Elastic Registration Research Articles

Daily Engine Performance Trending using Common Flight Regime Identification

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and Functional Principal Component Analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm-assisted orthogonal matching pursuit for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.

Automated analysis of flow cytometry data with minimal training files: Research evaluation of an elastic image registration algorithm for TBNK, stem cell enumeration, and lymphoid screening tube assays.

Automated analysis of flow cytometry data can improve objectivity and reduce analysis time but has generally required work by software and algorithm experts. Here, we investigated the performance of BD ElastiGate™ Software (hereafter ElastiGate), which allows users to automate gating by selecting gated training files, then uses elastic image registration to gate new files. Three assays of increasing complexity were examined: TBNK, stem cell enumeration (SCE), and lymphoid screening tube (LST). For TBNK analysis, 60 peripheral blood (PB) samples from normal, HIV+, and controls were tested with ground truth analysis by an existing automated method. For SCE, 128 samples including bone marrow (BM), cord blood (CB), and apheresis were tested with analysis by multiple manual analysts. For LST, 80 PB and 28 BM samples were tested with manual analysis. For ElastiGate, a minimal number of training files was selected. Results were compared by Bland-Altman or F1 score analysis. For TBNK, ElastiGate using three training files (1 control, 1 normal, 1 HIV+) showed mean %bias across all reported populations between -1.48% and 7.13% (average 2.08%). For SCE, ElastiGate using three BM and two CB training files showed median F1 scores >0.93 in comparison to >0.94 and >0.92 for two other manual analysts. For LST, ElastiGate using four training files for each of PB and BM showed median F1 scores >0.945 for 13 of 14 PB populations and 10 of 14 BM populations, with generally similar or better performance for normal samples compared to abnormal; populations with lower scores were often associated with lower agreement between manual analysts. Based on analysis of three assays with four sample types of increasing complexity, ElastiGate with minimal training files may perform as an automated gating assistant. The results reported here are for research use only, not for use in diagnostic or therapeutic procedures.

From Phantoms to Patients: Improved Fusion and Voxel-Wise Analysis of Diffusion-Weighted Imaging and FDG-Positron Emission Tomography in Positron Emission Tomography/Magnetic Resonance Imaging for Combined Metabolic-Diffusivity Index (cDMI).

Hybrid positron emission tomography/magnetic resonance imaging (PET/MR) opens new possibilities in multimodal multiparametric (m2p) image analyses. But even the simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) does not guarantee perfect voxel-by-voxel co-registration due to organs and distortions, especially in diffusion-weighted imaging (DWI), which would be, however, crucial to derive biologically meaningful information. Thus, our aim was to optimize fusion and voxel-wise analyses of DWI and standardized uptake values (SUVs) using a novel software for m2p analyses. Using research software, we evaluated the precision of image co-registration and voxel-wise analyses including the rigid and elastic 3D registration of DWI and [18F]-Fluorodeoxyglucose (FDG)-PET from an integrated PET/MR system. We analyzed DWI distortions with a volume-preserving constraint in three different 3D-printed phantom models. A total of 12 PET/MR-DWI clinical datasets (bronchial carcinoma patients) were referenced to the T1 weighted-DIXON sequence. Back mapping of scatterplots and voxel-wise registration was performed and compared to the non-optimized datasets. Fusion was rated using a 5-point Likert scale. Using the 3D-elastic co-registration algorithm, geometric shapes were restored in phantom measurements; the measured ADC values did not change significantly (F = 1.12, p = 0.34). Reader assessment showed a significant improvement in fusion precision for DWI and morphological landmarks in the 3D-registered datasets (4.3 ± 0.2 vs. 4.6 ± 0.2, p = 0.009). Most pronounced differences were noted for the chest wall (p = 0.006), tumor (p = 0.007), and skin contour (p = 0.014). Co-registration increased the number of plausible ADC and SUV combinations by 25%. The volume-preserving elastic 3D registration of DWI significantly improved the precision of fusion with anatomical sequences in phantom and clinical datasets. The research software allowed for a voxel-wise analysis and visualization of [18F]FDG-PET/MR data as a "combined diffusivity-metabolic index" (cDMI). The clinical value of the optimized PET/MR biomarker can thus be tested in future PET/MR studies.

Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass

ABSTRACT Objective It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass. Methods An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared. Results The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p < .05). Observation group demonstrated greater alveolar ridge elevation and bone augmentation versus control group, implying superior healing outcomes. Conclusion The feature point extraction algorithm is applicable for CBCT image processing in patients with insufficient maxillary bone mass. Autologous CGF exhibited superior therapeutic efficacy.

Assessment of lung deformation in patients with idiopathic pulmonary fibrosis with elastic registration technique on pulmonary three-dimensional ultrashort echo time MRI.

To assess lung deformation in patients with idiopathic pulmonary fibrosis (IPF) using with elastic registration algorithm applied to three-dimensional ultrashort echo time (3D-UTE) MRI and analyze relationship of lung deformation with the severity of IPF. Seventy-six patients with IPF (mean age: 62±6 years) and 62 age- and gender-matched healthy controls (mean age: 58±4 years) were prospectively enrolled. End-inspiration and end-expiration images acquired with a single breath-hold 3D-UTE sequence were registered using elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Jac-mean (absolute value of the log means of Jacobian determinants) and the Dice similarity coefficient (Dice) were compared between different groups. Compared with healthy controls, the Jac-mean of IPF patients significantly decreased (0.21±0.08 vs. 0.27±0. 07, p < 0.001). Furthermore, the Jac-mean and Dice correlated with the metrics of pulmonary function tests and the composite physiological index. The lung deformation in IPF patients with dyspnea Medical Research Council (MRC) ≥ 3 (Jac-mean: 0.16±0.03; Dice: 0.06±0.02) was significantly lower than MRC1 (Jac-mean: 0. 25±0.03, p < 0.001; Dice: 0.10±0.01, p < 0.001) and MRC 2 (Jac-mean: 0.22±0.11, p = 0.001; Dice: 0.08±0.03, p = 0.006). Meanwhile, Jac-mean and Dice correlated with health-related quality of life, 6 min-walk distance, and the extent of pulmonary fibrosis. Jac-mean correlated with pulmonary vascular-related indexes on high-resolution CT. The decreased lung deformation in IPF patients correlated with the clinical severity of IPF patients. Elastic registration of inspiratory-to-expiratory 3D UTE MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. This prospective study demonstrated that lung deformation decreased in idiopathic pulmonary fibrosis (IPF) patients and correlated with the severity of IPF. Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. • Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI could evaluate lung deformation. • Lung deformation significantly decreased in idiopathic pulmonary fibrosis (IPF) patients, compared with the healthy controls. • Reduced lung deformation of IPF patients correlated with worsened pulmonary function and the composite physiological index (CPI).

Deep Learning Style Transfer for Elastic Image Registration of Visually Distinct Correlative Microscopy Images

Deep Learning Style Transfer for Elastic Image Registration of Visually Distinct Correlative Microscopy Images

In vivo corneal confocal microscopy: Technical innovations and frontier applications

At present, the only commercially available system for in vivo Corneal Confocal Microscopy enables unique insights into the corneal morphology, especially corneal nerves. Unfortunately—technology, user‐friendliness and image analysis of the standard platform has remained at the level of 15 years ago. This contribution presents cutting edge insights into new developments based on research within our German Research Foundation supported interdisciplinary research network. This covers a wide range of new developments addressing usability, large field as well as 3D imaging, dedicated contact cap designs, elastic image registration and AI based tissue classification. Furthermore, cutting edge technologies offer 4D imaging to perform dynamic investigations and multiwavelength as well as OCT‐guided Corneal Confocal Microscopy. Finally, key aspects demonstrating the potential of the technique for static and dynamic corneal nerves analysis will be presented.

Moisture gradients in sawn timber during aggressive kiln drying investigated with X-ray computed tomography

ABSTRACT Reducing the time for drying sawn timber to a certain moisture level without deteriorating its quality is increasingly important for an economic and energy-efficient industrial timber-drying process and to support the transition to a sustainable society. It is, however, crucial to ensure that the quality of the timber, i.e. the degree of distortion, cracking, discolouration, and moisture variation within and between the pieces in a drying batch, is not compromised. Drying-simulation software tend to be too conservative in drying-rate recommendations, which has been observed in practise particularly for large cross-section timber of Norway spruce. This study investigated the drying rate and checking occurrence of centre-yielded Norway spruce planks when dried with more aggressive schedules than normally used in practise, i.e. using higher dry-bulb temperatures and/or lower relative humidities than recommended in the conventional optimisation programmes used in the sawmill industry in Sweden. The objective was to investigate the possibility to considerably reduce the total drying time without compromising the quality of the dried timber. The quality of the planks was indirectly assessed by estimating their moisture-content distributions, calculating the moisture gradients and monitoring checking. This was achieved with 4D (3D + time) X-ray computed tomography and a recently developed image processing algorithm based on elastic image registration. The key findings in this study suggest that Norway spruce timber can be dried with significantly higher temperatures and lower relative humidities than suggested by simulations, leading to reduced total drying time without inducing checking. This methodology can help to improve the design of drying schedules to reduce drying time and energy consumption while maintaining timber quality at a level accepted by the customers.

Вторичный магнитоупругий резонанс в структуре: тонкая магнитная пленка – толстая упругая подложка

The task about excitation of connected magnetic and elastic vibrations in flat-parallel structure consisted of normal magnetized thin magnetic film applied on thick nonmagnetic substrate is considered. As a result of estimated task solution the amplitude-frequency characteristics of magnetic and elastic vibrations are found. It is shown that in conditions when the ferromagnetic resonance frequency is higher then own elastic vibration of the structure on the amplitude-frequency characteristics of magnetic vibrations applied the equidistance net of elastic resonances of structure. For the interpretation of observed resonance net it is proposed the model of phase synchronization between initial excitation and elastic wave which experience two passages along the thickness of structure. It is shown that the frequency of maximum elastic vibrations rounding exceeds frequency of maximum magnetization characteristic. The existence of own rounding elastic vibrations characteristic is named the second elastic system resonance. It is investigated the dependence of maximum second resonance characteristic from value of magnetoelastic interaction constant. It is shown that by increasing of this constant the frequency distance between both characteristic maxima increases. As a hypothesis of this increasing it is proposed the analogy with splitting of amplitude-frequency characteristic of system consisted of two connected resonators. It is investigated the amplitude-frequency characteristics magnetic and elastic vibrations in wide frequency region. On the equidistance elastic resonances net amplitude-frequency characteristic it is found the narrow equidistance disposed resonance depressions which are correspond to decreasing of amplitude more then two orders of value. It is shown that the frequency interval between neighbouring depressions is back proportional to distance between point of elastic vibrations registration and surface of substrate opposite to magnetic film. For the interpretation of depressions formation it is proposed the model which takes into account the phase opposition in registration point between wave which goes out of the excitation point and wave which is reflected from the opposite end of structure. Briefly noted some physical analogies of investigated phenomena.

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer.

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy. Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint. Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC. The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Elastic Registration Algorithm Based on Three-dimensional Pulmonary MRI in Quantitative Assessment of Severity of Idiopathic Pulmonary Fibrosis.

To quantitatively analyze lung elasticity in idiopathic pulmonary fibrosis (IPF) using elastic registration based on 3-dimensional pulmonary magnetic resonance imaging (3D-PMRI) and to assess its' correlations with the severity of IPF patients. Thirty male patients with IPF (mean age: 62±6y) and 30 age-matched male healthy controls (mean age: 62±6y) were prospectively enrolled. 3D-PMRI was acquired with a 3-dimensional ultrashort echo time sequence in end-inspiration and end-expiration. MR images were registered from end-inspiration to end-expiration with the elastic registration algorithm. Jacobian determinants were calculated from deformation fields on color maps. The log means of the Jacobian determinants (Jac-mean) and Dice similarity coefficient were used to describe lung elasticity between 2 groups. Then, the correlation of lung elasticity with dyspnea Medical Research Council (MRC) score, exercise tolerance, health-related quality of life, lung function, and the extent of pulmonary fibrosis on chest computed tomography were analyzed. The Jac-mean of IPF patients (-0.19, [IQR: -0.22, -0.15]) decreased (absolute value), compared with healthy controls (-0.28, [IQR: -0.31, -0.24], P<0.001). The lung elasticity in IPF patients with dyspnea MRC≥3 (Jac-mean: -0.15; Dice: 0.06) was significantly lower than MRC 1 (Jac-mean: -0.22, P=0.001; Dice: 0.10, P=0.001) and MRC 2 (Jac-mean: -0.21, P=0.007; Dice: 0.09, P<0.001). In addition, the Jac-mean negatively correlated with forced vital capacity % (r=-0.487, P<0.001), forced expiratory volume 1% (r=-0.413, P=0.004), TLC% (r=-0.488, P<0.001), diffusing capacity of the lungs for carbon monoxide % predicted (r=-0.555, P<0.001), 6-minute walk distance (r=-0.441, P=0.030) and positively correlated with respiratory symptoms (r=0.430, P=0.042). Meanwhile, the Dice similarity coefficient positively correlated with forced vital capacity % (r=0.577, P=0.004), forced expiratory volume 1% (r=0.526, P=0.012), diffusing capacity of the lungs for carbon monoxide % predicted (r=0.435, P=0.048), 6-minute walk distance (r=0.473, P=0.016), final peripheral oxygen saturation (r=0.534, P=0.004), the extent of fibrosis on chest computed tomography (r=-0.421, P=0.021) and negatively correlated with activity (r=-0.431, P=0.048). Lung elasticity decreased in IPF patients and correlated with dyspnea, exercise tolerance, health-related quality of life, lung function, and the extent of pulmonary fibrosis. The lung elasticity based on elastic registration of 3D-PMRI may be a new nonradiation imaging biomarker for quantitative evaluation of the severity of IPF.

Real-time correction of chromatic aberration in optical fluorescence microscopy

Multi-color fluorescence imaging is a powerful tool for studying the spatial relationships and interactions among sub-cellular structures in biological specimens. However, if improperly corrected, geometrical distortions caused by mechanical drift, refractive index mismatch, or chromatic aberration can lead to lower image resolution. In this paper, we present an extension of the image processing framework of Scipion by integrating a protocol called OFM Corrector, which corrects geometrical distortions in real-time using a B-spline-based elastic continuous registration technique. Our proposal provides a simple strategy to overcome chromatic aberration by digitally re-aligning color channels in multi-color fluorescence microscopy images, even in 3D or time. Our method relies on a geometrical calibration, which we do with fluorescent beads excited by different wavelengths of light and subsequently registered to get the elastic warp as a reference to correct chromatic shift. Our software is freely available with a user-friendly GUI and can be broadly used for various biological imaging problems. The paper presents a valuable tool for researchers working in light microscopy facilities.

Evaluation of Different Registration Algorithms to Reduce Motion Artifacts in CT-Thermography (CTT).

Computed tomography (CT)-based Thermography (CTT) is currently being investigated as a non-invasive temperature monitoring method during ablation procedures. Since multiple CT scans with defined time intervals were acquired during this procedure, interscan motion artifacts can occur between the images, so registration is required. The aim of this study was to investigate different registration algorithms and their combinations for minimizing inter-scan motion artifacts during thermal ablation. Four CTT datasets were acquired using microwave ablation (MWA) of normal liver tissue performed in an in vivo porcine model. During each ablation, spectral CT volume scans were sequentially acquired. Based on initial reconstructions, rigid or elastic registration, or a combination of these, were carried out and rated by 15 radiologists. Friedman's test was used to compare rating results in reader assessments and revealed significant differences for the ablation probe movement rating only (p = 0.006; range, 5.3-6.6 points). Regarding this parameter, readers assessed rigid registration as inferior to other registrations. Quantitative analysis of ablation probe movement yielded a significantly decreased distance for combined registration as compared with unregistered data. In this study, registration was found to have the greatest influence on ablation probe movement, with connected registration being superior to only one registration process.

Multiresolution discrete optimization registration method of ultrasound and magnetic resonance images based on key points

The registration of preoperative magnetic resonance (MR) images and intraoperative ultrasound (US) images is very important in the planning of brain tumor surgery and during surgery. Considering that the two-modality images have different intensity range and resolution, and the US images are degraded by lots of speckle noises, a self-similarity context (SSC) descriptor based on local neighborhood information was adopted to define the similarity measure. The ultrasound images were considered as the reference, the corners were extracted as the key points using three-dimensional differential operators, and the dense displacement sampling discrete optimization algorithm was adopted for registration. The whole registration process was divided into two stages including the affine registration and the elastic registration. In the affine registration stage, the image was decomposed using multi-resolution scheme, and in the elastic registration stage, the displacement vectors of key points were regularized using the minimum convolution and mean field reasoning strategies. The registration experiment was performed on the preoperative MR images and intraoperative US images of 22 patients. The overall error after affine registration was (1.57 ± 0.30) mm, and the average computation time of each pair of images was only 1.36 s; while the overall error after elastic registration was further reduced to (1.40 ± 0.28) mm, and the average registration time was 1.53 s. The experimental results show that the proposed method has prominent registration accuracy and high computational efficiency.

Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data.

For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would reduce resulting artifacts in the reconstructed images. This work presents a deep learning technique for inter-modality, elastic registration of PET/CT images for improving PET attenuation correction (AC). The feasibility of the technique is demonstrated for two applications: general whole-body (WB) imaging and cardiac myocardial perfusion imaging (MPI), with a specific focus on respiratory and gross voluntary motion. A convolutional neural network (CNN) was developed and trained for the registration task, comprising two distinct modules: a feature extractor and a displacement vector field (DVF) regressor. It took as input a non-attenuation-corrected PET/CT image pair and returned the relative DVF between them-it was trained in a supervised fashion using simulated inter-image motion. The 3D motion fields produced by the network were used to resample the CT image volumes, elastically warping them to spatially match the corresponding PET distributions. Performance of the algorithm was evaluated in different independent sets of WB clinical subject data: for recovering deliberate misregistrations imposed in motion-free PET/CT pairs and for improving reconstruction artifacts in cases with actual subject motion. The efficacy of this technique is also demonstrated for improving PET AC in cardiac MPI applications. A single registration network was found to be capable of handling a variety of PET tracers. It demonstrated state-of-the-art performance in the PET/CT registration task and was able to significantly reduce the effects of simulated motion imposed in motion-free, clinical data. Registering the CT to the PET distribution was also found to reduce various types of AC artifacts in the reconstructed PET images of subjects with actual motion. In particular, liver uniformity was improved in the subjects with significant observable respiratory motion. For MPI, the proposed approach yielded advantages for correcting artifacts in myocardial activity quantification and potentially for reducing the rate of the associated diagnostic errors. This study demonstrated the feasibility of using deep learning for registering the anatomical image to improve AC in clinical PET/CT reconstruction. Most notably, this improved common respiratory artifacts occurring near the lung/liver border, misalignment artifacts due to gross voluntary motion, and quantification errors in cardiac PET imaging.

Lung shrinking assessment on HRCT with elastic registration technique for monitoring idiopathic pulmonary fibrosis

ObjectivesEvaluation and follow-up of idiopathic pulmonary fibrosis (IPF) mainly rely on high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs). The elastic registration technique can quantitatively assess lung shrinkage. We aimed to investigate the correlation between lung shrinkage and morphological and functional deterioration in IPF.MethodsPatients with IPF who underwent at least two HRCT scans and PFTs were retrospectively included. Elastic registration was performed on the baseline and follow-up HRCTs to obtain deformation maps of the whole lung. Jacobian determinants were calculated from the deformation fields and after logarithm transformation, log_jac values were represented on color maps to describe morphological deterioration, and to assess the correlation between log_jac values and PFTs.ResultsA total of 69 patients with IPF (male 66) were included. Jacobian maps demonstrated constriction of the lung parenchyma marked at the lung base in patients who were deteriorated on visual and PFT assessment. The log_jac values were significantly reduced in the deteriorated patients compared to the stable patients. Mean log_jac values showed positive correlation with baseline percentage of predicted vital capacity (VC%) (r = 0.394, p < 0.05) and percentage of predicted forced vital capacity (FVC%) (r = 0.395, p < 0.05). Additionally, the mean log_jac values were positively correlated with pulmonary vascular volume (r = 0.438, p < 0.01) and the number of pulmonary vascular branches (r = 0.326, p < 0.01).ConclusionsElastic registration between baseline and follow-up HRCT was helpful to quantitatively assess the morphological deterioration of lung shrinkage in IPF, and the quantitative indicator log_jac values were significantly correlated with PFTs.Key Points• The elastic registration on HRCT was helpful to quantitatively assess the deterioration of IPF.• Jacobian logarithm was significantly reduced in deteriorated patients and mean log_jac values were correlated with PFTs.• The mean log_jac values were related to the changes of pulmonary vascular volume and the number of vascular branches.

Quantifying AI Assisted Auto-Segmentations Performance for a Clinical Online Adaptive Radiotherapy System in Multiple Treatment Sites

<h3>Purpose/Objective(s)</h3> The growing translation of online adaptive radiotherapy (ART) into clinical applications has sparked the need for a comprehensive assessment of artificial intelligence (AI) and automated segmentation performance employed by these novel treatment systems. Several groups have reported their initial experiences and evaluations using retroactive simulated data. Herein, incorporating clinical treatment data, we quantitatively examined the capability of a novel CBCT based online ART system for anatomy delineation of various tumor sites using AI and automated deformable segmentation algorithms. <h3>Materials/Methods</h3> Twelve patients treated with online ART were selected for 3 different tumor sites including pelvis, thorax, and head and neck (HN). The novel CBCT based ART system generates auto-segmentations for targets, influencer organs at risk (OARs) and other OARs. Influencers are site specific critical OARs which guide target generations. Convolutional neural networks (CNN) were used for pelvis influencer segmentations. Influencers for thorax and HN sites, initial target contours, and other non-influencer OARs were segmented using elastic deformable image registration (DIR). The initial auto-segmentations were extracted for targets, influencers, and high impact OARs. Contour qualities were quantified using volumetric dice similarity coefficient (DSC) against physician modified treatment contours, and compared using one-way ANOVA with a post hoc Tukey test for multiple comparisons. <h3>Results</h3> A total of 43 ART fractional data was analyzed. Physicians spent the most effort in modifying auto segmentation of target structures, with mean DSC of 0.59, 0.88 and 0.87 for pelvis, thoracic and HN sites, respectively. For influencer OARs, CNN generated pelvis influencers (mean DSC = 0.92) was less accurate than DIR generated influencers for thoracic and HN (mean DSC = 0.98 and 0.99) sites, with p-value < 0.005 and 0.0005. A table showing auto segmentation quality for influencers is included. All other high impact OAR auto segmentations required minimal revision with mean DSC of 0.99 for pelvis, thorax and HN sites. <h3>Conclusion</h3> Overall, CBCT based ART demonstrated high quality of auto segmentation for most OARs. However, manual revisions are still needed for targets and OARs with dosimetric importance, especially for treatment sites with inferior soft tissue contrast in CBCT. This study provides important insight for clinical adoption of online ART treatment system, automated segmentation algorithm development and clinical decision-making.

Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics

This paper proposes a complete convolutional formulation for 2D multimodal pairwise image registration problems based on free-form deformations. We have reformulated in terms of discrete 1D convolutions the evaluation of spatial transformations, the regularization term, and their gradients for three different multimodal registration metrics, namely, normalized cross correlation, mutual information, and normalized mutual information. A sufficient condition on the metric gradient is provided for further extension to other metrics. The proposed approach has been tested, as a proof of concept, on contrast-enhanced first-pass perfusion cardiac magnetic resonance images. Execution times have been compared with the corresponding execution times of the classical tensor product formulation, both on CPU and GPU. The speed-up achieved by using convolutions instead of tensor products depends on the image size and the number of control points considered, the larger those magnitudes, the greater the execution time reduction. Furthermore, the speed-up will be more significant when gradient operations constitute the major bottleneck in the optimization process.

P01.01.A Lesion-Function Analysis from Multimodal Imaging and Normative Brain Atlases for Prediction of Cognitive Deficits in Glioma Patients

Abstract Background Cognitive deficits are common in glioma patients following multimodality therapy, but the relative impact of different types and locations of treatment-related brain damage and recurrent tumors on cognition is not well understood. Material and Methods In 121 WHO Grade III/IV glioma patients, structural MRI, O-(2-[18F]fluoroethyl)-L-tyrosine FET-PET, and neuropsychological testing were performed at a median interval of 14 months (range, 1-214 months) after therapy initiation. Resection cavities, T1-enhancing lesions, T2/FLAIR hyperintensities, and FET-PET positive tumor sites were semiautomatically segmented and elastically registered to a normative, resting state (RS) fMRI-based functional cortical network atlas and to the JHU atlas of white matter (WM) tracts, and their influence on cognitive test scores relative to a cohort of matched healthy subjects was assessed. Results T2/FLAIR hyperintensities presumably caused by radiation therapy covered more extensive brain areas than the other lesion types and significantly impaired cognitive performance in many domains when affecting left-hemispheric RS-nodes and WM-tracts as opposed to brain tissue damage caused by resection or recurrent tumors. Verbal episodic memory proved to be especially vulnerable to T2/FLAIR abnormalities affecting the nodes and tracts of the left temporal lobe. Conclusion In order to improve radiotherapy planning, publicly available brain atlases, in conjunction with elastic registration techniques, should be used, similar to neuronavigation in neurosurgery.

Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer

Radiotherapy treatments are carried out using computerized axial tomography. In radiation therapy planning, the radiation oncologist must do a manual segmentation of volumes of interest to delineate the organs that should be irradiated. This way of carrying out the process generates long execution times and introduces a subjective component. In this study, a contour-propagation algorithm is formulated to automate the segmentation, based on elastic registration or nonrigid demon registration. A heuristic algorithm to find the parameters that optimize the registration is also proposed. The parameters found along with the contour-propagation algorithm are able to estimate contours of scans with Dice similarity coefficients (DSC) greater than 0.92 and maintain stability with B-spline registration, which takes in the parameters found as input. The study allows for validating the results using the correlation coefficient (CC) to compare the similarity between the voxels’ gray-scale intensity of the estimated tomography and the original tomography, obtaining values greater than 0.96. These values were validated under medical criteria and applied to liver and breast CT scans, indicating good performance for radiation therapy planning.