Elastic Image Registration Research Articles

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.

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.

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

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.

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.

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.

Towards free breathing 3D ASL imaging of the human liver using prospective motion correction.

Measurements of liver perfusion yield valuable information about certain diseases like carcinomas and cirrhosis. To assess perfusion, noninvasive arterial spin labeling (ASL) MRI has the potential to become an important alternative to contrast agent-based methods. Unfortunately, ASL perfusion-weighted images are highly susceptible to breathing motion. This work presents a prospective motion-compensation technique, tackling breathing motion during ASL experiments of the human liver. Feasibility of 3D pseudo-continuous ASL imaging under free breathing is investigated by using a prospective motion-compensation technique with fat signal in additional 2D-EPI navigators. The proposed technique is compatible with strong signal suppression, occurring in background-suppressed pseudo-continuous ASL imaging. An additional retrospective 2D elastic registration algorithm is proposed to correct for residual elastic deformations. The technique is validated in 8 healthy volunteers. The prospective technique allowed a significant reduction of the cranial-caudal liver shifts during free breathing pseudo-continuous ASL experiments. Additional 2D elastic image registration allowed reduction of cranial-caudal liver motion to levels of timed breathing protocols. Artifacts in perfusion-weighted images could be reduced when compared with free-breathing ASL images without motion correction. The proposed technique is suitable for prospective compensation of liver motion in background-suppressed 3D pseudo-continuous ASL imaging. Future work focuses on further improving the quality of perfusion-weighted images.

Impact of Surgeon's Experience in Rigid versus Elastic MRI/TRUS-Fusion Biopsy to Detect Significant Prostate Cancer Using Targeted and Systematic Cores.

Simple SummaryFor the transfer of suspicious lesions in magnetic resonance imaging (MRI) to ultrasound in prostate fusion biopsy, biopsy platforms can be distinguished by rigid or elastic image registration. This study evaluates the detection rate of these different platforms for transperineal fusion-guided prostate biopsy to detect clinically significant prostate cancer under consideration of the surgeon’s learning curve. In our cohort, rigid and elastic registration systems showed a similar prostate cancer detection rate in experienced surgeons, whereas novices seem to benefit from rigid fusion. In the total cohort, targeted fusion biopsy with a rigid registration system outperformed elastic registration target biopsy with a superior significant prostate cancer detection rate, each compared to systematic saturation biopsy. Thus, rigid target biopsy aided in reducing targeting errors that result in missing MRI-visualized significant prostate cancer. These results can provide valuable decision support in selecting a biopsy fusion platform to increase the detection rate and risk stratification of prostate cancer, especially at the beginning of the surgeon’s learning curve.Multiparametric magnetic resonance imaging (mpMRI) and MRI/ultrasound fusion-targeted prostate biopsy (FB) have excellent sensitivity in detecting significant prostate cancer (sPC). FB platforms can be distinguished by rigid (RTB) or elastic image registration (ETB). We compared RTB and ETB by analyzing sPC detection rates of both RTB and ETB at different stages of the surgeons’ learning curve. Patients undergoing RTB between 2015–2017 (n = 502) were compared to patients undergoing ETB from 2017–2019 (n = 437). SPC detection rates were compared by Chi-square-test on patient-basis. Combination of transperineal systematic biopsy and each TB served as reference and sub-analyses were performed for different grades of surgeon’s experience. In the RTB subgroup, 233 men (46%) had sPC, compared to 201 (46%) in the ETB subgroup. RTB alone detected 94% of men with sPC and ETB 87% (p = 0.02). However, for at least intermediate-experienced surgeons (>100 FB), no differences occurred between RTB and ETB. In the total cohort, at least intermediate-experienced surgeons detected significantly more sPC (10%, p = 0.008) than novices. Thus, targeted transperineal MRI/TRUS-FB with a RTB registration system showed a similar sPC detection rate to ETB in experienced surgeons but a superior sPC detection rate to ETB in the total cohort. Low-experienced surgeons seem to benefit from RTB.

Fully automated quantification of LV regional wall motion from echocardiograms to detect myocardial infarction

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Ultromics Ltd Background Myocardial wall motion analysis from echocardiography allows precise assessment of cardiac contractile function. Strain, which assesses myocardial deformation, has been shown to enable earlier detection of myocardial disease [1]. Current analysis software packages [2] use semi-automated methods to compute strain, which frequently require manual endocardial delineation and iterative contour adjustment based on tracking results, respectively, causing significant variability. Purpose We present a fully automated pipeline for tracking left ventricular (LV) wall motion to quantify global and segmental longitudinal strain from 2D echocardiograms, and go on to validate the pipeline with an openly available myocardial infarction (MI) dataset. Methods We applied our existing deep learning-based automated contouring method [3] to delineate the endocardial border in the A4C, A2C and A3C views and combined this with spline-based elastic image registration to track LV motion through time. We sampled points from a region of interest initiated from the endocardial border at the end-diastolic (ED) frame, and tracked subsequent motion by recomputing updated positions of all sample points based on each frame‘s displacement field, enabling us to both track the myocardium throughout the cardiac cycle and calculate longitudinal strain relative to the ED frame. The automated endocardial contour was used to regularise the process. The pipeline was independently tested on the HMC-QU dataset [4] which was downloaded from Kaggle and consists of a single cardiac cycle from the A4C view from 160 patients who were diagnosed with an acute MI and underwent echocardiography either prior to percutaneous coronary intervention or within 24 hours of undergoing the procedure; the dataset includes the labels of ED and end-systolic (ES) frames, as well as the presence of an MI in 6 segments excluding the apical cap (Fig 1a), as determined by the consensus of cardiologists from HMC Hospital in Qatar. The Wilcoxon signed-rank test was used to compare peak strain between the MI and non-MI segments; ROC curves were computed to compare the performance of the automatically derived peak longitudinal strain against the MI labels. Results Fig 1b shows ROC curves of peak segmental longitudinal strain for detecting MI, with the best performance in the mid-anterolateral segment (AUC 0.84), and a lower performance for basal segments than mid and apical segments, consistent with known variation in clinical practice [5]. Fig 2 shows that peak longitudinal strain computed from our pipeline was statistically significantly more positive in segments with an MI. Conclusions We present a fully automated pipeline for calculating segmental strain across a cardiac cycle to identify infarcted segments without any observer variability. Clinical application of this method has the potential to identify and monitor regional myocardial function and benefit patient management. Abstract Figure. Fig1. ROC of peak longitudinal strains Abstract Figure. Fig2.Boxplot of peak longitudinal strain

Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography-Performance Study In Silico.

Although the two dimensional Speckle Tracking Echocardiography has gained a strong position among medical diagnostic techniques in cardiology, it still requires further developments to improve its repeatability and reliability. Few works have attempted to incorporate the left ventricle segmentation results in the process of displacements and strain estimation to improve its performance. We proposed the use of mask information as an additional penalty in the elastic image registration based displacements estimation. This approach was studied using a short axis view synthetic echocardiographic data, segmented using an active contour method. The obtained masks were distorted to a different degree, using different methods to assess the influence of the segmentation quality on the displacements and strain estimation process. The results of displacements and circumferential strain estimations show, that even though the method is dependent on the mask quality, the potential loss in accuracy due to the poor segmentation quality is much lower than the potential accuracy gain in cases where the segmentation performs well.

A theoretical analysis of the potential effect of negative pressure in wood drying based on a CT-scanner study

A CT-scanner was used to scan 25 mm thick radiata pine boards during drying at 90 °C dry-bulb and 60 °C wet-bulb temperatures. Twenty transverse sections spaced by 10 mm along the length of the boards were CT-scanned six times during drying, and an elastic image registration technique was used to transform the CT-scanned images into moisture content profiles. The resulting images suggested that moisture content distributions did not always resemble diffusion-like moisture transfer mechanism, so common assumptions based on moisture content gradients as the main driving force for wood drying may not always be enough to explain the development of wet and dry zones coexisting below and above fiber saturation point. This paper discusses these findings based on both experimental CT-scanner data and the theory of water potential, which it is argued provides a more complete description of driving forces in wood drying.

Computational Analysis of Alzheimer Amyloid Plaque Composition in 2D- and Elastically Reconstructed 3D-MALDI MS Images.

MALDI mass spectrometry imaging (MSI) enables label-free, spatially resolved analysis of a wide range of analytes in tissue sections. Quantitative analysis of MSI datasets is typically performed on single pixels or manually assigned regions of interest (ROIs). However, many sparse, small objects such as Alzheimer's disease (AD) brain deposits of amyloid peptides called plaques are neither single pixels nor ROIs. Here, we propose a new approach to facilitate the comparative computational evaluation of amyloid plaque-like objects by MSI: a fast PLAQUE PICKER tool that enables a statistical evaluation of heterogeneous amyloid peptide composition. Comparing two AD mouse models, APP NL-G-F and APP PS1, we identified distinct heterogeneous plaque populations in the NL-G-F model but only one class of plaques in the PS1 model. We propose quantitative metrics for the comparison of technical and biological MSI replicates. Furthermore, we reconstructed a high-accuracy 3D-model of amyloid plaques in a fully automated fashion, employing rigid and elastic MSI image registration using structured and plaque-unrelated reference ion images. Statistical single-plaque analysis in reconstructed 3D-MSI objects revealed the Aβ1-42Arc peptide to be located either in the core of larger plaques or in small plaques without colocalization of other Aβ isoforms. In 3D, a substantially larger number of small plaques were observed than that indicated by the 2D-MSI data, suggesting that quantitative analysis of molecularly diverse sparsely-distributed features may benefit from 3D-reconstruction. Data are available via ProteomeXchange with identifier PXD020824.

Feature-tracking-based strain analysis - a comparison of tracking algorithms.

PurposeOptical flow feature-tracking (FT) strain assessment is increasingly being employed scientifically and clinically. Several software packages, employing different algorithms, enable computation of FT-derived strains. The aim of this study is to investigate the impact of the underlying algorithm on the validity and robustness of FT-derived strain results.Material and methodsCSPAMM and SSFP cine sequences were acquired in 30 subjects (15 patients with aortic stenosis and associated secondary hypertrophic cardiomyopathy, and 15 controls) in identical midventricular short-axis locations. Global peak systolic circumferential strain (PSCS) was calculated using tagging and feature-tracking software with different algorithms (non-rigid, elastic image registration, and blood myocardial border tracing). Intermodality agreement and intra- as well inter-observer variability were assessed.ResultsIntermodality/inter-algorithm comparison for global PSCS using Friedman’s test revealed statistically significant differences (tagging vs. blood myocardial border tracing algorithm). Intermodality assessment revealed the highest correlation between tagging and non-rigid, elastic image registration (r = 0.84), while correlation between tagging and blood myocardial border tracing (r = 0.36) and between the two feature-tracking software packages (r = 0.5) were considerably lower.ConclusionsThe type of algorithm employed during feature-tracking strain assessment has a significant impact on the results. The non-rigid, elastic image registration algorithm produces more precise and reproducible results than the blood myocardium tracing algorithm.

A fast approach of nonparametric elastic image registration problem

In this paper, we present a fast algorithm of the nonparametric elastic image registration using a simple implementation of the Range Restricted GMRES (RRGMRES) method. This approach differs from the others in the fact that it is specified to the tridiagonal block matrix type to resolve a nonsymmetric linear system. In what follows, we prove existence and uniqueness of minimizer of the elastic registration problem and present the corresponding discrete problem by employing a finite difference scheme. The accuracy of the proposed method is demonstrated on different image registration examples; we also show the speedup of the proposed approach by calculating the corresponding CPU time and compared it with the classical elastic registration method.

A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization

Increasing the size of an image is an extensively studied problem in image processing. In recent years, many studies have been conducted on image super-resolution (SR). Since the super-resolution techniques depend on the motion precision estimation, we investigate the use of a nonlinear elastic (called hyperelastic) image registration. Also, we propose a spatially weighted second order SR algorithm, which takes into account the distribution of the spatial information in different image areas. The hyperelastic image registration is used to handle the subpixel errors between the unregistered images, while the spatially weighted second order regularization allows to increase the robustness of the restoration step with respect to degradation factors (blur and noise). As a result, the registration model is more efficient and easier to implement and the proposed SR algorithm reduces artifacts in flat regions of the image and also preserves well sharp edges. The efficiency of the proposed model is demonstrated using simulated and real tests, while comparison with other competitive SR methods is achieved.

Motion-Corrected Real-Time Cine Magnetic Resonance Imaging of the Heart: Initial Clinical Experience.

Free-breathing real-time (RT) imaging can be used in patients with difficulty in breath-holding; however, RT cine imaging typically experiences poor image quality compared with segmented cine imaging because of low resolution. Here, we validate a novel unsupervised motion-corrected (MOCO) reconstruction technique for free-breathing RT cardiac images, called MOCO-RT. Motion-corrected RT uses elastic image registration to generate a single heartbeat of high-quality data from a free-breathing RT acquisition. Segmented balanced steady-state free precession (bSSFP) cine images and free-breathing RT images (Cartesian, TGRAPPA factor 4) were acquired with the same spatial/temporal resolution in 40 patients using clinical 1.5 T magnetic resonance scanners. The respiratory cycle was estimated using the reconstructed RT images, and nonrigid unsupervised motion correction was applied to eliminate breathing motion. Conventional segmented RT and MOCO-RT single-heartbeat cine images were analyzed to evaluate left ventricular (LV) function and volume measurements. Two radiologists scored images for overall image quality, artifact, noise, and wall motion abnormalities. Intraclass correlation coefficient was used to assess the reliability of MOCO-RT measurement. Intraclass correlation coefficient showed excellent reliability (intraclass correlation coefficient ≥ 0.95) of MOCO-RT with segmented cine in measuring LV function, mass, and volume. Comparison of the qualitative ratings indicated comparable image quality for MOCO-RT (4.80 ± 0.35) with segmented cine (4.45 ± 0.88, P = 0.215) and significantly higher than conventional RT techniques (3.51 ± 0.41, P < 0.001). Artifact and noise ratings for MOCO-RT (1.11 ± 0.26 and 1.08 ± 0.19) and segmented cine (1.51 ± 0.90, P = 0.088 and 1.23 ± 0.45, P = 0.182) were not different. Wall motion abnormality ratings were comparable among different techniques (P = 0.96). The MOCO-RT technique can be used to process conventional free-breathing RT cine images and provides comparable quantitative assessment of LV function and volume measurements to conventional segmented cine imaging while providing improved image quality and less artifact and noise. The free-breathing MOCO-RT reconstruction method may have considerable clinical utility in cardiac magnetic resonance imaging for patients with difficulty breath-holding.

Cardiac magnetic resonance T1 and extracellular volume mapping with motion correction and co-registration based on fast elastic image registration

ObjectiveOur aim was to investigate the technical feasibility of a novel motion compensation method for cardiac magntic resonance (MR) T1 and extracellular volume fraction (ECV) mapping.Materials and methodsNative and post-contrast T1 maps were obtained using modified look-locker inversion recovery (MOLLI) pulse sequences with acquisition scheme defined in seconds. A nonrigid, nonparametric, fast elastic registration method was applied to generate motion-corrected T1 maps and subsequently ECV maps. Qualitative rating was performed based on T1 fitting-error maps and overlay images. Local deformation vector fields were produced for quantitative assessment. Intra- and inter-observer reproducibility were compared with and without motion compensation.ResultsEighty-two T1 and 39 ECV maps were obtained in 21 patients with diverse myocardial diseases. Approximately 60% demonstrated clear quality improvement after motion correction for T1 mapping, particularly for the poor-rating cases (23% before vs 2% after). Approximately 67% showed further improvement with co-registration in ECV mapping. Although T1 and ECV values were not clinically significantly different before and after motion compensation, there was improved intra- and inter-observer reproducibility after motion compensation.ConclusionsAutomated motion correction and co-registration improved the qualitative assessment and reproducibility of cardiac MR T1 and ECV measurements, allowing for more reliable ECV mapping.

Hybrid multiscale affine and elastic image registration approach towards wireless capsule endoscope localization

Abstract Wireless capsule endoscope (WCE) enables the visualization of the interior of the gastrointestinal (GI) tract. In particular it is very important for the examination of regions in the small bowel that cannot be reached by conventional endoscopy techniques. However, when an abnormality is found in WCE images of the small bowel, it is unknown how far is this abnormality from an anatomical reference point. The primary objective of the present paper is to give a contribution to WCE localization, using image-based methods. The main focus of this work is on the description of a hybrid multiscale affine and elastic image registration approach, its experimental application on WCE videos, and comparison with a multiscale affine registration. The proposed approach intends to track the WCE motion, by using the successive WCE frames that image the walls of the elastic small intestine. It includes registrations that capture both rigid-like and non-rigid deformations, due respectively to the rigid-like WCE movement and the elastic deformation of the small intestine originated by the GI peristaltic movement. Furthermore, the proposed approach enables the extraction of two parameters (scale and rotation) from which the relative displacement and orientation of the WCE inside the GI tract can be derived, via projective geometry. Under this approach an indicator of the WCE speed can be inferred, which can be clinically useful for video interpretation. The results of the experimental tests with real WCE video frames show the good performance of the proposed approach, when elastic deformations of the small intestine are involved in successive frames, and its superiority with respect to a multiscale affine image registration, which accounts for rigid-like deformations only and discards elastic deformations.

Voxel-based analysis unveils regional dose differences associated with radiation-induced morbidity in head and neck cancer patients

The risk of radiation-induced toxicity in patients treated for head and neck (HN) cancer with radiation therapy (RT) is traditionally estimated by condensing the 3D dose distribution into a monodimensional cumulative dose-volume histogram which disregards information on dose localization. We hypothesized that a voxel-based approach would identify correlations between radiation-induced morbidity and local dose release, thus providing a new insight into spatial signature of radiation sensitivity in composite regions like the HN district. This methodology was applied to a cohort of HN cancer patients treated with RT at risk of radiation-induced acute dysphagia (RIAD). We implemented an inter-patient elastic image registration framework that proved robust enough to match even the most elusive HN structures and to provide accurate dose warping. A voxel-based statistical analysis was then performed to test regional dosimetric differences between patients with and without RIAD. We identified a significantly higher dose delivered to RIAD patients in two voxel clusters in correspondence of the cricopharyngeus muscle and cervical esophagus. Our study goes beyond the well-established organ-based philosophy exploring the relationship between radiation-induced morbidity and local dose differences in the HN region. This approach is generally applicable to different HN toxicity endpoints and is not specific to RIAD.