Full article

Background: In neuroscience research about functional magnetic resonance imaging (fMRI), accurate inter-subject image registration is the basis for effective statistical analysis. Traditional fMRI registration methods are usually based on high-resolution structural MRI with clear anatomical structure features. However, this registration method based on structural information cannot achieve accurate functional consistency between subjects since the functional regions do not necessarily correspond to anatomical structures. In recent years, fMRI registration methods based on functional information have emerged, which usually ignore the importance of structural MRI information. Methods: In this study, we proposed a non-rigid cycle consistent bidirectional network with Transformer for unsupervised deformable functional MRI registration. The work achieves fMRI registration through structural MRI registration, and functional information is introduced to improve registration performance. Specifically, we employ a bidirectional registration network that implements forward and reverse registration between image pairs and apply Transformer in the registration network to establish remote spatial mapping between image voxels. Functional and structural information are integrated by introducing the local functional connectivity pattern, the local functional connectivity features of the whole brain are extracted as functional information. The proposed registration method was experimented on real fMRI datasets, and qualitative and quantitative evaluations of the quality of the registration method were implemented on the test dataset using relevant evaluation metrics. We implemented group ICA analysis in brain functional networks after registration. Functional consistency was evaluated on the resulting t-maps. Results: Compared with non-learning-based methods (Affine, Syn) and learning-based methods (Transmorph-tiny, Cyclemorph, VoxelMorph x2), our method improves the peak t-value of t-maps on DMN, VN, CEN, and SMN to 18.7, 16.5, 16.6, and 17.3 and the mean number of suprathreshold voxels (p < 0.05, t > 5.01) on the four networks to 2596.25, and there is an average improvement in peak t-value of 23.79%, 12.74%, 12.27%, 7.32%, and 5.43%. Conclusions: The experimental results show that the registration method of this study improves the structural and functional consistency between fMRI with superior registration performance. Full article

Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF data and the instability of UAV platforms. These challenges stem from the diversity of LVF data bands and significant inter-band differences. Even after geometric processing, adjacent flight lines still exhibit varying degrees of geometric deformation. In this paper, a progressive grouping-based strategy for iterative band selection and registration is proposed. In addition, an improved Scale-Invariant Feature Transform (SIFT) algorithm, termed the Double Sufficiency–SIFT (DS-SIFT) algorithm, is introduced. This method first groups bands, selects the optimal reference band, and performs coarse registration based on the SIFT method. Subsequently, during the fine registration stage, it introduces an improved position/scale/orientation joint SIFT registration algorithm (IPSO-SIFT) that integrates partitioning and the principle of structural similarity. This algorithm iteratively refines registration based on the grouping results. Experimental data obtained from a self-developed and integrated LVF hyperspectral remote sensing system are utilized to verify the effectiveness of the proposed algorithm. A comparison with classical algorithms, such as SIFT and PSO-SIFT, demonstrates that the registration of LVF hyperspectral data using the proposed method achieves superior accuracy and efficiency. Full article