Deformable Image Research Articles

Deformable vector fields warping for modelling of irregular breathing.

Purpose
4D computed tomography (4DCT) is the clinical standard to image organ motion in radiotherapy, although it is limited in imaging breathing variability. We propose a method to transfer breathing motion across longitudinal imaging datasets to include intra-patient variability and verify its performance in lung cancer patients. 
Methods 
Five repeated control 4DCTs for 6 non-small cell lung cancer patients were combined into multi-breath datasets (m4DCT) by merging stages of deformable image registration to isolate respiratory motion. The displacement of the centre of mass of the primary tumour and its volume changes were evaluated to quantify intra-patient differences. Internal target volumes defined on the m4DCT were compared with those conventionally drawn on the 4DCT.
Results 
Motion analysis suggests no discontinuity at the junction between successive breaths, confirming the method's ability to merge repeated imaging into a continuum. Motion (variability) is primarily in superior-inferior direction and goes from 14.4 mm (8.7 mm) down to 0.1 mm (0.6 mm), respectively for tumours located in the lower lobes or most apical ones. On average, up to 65% and 74% of the tumour volume was subject to expansion or contraction in the inhalation and exhalation phases. These variations lead to an enlargement of the ITV up to 8% of its volume in our dataset.
Conclusion 
4DCT can be extended to model variable breathing motion by adding synthetic phases from multiple time-resolved images. The inclusion of this improved knowledge of patients' breathing allows better definition of treatment volumes and their margins for radiation therapy.&#xD.

Dysphagia in Head and Neck Radiotherapy: The Influence of Pharyngeal Constrictor Anatomy and Dosimetry.

The goal of this study was to identify which anatomical and dosimetric changes correlated with late patient-reported dysphagia throughout the course of head and neck chemo-radiotherapy treatment. The patient cohort (n = 64) considered oropharyngeal and nasopharyngeal patients treated with curative intent, exhibiting no baseline dysphagia with a follow-up time greater than one year. Patients completed the MD Anderson Dysphagia Inventory during a follow-up visit. A composite score was measured ranging from 20 to 100, with a low score indicating a high symptom burden; a score ≤60 indicated patient-reported dysphagia. The pharyngeal (PCM) and cricopharyngeal constrictor muscles (CPM) were contoured on a planning CT image and adapted to weekly cone-beam CT anatomy using deformable image registration and dose was accumulated using weighted dose-volume histogram curves. The PCM and CPM were examined for volume, thickness, and dosimetric changes across treatment with the results correlated to symptom group. Anatomical evaluation indicated the PCM thickness increased more during treatment for patients with dysphagia, with base of C2 vertebrae (p = 0.04) and superior-inferior middle PCM (p = 0.01) thicknesses indicating a 1.0-1.5mm increase. The planned and delivered mean dose and DVH metrics to PCM and CPM were found to be within random error measured for the dose accumulation, indicating delivered and planned dose are equivalent. The PCM and CPM organs were found to lie approximately 5mm closer to high dose gradients in patients exhibiting dysphagia. The volume, thickness, and high dose gradient metrics may be useful metrics to identify patients at risk of late patient-reported dysphagia.

In situ SEM side observation of asperity behavior during sliding contact

The performance and evolution characteristics of the friction interface are crucial for the design optimization of material friction and wear, as well as the revelation of the mechanism of seismic sliding motion. To understand the friction behavior of rough surfaces, it is essential to understand the physical process of interaction among asperities. However, visualizing the contact of asperities is challenging because materials are typically opaque, and the dimensions of asperities are usually in the micron range. This study developed an in-situ scanning electron microscope friction device and a micro speckle fabrication method to measure the strain field of regular asperities from the side, and synchronously measure the macroscopic friction coefficient. In-situ friction experiments were conducted on brass and silicon materials. The results indicate that directly correlating the friction coefficient with the deformation and failure images of asperities can effectively explain the evolution process and differences in friction coefficient for the two materials, validating the performance of the device. Different failure modes of asperities were observed, including severe plastic deformation in brass asperities and fracture-producing large particles in silicon asperities. The critical transition of asperity failure modes in the experiments was analyzed based on a theoretical model. The phase diagram of asperity failure modes and friction coefficient evolution was plotted, providing potential explanation for the evolution of friction coefficient in friction experiments on randomly rough surfaces. The developed device in this study can be used for non-transparent materials and helps reveal the microscopic mechanisms behind experimental phenomena.

On the RFI Detection in Differential Interferometric Synthetic Aperture Radar

Abstract. Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites.

Influence of learned landmark correspondences on lung CT registration.

Disease or injury may cause a change in the biomechanical properties of the lungs, which can alter lung function. Image registration can be used to measure lung ventilation and quantify volume change, which can be a useful diagnostic aid. However, lung registration is a challenging problem because of the variation in deformation along the lungs, sliding motion of the lungs along the ribs, and change indensity. Landmark correspondences have been used to make deformable image registration robust to largedisplacements. To tackle the challenging task of intra-patient lung computed tomography (CT) registration, we extend the landmark correspondence prediction model deep convolutional neural network-Match by introducing a soft mask loss term to encourage landmark correspondences in specific regions and avoid the use of a mask during inference. To produce realistic deformations to train the landmark correspondence model, we use data-driven synthetic transformations. We study the influence of these learned landmark correspondences on lung CT registration by integrating them into intensity-based registration as a distance-basedpenalty. Our results on the public thoracic CT dataset COPDgene show that using learned landmark correspondences as a soft constraint can reduce median registration error from approximately 5.46 to 4.08mm compared to standard intensity-based registration, in the absence of lungmasks. We show that using landmark correspondences results in minor improvements in local alignment, while significantly improving globalalignment.

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.

Comparative analysis of left ventricle function and deformation imaging in short and long axis plane in cardiac magnetic resonance imaging.

Advancements in cardiac imaging have revolutionized our understanding of ventricular contraction. While ejection fraction (EF) is still the gold standard parameter to assess left ventricle (LV) function, strain imaging (SI) has provided valuable insights into ventricular mechanics. The lack of an integrative method including SI parameters in a single, validated formula may limit its use. Our aim was to compare different methods for evaluating global circumferential strain (GCS) and their relationship with global longitudinal strain (GLS) and EF in CMR and how the different evaluations fit in the theoretical relationship between EF and global strain. Retrospective monocenter study. Inclusion of every patient who underwent a CMR during a 15 months period with various clinical indication (congenital heart defect, myocarditis, cardiomyopathy). A minimum of three LV long-axis planes and a stack of short-axis slices covering the LV using classical steady-state free precession cine sequences. A single assessment of GLS on long axis (LAX) slices and a double assessment of GCS and EF with both short axis (SAX) and LAX slices were made by a single experienced CMR investigator. GCS-SAX and GCS-LAX were correlated (r = 0.77, P < 0.001) without being interchangeable with a high reproducibility for GCS, GLS and EF. EF calculated from LAX images showed an overestimation compared to EF derived from SAX images of 7%. The correlation between calculated EF and theoretical EF derived from SI was high (r = 0.88 with EF-SAX, 0.95 with EF-LAX). This study highlights the need to integrate strain imaging techniques into clinical by incorporating strain parameters into EF calculations, because it gives a deeper understanding of cardiac mechanics.

Abstract PO5-22-04: Genomic analysis of local recurrences following risk adapted breast radiotherapy in the IMPORT trials defines 'true recurrences' and 'new primaries'

Abstract Background: Most ipsilateral breast tumor recurrences (IBTR) following breast conserving surgery occur near the tumor bed (Veronisi et al, Ann Oncol 2001) and are considered to represent ‘true recurrence’ (derived from residual malignant cells of the index cancer). A smaller number occur elsewhere in the breast and are thought to be ‘new primaries’ (independently occurring cancer). The IMPORT LOW (Coles et al, The Lancet 2017) and IMPORT HIGH (Coles et al, The Lancet 2023) trials investigated adaptation of radiotherapy dose-volume to this spatially varying risk, testing partial breast irradiation and simultaneous integrated boost in patients with tumors at low and high risk of IBTR respectively. We analysed genomic relationships between index cancers and recurrences in these trials, to ascertain the frequency of true recurrences and unrelated new primaries, and their spatial distribution. Methodology: FFPE blocks were obtained from 137 patients who developed subsequent ipsi- or contralateral breast cancer: 66 from IMPORT HIGH and 71 from IMPORT LOW (4 with bilateral second cancers). DNA extracted from index and subsequent cancers underwent shallow whole genome sequencing (sWGS) using the Illumina NovaSeq 6000 system. Copy number profiles were derived from sWGS data using the R package QDNAseq(Scheinin et al, Genome Res 2014); those that failed QC criteria were removed. Relatedness of tumor pairs was determined using the R package Breakclone (Lips et al, Nat Genet 2022). This approach uses individual copy number aberration breakpoint position rather than events at chromosomal arm level. The p value cutoff for relatedness was 0.01; values between 0.01 and 0.05 were considered ambiguous. Researchers were blinded to clinical information prior to results. Results: In 80/137 tumor pairs both copy number profiles met QC criteria and clonal relationship could be ascertained. In total 26/80 were considered related, 20/80 ambiguous and 34/40 non-related (Table 1). 16/26 ipsilateral subsequent cancers in IMPORT HIGH were clonally related to the primary tumor and could be considered true recurrences. In 5/26 it was impossible to accurately call clonality as pairs shared only common copy number changes such as 1q or 8q gain and 16q loss. 5/26 were considered new primaries. In the lower risk IMPORT LOW cohort, subsequent cancers appear to have similar chances of being related or independent. As expected, most contralateral tumors in IMPORT LOW showed no evidence of a clonal relationship to the index tumors. In both trials an ambiguous subgroup of pairs shared some common copy number changes suggesting similar phenotype.5/14 contralateral tumors in IMPORT HIGH, in which some women were also at high risk of systemic relapse, shared very similar copy number profiles to the index tumor and may be metastases rather than new primaries. Discussion: A considerable number of tumors classed as IBTR appear genomically unrelated to the index cancer particularly in low risk cancer; this biology is relevant to clinical management both initially and at ‘recurrence’. The finding that 5/14 contralateral cancers in the high risk group appear to be clonally related suggests that some may be metastases. Results are being confirmed by targeted sequencing and will be correlated with recorded spatial and clinicopathological data and patient outcome. Future work will analyse spatial relationships more precisely using deformable image registration. *From the 77 patients with baseline circulating tumor DNA samples, 2 patients were excluded because their samples failed during assay quality control. Table. PIK3CA and ESR1 alterations detected in circulating tumor DNA at baseline and post abemaciclib treatment Citation Format: Sara Lightowlers, Maria Roman-Escorza, Elena Provenzano, Judith Bliss, Jason Carroll, H Y Charlie Chan, Clare Griffin, Joanne Haviland, Monica Jefford, Anna Kirby, Navita Somaiah, Mark Sydenham, Jenny Titley, John Yarnold, Charlotte Coles, Elinor Sawyer. Genomic analysis of local recurrences following risk adapted breast radiotherapy in the IMPORT trials defines 'true recurrences' and 'new primaries' [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-22-04.

1455: Deep-learning deformable image registration for 4DCT/3DMRI label propagation

1455: Deep-learning deformable image registration for 4DCT/3DMRI label propagation

A multi-institutional comparison of retrospective deformable dose accumulation for online adaptive magnetic resonance-guided radiotherapy

Background and PurposeApplication of different deformable dose accumulation (DDA) solutions makes institutional comparisons after online-adaptive magnetic resonance-guided radiotherapy (OA-MRgRT) challenging. The aim of this multi-institutional study was to analyze accuracy and agreement of DDA-implementations in OA-MRgRT. Material and MethodsOne gold standard (GS) case deformed with a biomechanical-model and five clinical cases consisting of prostate (2x), cervix, liver, and lymph node cancer, treated with OA-MRgRT, were analyzed. Six centers conducted DDA using institutional implementations. Deformable image registration (DIR) and DDA results were compared using the contour metrics Dice Similarity Coefficient (DSC), surface-DSC, Hausdorff-distance (HD95%), and accumulated dose-volume histograms (DVHs) analyzed via intraclass correlation coefficient (ICC) and clinical dosimetric criteria (CDC). ResultsFor the GS, median DDA errors ranged from 0.0 to 2.8 Gy across contours and implementations. DIR of clinical cases resulted in DSC > 0.8 for up to 81.3% of contours and a variability of surface-DSC values depending on the implementation. Maximum HD95%=73.3 mm was found for duodenum in the liver case. Although DVH ICC > 0.90 was found after DDA for all but two contours, relevant absolute CDC differences were observed in clinical cases: Prostate I/II showed maximum differences in bladder V28Gy (10.2/7.6%), while for cervix, liver, and lymph node the highest differences were found for rectum D2cm3 (2.8 Gy), duodenum Dmax (7.1 Gy), and rectum D0.5cm3 (4.6 Gy). ConclusionOverall, high agreement was found between the different DIR and DDA implementations. Case- and algorithm-dependent differences were observed, leading to potentially clinically relevant results. Larger studies are needed to define future DDA-guidelines.

3357: Impact of deformable image registration uncertainties for external beam radiotherapy applications

3357: Impact of deformable image registration uncertainties for external beam radiotherapy applications

3540: Tools and recommendations for commissioning and quality assurance of deformable image registration in radiotherapy

3540: Tools and recommendations for commissioning and quality assurance of deformable image registration in radiotherapy

Abstract 1004: Three-dimensional Characterization Of Sex Differences In Abdominal Aortic Aneurysm Progression Via Vascular Deformation Mapping

Introduction: Although Abdominal Aortic Aneurysms (AAA) are more common in men, women are at greater risk for AAA growth and rupture. Vascular deformation mapping (VDM) is a novel technique that utilizes deformable image registration to qualify and quantify three-dimensions (3D) AAA growth using routing clinical computed tomography angiogram (CTA) images. Hypothesis: We hypothesize that females will have higher rates of focal AAA growth when compared to males. Aims: In this study we aimed to leverage VDM to investigate sex differences in AAA growth. Methods: We identified patients with infra-renal AAA and ≥2 CTA from 2010-2021. Patients with non-contrast CT scans or poor image quality were excluded. Propensity score matching was used to identify a cohort of males and females matched on age, hypertension, and smoking history. AAA were segmented and VDM was conducted using a multi-step image registration of aortic CTA studies to generate a deformation field which is then used to quantify 3D AAA growth rate (GR). Patient characteristics, maximum diameter (Dmax), AAA volume (Vmax), and GR were obtained. Statistical shape modeling was utilized to compare average 3D growth patterns between males and females. Results: Seventeen male and females with AAA were matched. There was no differences between sexes in race, diabetes, symptomatic status, or repair. Males had a trend toward larger Dmax (4.9 ± 0.6 vs 4.5 ± 0.6 cm, p = 0.055) and had Vmax at baseline (118.7 ± 69.9 vs 76.6 ± 35.0 cm 3 , p = 0.034). There was no difference between sexes when looking at change in Dmax or Vmax. Male and female AAA had no difference in average GR (0.11 ± 0.08 vs 0.17 ± 0.22 cm/year, p = 0.293). Shape models demonstrated more diffuse AAA growth for males and more eccentric AAA growth for females (Figure 1). Conclusions: Despite comparable changes in Dmax, Vmax, and GR, women demonstrated a more eccentric distribution of AAA growth, a finding which may contribute to higher rupture risk in women.

Broad-UNet-diff: Diffeomorphic Deformable Medical Image Registration based on Multi-scale Feature Learning

Introduction: To propose a medical image registration method with significant performance improvement. The spatial transformation obtained by the traditional deformable image registration technology is not smooth enough, and the calculation amount is too large to solve the optimization parameters. The network model proposed based on deep learning medical image registration technology has some limitations, which cannot guarantee the registration of topological structures, resulting in the loss of spatial features. It makes the model have topological conservation and transform reversibility, has the ability to learn more multi-scale features and complex image structures, and captures finer changes while clearly encoding global correlation. Method: Based on the core UNet model, a deformable image registration method with a new architecture Broad-UNet-diff is proposed. The model is equipped with asymmetric parallel convolution and uses diffeomorphism mapping. Result: Compared with the seven classical registration methods under the brain MRI datasets, the proposed method has significantly improved the registration performance. In particular, compared with the advanced TransMorph-diff registration method, the Dice score can be improved by 12 %, but only the 1/10 parameters are needed. Conclusion: This method confirms its convincing effectiveness and accuracy.

Stereo-DICNet: An efficient and unified speckle matching network for stereo digital image correlation measurement

Digital image correlation (DIC) is an image-based, non-contact optical measurement technique for measuring the deformation of objects. Recently, deep learning has been preliminarily used in monocular-DIC measurement and has brought significant advantages in efficiency and robustness. However, the deep learning-based stereo-DIC methods is still underexplored, although stereo-DIC can measure three-dimensional deformation and shape compared to monocular-DIC. Different from monocular-DIC, stereo-DIC not only requires temporal matching (inter-frame) but also stereo matching (inter-camera), and the difference in image deformation scale between the two matching tasks make it difficult to perform them using a unified network. To solve this problem, we propose a unified speckle matching network that can be used for deformation measurement at different scales, referred to as Stereo-DICNet. Stereo-DICNet consists of three modules: feature extraction, cost volume generation, and displacement map prediction. Firstly, the feature extraction module composed of the residual layer and the spatial pyramid pooling layer is designed to extract the features of different receptive fields. Then, an attention connect volume was proposed to represent the similarity information between pixels at different spatial scales. Finally, the displacement map is predicted by a stacked hourglass-shaped sub-net. Simulation and physical experiments verify that Stereo-DICNet can perform both temporal matching and stereo matching. Moreover, material tensile experiments show that the MAE of Stereo-DICNet in the U, V, and W directions are no more than 0.005 mm, 0.015 mm, 0.01 mm respectively.

An Omnidirectional Image Super-Resolution Method Based on Enhanced SwinIR

For the significant distortion problem caused by the special projection method of equi-rectangular projection (ERP) images, this paper proposes an omnidirectional image super-resolution algorithm model based on position information transformation, taking SwinIR as the base. By introducing a space position transformation module that supports deformable convolution, the image preprocessing process is optimized to reduce the distortion effects in the polar regions of the ERP image. Meanwhile, by introducing deformable convolution in the deep feature extraction process, the model’s adaptability to local deformations of images is enhanced. Experimental results on publicly available datasets have shown that our method outperforms SwinIR, with an average improvement of over 0.2 dB in WS-PSNR and over 0.030 in WS-SSIM for ×4 pixel upscaling.

Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning.

The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel automatic method to predict registration uncertainty based on multi-category features and supervised learning. Three types of features, including deformation field statistical features, deformation field physiologically realistic features, and image similarity features, are introduced and calculated to train the random forest regressor for local registration uncertain prediction. Deformation field statistical features represent the numerical stability of registration optimization, which are correlated to the uncertainty of deformation fields; deformation field physiologically realistic features represent the biomechanical properties of organ motions, which mathematically reflect the physiological reality of deformation; image similarity features reflect the similarity between the warped image and fixed image. The multi-category features comprehensively reflect the registration uncertainty. The strategy of spatial adaptive random perturbations is also introduced to accurately simulate spatial distribution of registration uncertainty, which makes deformation field statistical features more discriminative to the uncertainty of deformation fields. Experiments were conducted on three publicly available thoracic CT image datasets. Seventeen randomly selected image pairs are used to train the random forest model, and 9 image pairs are used to evaluate the prediction model. The quantitative experiments on lung CT images show that the proposed method outperforms the baseline method for uncertain prediction of classical iterative optimization-based registration and deep learning-based registration with different registration qualities. The proposed method achieves good performance for registration uncertain prediction, which has great potential in improving the accuracy of registration uncertain prediction.

A novel hybrid method for the registration of deformable medical images using common subregions

A novel hybrid method for the registration of deformable medical images using common subregions

New full-counts phase-matched data-driven gated (DDG) PET/CT.

Data-driven gated (DDG) PET has gained clinical acceptance and has been shown to match or outperform external-device gated (EDG) PET. However, in most clinical applications, DDG PET is matched with helical CT acquired in free breathing (FB) at a random respiratory phase, leaving registration, and optimal attenuation correction (AC) to chance. Furthermore, DDG PET requires additional scan time to reduce image noise as it only preserves 35%-50% of the PET data at or near the end-expiratory phase of the breathing cycle. A new full-counts, phase-matched (FCPM) DDG PET/CT was developed based on a low-dose cine CT to improve registration between DDG PET and DDG CT, to reduce image noise, and to avoid increasing acquisition times in DDG PET. A new DDG CT was developed for three respiratory phases of CT images from a low dose cine CT acquisition of 1.35mSv for a coverage of about 15.4cm: end-inspiration (EI), average (AVG), and end-expiration (EE) to match with the three corresponding phases of DDG PET data: -10% to 15%; 15% to 30%, and 80% to 90%; and 30% to 80%, respectively. The EI and EE phases of DDG CT were selected based on the physiological changes in lung density and body outlines reflected in the dynamic cine CT images. The AVG phase was derived from averaging of all phases of the cine CT images. The cine CT was acquired over the lower lungs and/or upper abdomen for correction of misregistration between PET and FB CT as well as DDG PET and FB CT. The three phases of DDG CT were used for AC of the corresponding phases of PET. After phase-matched AC of each PET dataset, the EI and AVG PET data were registered to the EE PET data with deformable image registration. The final result was FCPM DDG PET/CT which accounts for all PET data registered at the EE phase. We applied this approach to 14 18F-FDG lung cancer patient studies acquired at 2min/bed position on the GE Discovery MI (25-cm axial FOV) and evaluated its efficacy in improved quantification and noise reduction. Relative to static PET/CT, the SUVmax increases for the EI, AVG, EE, and FCPM DDG PET/CT were 1.67±0.40, 1.50±0.28, 1.64±0.36, and 1.49±0.28, respectively. There were 10.8% and 9.1% average decreases in SUVmax from EI and EE to FCPM DDG PET/CT, respectively. EI, AVG, and EE DDG PET/CT all maintained increased image noise relative to static PET/CT. However, the noise levels of FCPM and static PET were statistically equivalent, suggesting the inclusion of all counts was able to decrease the image noise relative to EI and EE DDG PET/CT. A new FCPM DDG PET/CT has been developed to account for 100% of collected PET data in DDG PET applications. Image noise in FCPM is comparable to static PET, while small decreases in SUVmax were also observed in FCPM when compared to either EI or EE DDG PET/CT.

Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.

We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62±0.27, 2.39±1.30, 3.07±2.42, 1.04±0.72mm, and average DSCs are 0.94±0.03, 0.80±0.12, 0.77±0.15, 0.90±0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.