Morphology of the dysplastic hip and the relationship with sex and acetabular version.

The dysplastic hip is characterized by incomplete coverage of the femoral head, resulting in increased risk of early osteoarthritis. The morphological variation of the hip joint is diverse and clear differences exist between females and males. The aim of this observational study was therefore to investigate the relationship between the morphology of the hip, sex, and hip dysplasia using a three-dimensional model. Statistical shape models of the combined femur and pelvic bones were created from bilateral hips of 75 patients. Using manual angle measurements and regression analysis, the characteristic shape differences associated with sex and hip dysplasia were determined. The model showed clear differences associated with sex and hip dysplasia. We found that the acetabular anteversion in females was significantly higher (p < 0.0001) than in males while no significant difference in acetabular anteversion was found between normal and dysplastic hips (p = 0.11). The model showed that decreased acetabular anteversion resulted in the appearance of the cross-over sign and the prominent ischial spine sign commonly associated with retroversion. Sex could be predicted with an area under the curve of 0.99 and hip dysplasia could be predicted with an area under the curve of ≥0.73. Our findings suggest that retroversion is a result of decreased anteversion of the acetabulum and is primarily associated with sex. This finding should be taken into account during the reorientation of the acetabulum in the surgical treatment of hip dysplasia.

Spinopelvic alignment and lumbar vertebral shape in children: associations with structural spinal abnormalities and body composition in the generation R study.

PURPOSE: To investigate the spinopelvic alignment and vertebral shape in children, and associations with body composition and structural spinal abnormalities on magnetic resonance imaging (MRI). METHODS: We performed a cross-sectional study embedded in the Generation R Study, a prospective population-based birth cohort. Pelvic incidence and vertebral concavity ratios for each lumbar level were determined on sagittal MRI images in 9-year-old children, and structural spinal abnormalities were scored semi-quantitatively. The BMI-SD score was calculated, and body composition was assessed using DXA scans. Associations of pelvic incidence and vertebral concavity ratios with structural abnormalities and body composition measures were assessed using (multilevel) regression analyses. RESULTS: This study included 522 participants (47.7% boys), aged 9.9 years (IQR 9.7-10.0). The mean pelvic incidence was 36.6° (SD 8.0). Vertebral concavity ratios ranged from 0.87 to 0.90, with significantly lower ratios for boys compared to girls. Associations were found for a larger pelvic incidence with decreased disc height [OR 1.03 (95% CI 1.02-1.05)], and a pelvic incidence in the lowest tertile with less disc bulging [OR 0.73 (95% CI 0.56-0.95)]. Increased vertebral concavity ratio was associated with decreased disc height [OR 14.16 (95% CI 1.28-157.13)]. Finally, increased fat-free mass index was associated with a smaller pelvic incidence [adjusted OR 0.85 (95% CI 0.07-1.63)]. CONCLUSION: The mean pelvic incidence of 9-year-old children is 36.6° on supine MRI images, and a slightly concave shape of the lumbar vertebrae is seen. Spinopelvic alignment is associated with structural spinal abnormalities, and might itself be influenced by the children's body composition.

Hippocampal shape is predictive for the development of dementia in a normal, elderly population.

Previous studies have shown that hippocampal volume is an early marker for dementia. We investigated whether hippocampal shape characteristics extracted from MRI scans are predictive for the development of dementia during follow up in subjects who were nondemented at baseline. Furthermore, we assessed whether hippocampal shape provides additional predictive value independent of hippocampal volume. Five hundred eleven brain MRI scans from elderly nondemented participants of a prospective population-based imaging study were used. During the 10-year follow-up period, 52 of these subjects developed dementia. For training and evaluation independent of age and gender, a subset of 50 cases and 150 matched controls was selected. The hippocampus was segmented using an automated method. From the segmentation, the volume was determined and a statistical shape model was constructed. We trained a classifier to distinguish between subjects who developed dementia and subjects who stayed cognitively healthy. For all subjects the a posteriori probability to develop dementia was estimated using the classifier in a cross-validation experiment. The area under the ROC curve for volume, shape, and the combination of both were, respectively, 0.724, 0.743, and 0.766. A logistic regression model showed that adding shape to a model using volume corrected for age and gender increased the global model-fit significantly (P = 0.0063). We conclude that hippocampal shape derived from MRI scans is predictive for dementia before clinical symptoms arise, independent of age and gender. Furthermore, the results suggest that hippocampal shape provides additional predictive value over hippocampal volume and that combining shape and volume leads to better prediction.

Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia.

Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86 - 91%) than all other approaches (AUC = 57 - 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself.

Neurocognitive functioning and radiologic changes in primary CNS lymphoma patients: results from the HOVON 105/ALLG NHL 24 randomized controlled trial.

BACKGROUND: To analyze the effect of treatment on neurocognitive functioning and the association of neurocognition with radiological abnormalities in primary central nervous system lymphoma (PCNSL). METHODS: One hundred and ninety-nine patients from a phase III trial (HOVON 105/ALLG NHL 24), randomized to standard chemotherapy with or without rituximab, followed in patients ≤60 years old by 30-Gy whole-brain radiotherapy (WBRT), were asked to participate in a neuropsychological evaluation before and during treatment, and up to 2 years posttreatment. Scores were transformed into a standardized z-score; clinically relevant changes were defined as a change in z-score of ≥1 SD. The effect of WBRT was analyzed in irradiated patients. All MRIs were centrally assessed for white matter abnormalities and cerebral atrophy, and their relation with neurocognitive scores over time in each domain was calculated. RESULTS: 125/199 patients consented to neurocognitive evaluation. Statistically significant improvements in neurocognition were seen in all domains. A clinically relevant improvement was seen only in the motor speed domain, without differences between the arms. In the follow-up of irradiated patients (n = 43), no change was observed in any domain score, compared to after WBRT. Small but significant inverse correlations were found between neurocognitive scores over time and changes in white matter abnormalities (regression coefficients: -0.048 to -0.347) and cerebral atrophy (-0.212 to -1.774). CONCLUSIONS: Addition of rituximab to standard treatment in PCNSL patients did not impact neurocognitive functioning up to 2 years posttreatment, nor did treatment with 30-Gy WBRT in patients ≤60 years old. Increased white matter abnormalities and brain atrophy showed weak associations with neurocognition.

Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning.

Many medical image segmentation methods are based on the supervised classification of voxels. Such methods generally perform well when provided with a training set that is representative of the test images to the segment. However, problems may arise when training and test data follow different distributions, for example, due to differences in scanners, scanning protocols, or patient groups. Under such conditions, weighting training images according to distribution similarity have been shown to greatly improve performance. However, this assumes that a part of the training data is representative of the test data; it does not make unrepresentative data more similar. We, therefore, investigate kernel learning as a way to reduce differences between training and test data and explore the added value of kernel learning for image weighting. We also propose a new image weighting method that minimizes maximum mean discrepancy (MMD) between training and test data, which enables the joint optimization of image weights and kernel. Experiments on brain tissue, white matter lesion, and hippocampus segmentation show that both kernel learning and image weighting, when used separately, greatly improve performance on heterogeneous data. Here, MMD weighting obtains similar performance to previously proposed image weighting methods. Combining image weighting and kernel learning, optimized either individually or jointly, can give a small additional improvement in performance.

The value of hippocampal volume, shape, and texture for 11-year prediction of dementia: a population-based study.

Hippocampal volume and shape are known magnetic resonance imaging biomarkers of neurodegeneration. Recently, hippocampal texture has been shown to improve prediction of dementia in patients with mild cognitive impairment, but it is unknown whether texture adds prognostic information beyond volume and shape and whether the predictive value extends to cognitively healthy individuals. Using 510 subjects from the Rotterdam Study, a prospective, population-based cohort study, we investigated if hippocampal volume, shape, texture, and their combination were predictive of dementia and determined how predictive performance varied with time to diagnosis and presence of early clinical symptoms of dementia. All features showed significant predictive performance with the area under the receiver operating characteristic curve ranging from 0.700 for texture alone to 0.788 for the combination of volume and texture. Although predictive performance extended to those without objective cognitive complaints or mild cognitive impairment, performance decreased with increasing follow-up time. We conclude that a combination of multiple hippocampal features on magnetic resonance imaging performs better in predicting dementia in the general population than any feature by itself.

Aortic valve calcification volumes and chronic brain infarctions in patients undergoing transcatheter aortic valve implantation.

Chronic silent brain infarctions, detected as new white matter hyperintensities on magnetic resonance imaging (MRI) following transcatheter aortic valve implantation (TAVI), are associated with long-term cognitive deterioration. This is the first study to investigate to which extent the calcification volume of the native aortic valve (AV) measured with cardiac computed tomography angiography (CTA) predicts the increase in chronic white matter hyperintensity volume after TAVI. A total of 36 patients (79 ± 5 years, median EuroSCORE II 1.9%, Q1-Q3 1.5-3.4%) with severe AV stenosis underwent fluid attenuation inversion recovery (FLAIR) MRI < 24 h prior to TAVI and at 3 months follow-up for assessment of cerebral white matter hyperintensity volume (mL). Calcification volumes (mm3) of the AV, aortic arch, landing zone and left ventricle were measured on the CTA pre-TAVI. The largest calcification volumes were found in the AV (median 692 mm3) and aortic arch (median 633 mm3), with a large variation between patients (Q1-Q3 482-1297 mm3 and 213-1727 mm3, respectively). The white matter hyperintensity volume increased in 72% of the patients. In these patients the median volume increase was of 1.1 mL (Q1-Q3 0.3-4.6 mL), corresponding with a 27% increase from baseline (Q1-Q3 7-104%). The calcification volume in the AV predicted the increase of white matter hyperintensity volume (Δ%), with a 35% increase of white matter hyperintensity volume, per 100 mm3 of AV calcification volume (SE 8.5, p < 0.001). The calcification volumes in the aortic arch, landing zone and left ventricle were not associated with the increase in white matter hyperintensity volume. In 72% of the patients new chronic white matter hyperintensities developed 3 months after TAVI, with a median increase of 27%. A higher calcification volume in the AV was associated with a larger increase in the white matter hyperintensity volume. These findings show the potential for automated AV calcium screening as an imaging biomarker to predict chronic silent brain infarctions.

Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners.

Many successful approaches in MR brain segmentation use supervised voxel classification, which requires manually labeled training images that are representative of the test images to segment. However, the performance of such methods often deteriorates if training and test images are acquired with different scanners or scanning parameters, since this leads to differences in feature representations between training and test data. In this paper we propose a feature-space transformation (FST) to overcome such differences in feature representations. The proposed FST is derived from unlabeled images of a subject that was scanned with both the source and the target scan protocol. After an affine registration, these images give a mapping between source and target voxels in the feature space. This mapping is then used to map all training samples to the feature representation of the test samples. We evaluated the benefit of the proposed FST on hippocampus segmentation. Experiments were performed on two datasets: one with relatively small differences between training and test images and one with large differences. In both cases, the FST significantly improved the performance compared to using only image normalization. Additionally, we showed that our FST can be used to improve the performance of a state-of-the-art patch-based-atlas-fusion technique in case of large differences between scanners.