Intrasubject multimodal groupwise registration with the conditional template entropy

Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1◦. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine

The use of cardiac CT acquisition mode for dynamic musculoskeletal imaging

Objectives To quantitatively evaluate the impact of a cardiac acquisition CT mode on motion artifacts in comparison to a conventional cine mode for dynamic musculoskeletal (MSK) imaging. Methods A rotating PMMA phantom with air-filled holes drilled at varying distances from the disk center corresponding to linear hole speeds of 0.75 cm/s, 2.0 cm/s, and 3.6 cm/s was designed. Dynamic scans were obtained in cardiac and cine modes while the phantom was rotating at 48°/s in the CT scanner. An automated workflow to compute the Jaccard distance (JD) was established to quantify degree of motion artifacts in the reconstructed phantom images. JD values between the cardiac and cine scan modes were compared using a paired sample t-test. In addition, three healthy volunteers were scanned with both modes during a cyclic flexion–extension motion of the knee and analysed using the proposed metric. Results For all hole sizes and speeds, the cardiac scan mode had significantly lower (p-value <0.001) JD values. (0.39 [0.32–0.46]) i.e less motion artifacts in comparison to the cine mode (0.72 [0.68–0.76]). For both modes, a progressive increase in JD was also observed as the linear speed of the holes increased from 0.75 cm/s to 3.6 cm/s. The dynamic images of the three healthy volunteers showed less artifacts when scanned in cardiac mode compared to cine mode, and this was quantitatively confirmed by the JD values. Conclusions A cardiac scan mode could be used to study dynamic musculoskeletal phenomena especially of fast-moving joints since it significantly minimized motion artifacts

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine

Registration of magnetic resonance and computed tomography images in patients with oral squamous cell carcinoma for three-dimensional vir

The aim of this study was to evaluate and present an automated method for registration of magnetic resonance imaging (MRI) and computed tomography (CT) or cone beam CT (CBCT) images of the mandibular region for patients with oral squamous cell carcinoma (OSCC). Registered MRI and (CB)CT could facilitate the three-dimensional virtual planning of surgical guides employed for resection and reconstruction in patients with OSCC with mandibular invasion. MRI and (CB)CT images were collected retrospectively from 19 patients. MRI images were aligned with (CB)CT images employing a rigid registration approach (stage 1), a rigid registration approach using a mandibular mask (stage 2), and two non-rigid registration approaches (stage 3). Registration accuracy was quantified by the mean target registration error (mTRE), calculated over a set of landmarks annotated by two observers. Stage 2 achieved the best registration result, with an mTRE of 2.5 ± 0.7 mm, which was comparable to the inter- and intra-observer variabilities of landmark placement in MRI. Stage 2 was significantly better aligned compared to all approaches in stage 3. In conclusion, this study demonstrated that rigid registration with the use of a mask is an appropriate image registration method for aligning MRI and (CB)CT images of the mandibular region in patients with OSCC

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems

Lung motion modelling and estimation for image guided radiation therapy

Notre travail vise à fournir des contributions méthodologiques pour la quantification, caractérisation et la représentation du mouvement du poumon par imagerie, afin de faciliter son intégration dans le processus de traitement. Nous décrivons une méthode originale permettant l\u27extraction automatique d\u27un masque de mouvement qui sépare le thorax en régions mobiles et moins mobiles. En fournissant une interface où des mouvements glissants ont lieux, notre approche permet de préserver les discontinuités dans les champs de mouvement, ce qui facilite le recalage. Des contraintes plus fortes peuvent ainsi être imposées pour chaque région, tout en maintenant la précision de l\u27appariement. Ensuite, nous développons une méthodologie de recalage spacio-temporel pour l\u27imagerie corrélée à la respiration. Un modèle de déformation spatiale basée sur les B-splines est étendu au domaine temporel en le couplant à un modèle de trajectoire cyclique avec des contraintes de lissage approprié. En renforçant la cohérence temporelle de la déformation à travers le cycle respiration, nous améliorons la robustesse du recalage aux artéfacts. Comme application, nous avons également étudié la possibilité d\u27effectuer l\u27estimation de mouvement respiratoire à partir d\u27une séquence de projection CBCT. Un fort a priori est introduit sous la forme d\u27un modèle spécifique au patient construit à partir d\u27une acquisition 4D CT préalable. L\u27estimation de mouvement est réalisée en comparant les projections avec une séquence simulée à l\u27aide du modèle. Nous proposons une approche d\u27optimisation semi-globale, considérant le cycle respiratoire dans son ensemble et fournissant des estimations lisses du mouvement