Automated age estimation of young individuals based on 3D knee MRI using deep learning.

Age estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation exposure. Recently, also non-invasive methods using magnetic resonance imaging (MRI) have evaluated and confirmed a correlation between growth plate ossification in long bones and the chronological age of young subjects. However, automated and user-independent approaches are required to perform reliable assessments on large datasets. The aim of this study was to develop a fully automated and computer-based method for age estimation based on 3D knee MRIs using machine learning. The proposed solution is based on three parts: image-preprocessing, bone segmentation, and age estimation. A total of 185 coronal and 404 sagittal MR volumes from Caucasian male subjects in the age range of 13 and 21 years were available. The best result of the fivefold cross-validation was a mean absolute error of 0.67 ± 0.49 years in age regression and an accuracy of 90.9%, a sensitivity of 88.6%, and a specificity of 94.2% in classification (18-year age limit) using a combination of convolutional neural networks and tree-based machine learning algorithms. The potential of deep learning for age estimation is reflected in the results and can be further improved if it is trained on even larger and more diverse datasets.

Forensic age assessment of the knee: proposal of a new classification system using two-dimensional ultrasound volumes and comparison to MRI.

OBJECTIVES: To assess epiphyseal growth plate closure of the knee for forensic age estimation using an ultrasound (US)-based method and to compare the findings with MRI. METHODS: Thirty-three healthy male individuals (age, 14.4-19.3 years) were prospectively evaluated for epiphyseal growth plate closure of the right knee by recordings of two-dimensional US volumes and a high-resolution T1-weighted MRI sequence. The degree of epiphyseal growth plate closure was rated independently by two readers for each method using a modality specific three-point scale that differentiates between an open physis (S1), a partially closed physis (S2), and a closed physis (S3). RESULTS: The inter-rater agreement was high for the US (Cohen's kappa (CK): femur 95.2%, tibia 81.3%, fibula 86.3%) and the MRI method (CK: femur 70.2%, tibia 90.8%, fibula 79.8%). The degree of growth plate closure associated positively with advancing age. The US system showed a clearer separation of median ages with lower overlap than the MRI system. Open growth plates on minors (< S3 on femur and tibia) were identified by US with higher sensitivity (1.0 vs. 0.7) and slightly lower specificity (0.7 vs. 0.85) compared with MRI. The examination time was substantially shorter on US than on MRI (2.65 ± 0.91 min vs. 24.72 ± 2.72 min; p < 0.001). CONCLUSIONS: The US method for evaluation of growth plate closure of the knee can reliably assign male individuals to different ossification stages and identifies minors with high accuracy. More studies with larger numbers are needed to further evaluate this method. KEY POINTS: • US is feasible to determine the degree of epiphyseal growth plate closure of the knee, shows a high degree of reliability, and is comparable to MRI. • US of the knee can detect open growth plates on male minors with high accuracy. • US of the knee may be used as a fast, non-invasive imaging tool for forensic age estimation to identify male minors.

Virtual Reconstruction and Comparative Analyses of the Middle Pleistocene Apidima 2 Cranium (Greece).

The Apidima 2 fossil cranium from South Peloponnese is one of the most important hominin specimens from Southeast Europe. Nevertheless, there has been continuous controversy as to whether it represents a so-called Preneandertal/Homo heidelbergensis such as, for example, the Petralona cranium from Northern Greece or a more derived Neandertal. Recent absolute dating evidence alone cannot clarify the issue because both classifications would be possible during the respective Middle Pleistocene time span. Since only limited data were available on the cranium, there have been repeated claims for the need of a broader comparative study of the hominin. The present article presents a CT-based virtual reconstruction including corrections of postmortem fractures and deformation as well as detailed metrical and morphological analyses of the specimen. Endocranial capacity could be estimated for the first time based on virtual reconstruction. Our multivariate analyses of metric data from the face and vault revealed close affinities to early and later Neandertals, especially showing the derived facial morphometrics. In addition, comparative analyses of Apidima 2 were done for many derived Neandertal features. Here again, a significant number of Neandertal features could be found in the Apidima cranium but no conditions common in Preneandertals. In agreement with a later Middle Pleistocene age Apidima is currently the earliest evidence of a hominin in Europe with such a derived Neandertal facial morphology. The place of Apidima in the complex process of Neandertal evolution as well as its taxonomic classification are discussed as well. Anat Rec, 303:1374-1392, 2020. © 2019 American Association for Anatomy.

A 2-year follow-up MRI study for the evaluation of an age estimation method based on knee bone development.

Age estimation is an actual topic in the area of forensic medicine with a special focus on the age limits of 16 and 18 years. Current research on this topic relies on retrospective data of inhomogeneous populations relating to sex, age range, and socioeconomic status. In this work, we present a 2-year follow-up study for the evaluation of an age estimation method on a prospective magnetic resonance imaging (MRI) knee data collective of a homogeneous population. The study includes 40 male subjects from northern Germany aged 14 to 21 years. Three MRI examinations were evenly acquired within 2 years for each subject. As a first evaluation, a three-stage system was used to assess the ossification status of the knee (I:"open", II:"partially ossified", III:"fully ossified"). Three raters assessed the growth plate of the distal femur, proximal tibia, and proximal fibula based on central 2D slices. A good inter-rater agreement was attained (κ = 0.84). All subjects younger than 18 years were rated as stage I and had a cumulative knee score (SKJ) ≤ 5. Based on the follow-up datasets, new parameters quantifying the intra-individual ossification process were calculated. The results of this follow-up analysis show a different start, end, and speed of each growth plate's maturation as well as an ossification peak for individuals at the age of 16. The generated MRI database provides new insights into the ossification process over time and serves as a basis for further evaluations of age estimation methods.

Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks.

Age assessment is used to estimate the chronological age of an individual who lacks legal documentation. Recent studies indicate that the ossification degree of the growth plates in the knee joint correlates with chronological age of adolescents and young adults. To verify this hypothesis, a high number of datasets need to be analysed. An approach which enables an automated detection and analysis of the bone structures may be necessary to handle large datasets. The purpose of this study was to develop a fully automatic 2D knee segmentation based on 3D MR images using convolutional neural networks. A total of 76 datasets were available and divided into a training set (74%), a validation set (13%) and a test set (13%). Multiple preprocessing steps were applied to correct image intensity values and to reduce the image size. Image augmentation was employed to virtually increase the dataset size for training. The proposed architecture for the segmentation task resembles the encoder-decoder model type used for the U-Net. The trained network achieved a dice similarity coefficient score of 98% compared to the manual segmentations and an intersection over union of 96%. The precision and recall of the model were balanced, and the error was only 1.2%. No overfitting was observed during training. As a proof of concept, the predicted segmentations were used for the age estimation of 145 subjects. Initial results show the potential of this approach attaining a mean absolute error of 0.48 ± 0.32 years for a test set of 14 subjects. The proposed automated segmentation can contribute to faster, reproducible and potentially more reliable age estimation in the future.

Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.

Acute versus Chronic Myocardial Infarction: Diagnostic Accuracy of Quantitative Native T1 and T2 Mapping versus Assessment of Edema on Standard T2-weighted Cardiovascular MR Images for Differentiation.

Purpose To analyze the diagnostic accuracy of native T1 and T2 mapping compared with visual and quantitative assessment of edema on T2-weighted cardiac magnetic resonance (MR) images to differentiate between acute and chronic myocardial infarction. Materials and Methods This study had institutional ethics committee approval. Written informed consent was obtained from 67 consecutive patients (57 years ± 12; 78% men) with a first acute myocardial infarction, who were prospectively enrolled between April 2011 and June 2015. Four serial 1.5-T MR imaging examinations were performed at 8 days ± 5, 7 weeks ± 2, 3 months ± 0.5, and 6 months ± 1.4 after infarction and included T2-weighted, native T1/T2 mapping, and late gadolinium enhancement MR imaging. Complete follow-up data were obtained in 42 patients. Regional native T1/T2 relaxation time, T2-weighted ratio, and extracellular volume were serially measured in infarcted and remote myocardium. Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accuracy of the MR imaging parameters for discriminating between acute and chronic myocardial infarction. Results Native T1 of infarcted myocardium decreased from 1286 msec ± 99 at baseline to 1077 msec ± 50 at 6 months (P < .0001), whereas T2 decreased from 84 msec ± 10 to 58 msec ± 4 (P < .0001). The T2-weighted ratio decreased from 4.1 ± 1.0 to 2.4 ± 0.6 (P < .0001). Of all the MR imaging parameters obtained, native T1 and T2 yielded the best areas under the ROC curve (AUCs) of 0.975 and 0.979, respectively, for differentiating between acute and chronic myocardial infarction. Visual analysis of the presence of edema at standard T2-weighted cardiac MR imaging resulted in an inferior AUC of 0.863 (P < .01). Conclusion Native T1 and T2 of infarcted myocardium are excellent discriminators between acute and chronic myocardial infarction and are superior to all other MR imaging parameters. Online supplemental material is available for this article.

Prediction of the estimated 5-year risk of sudden cardiac death and syncope or non-sustained ventricular tachycardia in patients with hypertrophic cardiomyopathy using late gadolinium enhancement and extracellular volume CMR.

OBJECTIVES: To evaluate the ability of late gadolinium enhancement (LGE) and mapping cardiac magnetic resonance (CMR) including native T1 and global extracellular volume (ECV) to identify hypertrophic cardiomyopathy (HCM) patients at risk for sudden cardiac death (SCD) and to predict syncope or non-sustained ventricular tachycardia (VT). METHODS: A 1.5-T CMR was performed in 73 HCM patients and 16 controls. LGE size was quantified using the 3SD, 5SD and full width at half maximum (FWHM) method. T1 and ECV maps were generated by a 3(3)5 modified Look-Locker inversion recovery sequence. Receiver-operating curve analysis evaluated the best parameter to identify patients with increased SCD risk ≥4% and patients with syncope or non-sustained VT. RESULTS: Global ECV was the best predictor of SCD risk with an area under the curve (AUC) of 0.83. LGE size was significantly inferior to global ECV with an AUC of 0.68, 0.70 and 0.70 (all P < 0.05) for 3SD-, 5SD- and FWHM-LGE, respectively. Combined use of the SCD risk score and global ECV significantly improved the diagnostic accuracy to identify HCM patients with syncope or non-sustained VT. CONCLUSIONS: Combined use of the SCD risk score and global ECV has the potential to improve HCM patient selection, benefiting most implantable cardioverter defibrillators. KEY POINTS: • Global ECV identified the best HCM patients with increased SCD risk. • Global ECV performed equally well compared to a SCD risk score. • Combined use of the SCD risk score and global ECV improved test accuracy. • Combined use potentially improves selection of HCM patients for ICD implantation.

T1 and T2 mapping cardiovascular magnetic resonance imaging techniques reveal unapparent myocardial injury in patients with myocarditis.

INTRODUCTION: This study evaluated the ability of T1 and T2 mapping cardiovascular magnetic resonance (CMR) to detect myocardial injury in apparently normal myocardium of patients with myocarditis. MATERIALS AND METHODS: We included 20 patients with "infarct-like" acute myocarditis who had typical focal myocardial lesions on late gadolinium enhancement (LGE) images as well as 20 healthy controls. The CMR protocol consisted of a standard myocarditis protocol which was combined with T1 (modified Look-Locker inversion recovery (MOLLI) with a 3(3)5 scheme and T2 mapping (hybrid gradient- and spin-echo multi-echo sequence, GraSE). First, LGE images were used to depict focal myocardial injury and apparently normal, remote myocardium. Second, native T1, T2 and ECV values were obtained in focal lesions but also in apparently normal myocardium. Third, native T1, T2 and ECV values ≥2 standard deviations above reference values obtained in healthy volunteers were used to quantify myocardial injury in patients with myocarditis. RESULTS: Apparently normal myocardium had significantly higher median native T1 [1095 (1055-1148) ms] and ECV [34 (32-35) %] values compared to reference values from healthy volunteers, which were 1051 (1021-1064) ms (p < 0.01) and 26 (24-27) % (p < 0.0001). Furthermore, a nonsignificant increase in median myocardial T2 was detected in apparently normal myocardium of patients with myocarditis compared to healthy volunteers [59 (55-65) vs. 56 (54-60) ms; p = 0.18]. Consequently, the amount of myocardial injury was significantly larger on native T1 [48 (32-56) %; p < 0.01] and ECV maps [58 (50-66) %; p < 0.01] compared to LGE [14 (9-20) %]. CONCLUSIONS: Native T1 and ECV maps reveal hidden myocardial injury in normal appearing myocardium of patients with myocarditis. The amount of myocardial injury in myocarditis was underestimated by conventional LGE imaging.

[Reconstructive investigations and identification measures in unknown soldiers of the Second World War]. / Rekonstruktive Untersuchungen und Identifikationsmassnahmen bei unbekannten Soldaten aus dem Zweiten Weltkrieg.

The article reports on the exhumation and identification of unknown soldiers from the Second World War. With the help of medicolegal investigation and reconstruction methods an American pilot presumably murdered by a shot to the head (lynch law) and an interned Italian soldier could be identified after about 70 years and brought back home.

Serum Matrix Metalloproteinases as Quantitative Biomarkers for Myocardial Fibrosis and Sudden Cardiac Death Risk Stratification in Patients With Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is associated with an increased risk of sudden cardiac death due to ventricular tachycardia (VT), and myocardial fibrosis reflects an important risk factor. Several matrix metalloproteinases (MMPs) and procollagen N-terminal propeptides (PNPs) are involved in collagen turnover and discussed as fibrosis biomarkers. We aimed to identify biomarkers that correlate with myocardial fibrosis in late-gadolinium-enhancement cardiac magnetic resonance imaging (LGE-CMR) and/or cardiac events (syncope, VT) in HCM patients. METHODS AND RESULTS: In 54 HCM patients (age 55.9 ± 14.3 y, 50% female) fibrosis was quantified by LGE-CMR. Serum concentrations of MMP-1, -2, -3, -9, and tissue inhibitor of MMP (TIMP) 1 were analyzed by means of enzyme-linked immunosorbent assay and PINP, PIIINP, and type I collagen C-terminal telopeptide (ICTP) concentrations by radioimmunoassay. MMP-9 was associated with fibrosis in LGE-CMR (mean increase 0.66 g/unit MMP9 [95% confidence interval [CI] 0.50-0.82]; P < .001) and with cardiac events in women (odds ratio [OR] 1.07 [1.01-1.12], P = .01) but not in men. Increased MMP-2 levels in women were associated with lower fibrosis (0.05 [-0.09 to -0.01]; P = .015). MMP-3 levels were positively associated with cardiac events (OR 1.13 [1.05-1.22]; P = .001) independently from fibrosis and sex. No association was detected for MMP-1, TIMP-1, PNPs, and ICTP. CONCLUSIONS: These data suggest that MMP-9 is a useful biomarker for fibrosis and cardiac events in female HCM patients, whereas MMP-3 is associated with a higher event rate independent from fibrosis and sex.

Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques.

OBJECTIVE: 3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved. METHODS: Eight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated. RESULTS: The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6mm. CONCLUSION: TOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.

Cardiovascular magnetic resonance demonstrates reversible atrial dysfunction after catheter ablation of persistent atrial fibrillation.

INTRODUCTION: There is a paucity of data on atrial injury following ablation of persistent atrial fibrillation (AF). This study aimed at assessing reversibility of atrial dysfunction after successful persistent AF ablation using cardiovascular magnetic resonance (CMR). METHODS AND RESULTS: CMR was performed during sinus rhythm (SR) in 20 consecutive patients with persistent AF at baseline (BL) within 24 hours after ablation and after 6-month follow-up (FU). Catheter ablation included atrial substrate modification using the stepwise approach following pulmonary vein isolation (PVI) in order to attempt termination of persistent AF. Active left (LA) and right atrial (RA) function were quantified by calculating the active emptying fraction (AEF) from transvalvular flow profiles using velocity encoded (VENC) CMR. LA appendage (LAA) function was quantified by measurements of peak a-wave velocities from flow profiles perpendicular to the LAA orifice. Peri-atrial edema was assessed using black-blood T2 -weighted CMR. A significant improvement was found in LA-AEF from 18 (12-26)% at BL to 25 (22-35)% at FU (P = 0.0001). Furthermore, RA-AEF significantly increased from 31 (19-35)% at BL to 40 (35-51)% at FU (P < 0.0001). A significant improvement was also found for LAA a-wave velocities from 45 (31-65) cm/s at BL to 62 (49-75) cm/s at FU (P < 0.01). The area of peri-atrial edema on T2 -weighted CMR decreased from 1393 (1098-1797) mm(2) at BL to 24 (1-92) mm(2) at FU (P < 0.0001). CONCLUSION: CMR demonstrates reversibility of LA, LAA, and RA dysfunction associated with resorption of peri-atrial edema in patients with SR after persistent AF ablation.

Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations.

PURPOSE: Exact knowledge about the nidus of an arteriovenous malformation (AVM) and the connected vessels is often required for image-based research projects and optimal therapy planning. The aim of this work is to present and evaluate a computer-aided nidus segmentation technique and subsequent angiographic characterization of the connected vessels that can be visualized in 3D. METHODS: The proposed method was developed and evaluated based on 15 datasets of patients with an AVM. Each dataset consists of a high-resolution 3D and a 4D magnetic resonance angiography (MRA) image sequence. After automatic cerebrovascular segmentation from the 3D MRA dataset, a voxel-wise support vector machine classification based on four extracted features is performed to generate a new parameter map. The nidus is represented by positive values in this parameter map and can be extracted using volume growing. Finally, the nidus segmentation is dilated and used for an automatic identification of feeding arteries and draining veins by integrating hemodynamic information from the 4D MRA datasets. RESULTS: A quantitative comparison of the computer-aided AVM nidus segmentation results to manual gold-standard segmentations by two observers revealed a mean Dice coefficient of 0.835, which is comparable to the inter-observer agreement for which a mean Dice coefficient of 0.830 was determined. The angiographic characterization was visually rated feasible for all patients. CONCLUSION: The presented computer-aided method enables a reproducible and fast extraction of the AVM nidus as well as an automatic angiographic characterization of the connected vessels, which can be used to support image-based research projects and therapy planning of AVMs.

3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights.

The aim of this work is to present and evaluate a level-set segmentation approach with vesselness-dependent anisotropic energy weights, which focuses on the exact segmentation of malformed as well as small vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) datasets. In a first step, a vesselness filter is used to calculate the vesselness dataset, which quantifies the likeliness of each voxel to belong to a bright tubular-shaped structure and estimate the corresponding vessel directions from a given TOF dataset. The vesselness and TOF datasets are then combined using fuzzy-logic and used for initialization of a variational level-set method. The proposed level-set model has been extended in a way that the weight of the internal energy is locally adapted based on the vessel direction information. Here, the main idea is to weight the internal energy lower if the gradient direction of the level-set is similar to the direction of the eigenvector extracted by the vesselness filter. Furthermore, an additional vesselness force has been integrated in the level-set formulation. The proposed method was evaluated based on ten TOF MRA datasets from patients with an arteriovenous malformation. Manual segmentations from two observers were available for each dataset and used for quantitative comparison. The evaluation revealed that the proposed method yields significantly better segmentation results than four other state-of-the-art segmentation methods tested. Furthermore, the segmentation results are within the range of the inter-observer variation. In conclusion, the proposed method allows an improved delineation of small vessels, especially of those represented by low intensities and high surface curvatures.

4D blood flow visualization fusing 3D and 4D MRA image sequences.

PURPOSE: To present and evaluate the feasibility of a novel automatic method for generating 4D blood flow visualizations fusing high spatial resolution 3D and time-resolved (4D) magnetic resonance angiography (MRA) datasets. MATERIALS AND METHODS: In a first step, the cerebrovascular system is segmented in the 3D MRA dataset and a surface model is computed. The hemodynamic information is extracted from the 4D MRA dataset and transferred to the surface model using rigid registration where it can be visualized color-coded or dynamically over time. The presented method was evaluated using software phantoms and 20 clinical datasets from patients with an arteriovenous malformation. Clinical evaluation was performed by comparison of Spetzler-Martin scores determined from the 4D blood flow visualizations and corresponding digital subtraction angiographies. RESULTS: The performed software phantom validation showed that the presented method is capable of producing reliable visualization results for vessels with a minimum diameter of 2 mm for which a mean temporal error of 0.27 seconds was achieved. The clinical evaluation based on 20 datasets comparing the 4D visualization to DSA images revealed an excellent interrater reliability. CONCLUSION: The presented method enables an improved combined representation of blood flow and anatomy while reducing the time needed for clinical rating.

Evaluation of different magnetic resonance imaging techniques for the assessment of active left atrial emptying.

OBJECTIVES: There is currently no agreement on the best method of assessing active left atrial (LA) emptying. This study evaluated the relative merits of cine- and velocity encoded (VENC) magnetic resonance imaging (MRI) for the assessment of active LA emptying. METHODS: Total LA emptying volume (TLAEV) and active LA stroke volume (ALASV) were assessed in 107 consecutive patients using cine-MRI and transmitral flow measurements by VENC-MRI. The fraction of active LA emptying (ALAEF) was calculated as the ratio of ALASV to TLAEV. LA and left ventricular (LV) output were calculated by multiplying TLAEV and LV stroke volume by heart rate, respectively. RESULTS: Intra- and inter-observer variances were significantly larger for cine-MRI than for VENC-MRI measurements of ALASV (24.7 mL(2) vs. 3.7 mL(2) and 57.7 mL(2) vs. 4.2 mL(2); P < 0.0001). Biplane cine-MRI underestimated TLAEV (mean difference -57 ± 32 %; P < 0.0001) and ALASV (mean difference -24 ± 51 %; P < 0.0001) but overestimated ALAEF (mean difference 31 ± 54 %, P < 0.0001) compared with VENC-MRI. There was significantly better agreement between LV output and LA output measured by VENC-MRI compared with LA output measured by cine-MRI (mean difference 0.30 ± 1.12 L/min vs. -2.05 ± 1.44 L/min; P < 0.0001). CONCLUSION: VENC-MRI is the more appropriate method of assessing active LA emptying and its use should be favoured.

Rigid 3D-3D registration of TOF MRA integrating vessel segmentation for quantification of recurrence volumes after coiling cerebral aneurysm.

INTRODUCTION: A fast and reproducible quantification of the recurrence volume of coiled aneurysms is required to enable a more timely evaluation of new coils. This paper presents two registration schemes for the semi-automatic quantification of aneurysm recurrence volumes based on baseline and follow-up 3D MRA TOF datasets. METHODS: The quantification of shape changes requires a previous definition of corresponding structures in both datasets. For this, two different rigid registration methods have been developed and evaluated. Besides a state-of-the-art rigid registration method, a second approach integrating vessel segmentations is presented. After registration, the aneurysm recurrence volume can be calculated based on the difference image. The computed volumes were compared to manually extracted volumes. RESULTS: An evaluation based on 20 TOF MRA datasets (baseline and follow-up) of ten patients showed that both registration schemes are generally capable of providing sufficient registration results. Regarding the quantification of aneurysm recurrence volumes, the results suggest that the second segmentation-based registration method yields better results, while a reduction of the computation and interaction time is achieved at the same time. CONCLUSION: The proposed registration scheme incorporating vessel segmentation enables an improved quantification of recurrence volumes of coiled aneurysms with reduced computation and interaction time.

Quantification of recurrence volumes after endovascular treatment of cerebral aneurysm as surrogate endpoint for treatment stability.

INTRODUCTION: New coils with unproven clinical benefit enlarge the armamentarium for endovascular aneurysm treatment continuously. Large patient numbers needed to detect benefits of such new techniques prevent timely evaluation of efficacy. We propose measuring the volume of aneurysm recurrences as surrogate endpoint for coil stability. We hypothesize that this method allows detecting effects of new materials with reduced sample sizes in comparison to conventional studies with dichotomous endpoints. METHODS: Institutional review board approval and informed consent were obtained. Fifty-nine patients with decreasing aneurysm size and at least two available follow-up time-of-flight magnetic resonance angiographies (ToF-MRAs) were included. Newly developed software for measuring aneurysm volume differences based on ToF-MRA images was used. Based on the observed recurrence volumes and retreatment rates, the sample size for future studies comparing standard versus "new coils" were calculated. RESULTS: Mean recurrence volume was 38.92 µl (SD110.85 µl). To show a 50% reduction of retreatment rate to control (p = 0.05; power 80%) in a regular study (dichotomous endpoint), the required sample size would be n = 356 compared with n = 78 if using the continuous surrogate endpoint "recurrence volume". When extrapolating our data to data given in the literature, sample sizes could be reduced from n = 948 to n = 74 without loss of statistical power. CONCLUSION: Further studies on new materials using volumetric analysis based on ToF-MRA as surrogate endpoint could substantially decrease sample size and allow a more timely assessment of possible benefit of new materials with a fraction of the cost.

Reference-based linear curve fitting for bolus arrival time estimation in 4D MRA and MR perfusion-weighted image sequences.

The bolus arrival time (BAT) based on an indicator dilution curve is an important hemodynamic parameter. As the direct estimation of this parameter is generally problematic, various parametric models have been proposed that describe typical physiological shapes of indicator dilution curves, but it remains unclear which model describes the real physiological background. This article presents a method that indirectly incorporates physiological information derived from the data available. For this, a patient-specific hemodynamic reference curve is extracted, and the corresponding reference BAT is determined. To estimate a BAT for a given signal curve, the reference curve is fitted linearly to the signal curve. The parameters of the fitting process are then used to transfer the reference BAT to the signal curve. The validation of the method proposed based on Monte Carlo simulations showed that the approach presented is capable of improving the BAT estimation precision compared with standard BAT estimation methods by up to 59% while at the same time reduces the computation time. A major benefit of the method proposed is that no assumption about the underlying distribution of indicator dilution has to be made, as it is implicitly modeled in the reference curve.