Computational assessment of bone microarchitecture in the diagnosis of osteoporosis

In osteoporosis the assessment of the bone health status is important to identify fracture risk before are fragility fracture occurs. Invasive bone biopsies might be replaced by in-vivo virtual biopsies using high resolution peripheral quantitative computed tomography (HR-pQCT), which provide insights of the bone micrarchitecture at trabecular scale. Histomorphometry is usually applied to theses tomographic images by using stereology methods, however only a few post-processing methods capture image features beyond these global-based measurements. The high isotropic spatial resolution of HR-pQCT scans has the potential to provide more insights of the bone microarchitecture, nevertheless the high information density needs to be processed to extract relevant features describing the the different bone patterns. The objective of the thesis is to develop a novel post processing technique of in-vivo images by classifying the bone microarchitecture with 3D-texture analysis in a supervised and unsupervised learn- ing manner. Since we are only interested in the trabecular compartment, it is necessary to have an accurate and reproducible segmentation algorithm. For this reason, we introduce a reproducible and novel segmentation approach for HR-pQCT data, which uses a fully automated threshold-independent image analysis algorithm based on local texture features. Texture analysis is used for feature extraction describing the bone microarchitecture by a statistical approach that captures the distribution and relationship of the intensities in an image. Clustering is applied to these extracted texture features which result in distinctive trabecular microarchitecture classes (TMACs). These classes represent trabecular bone regions with common texture characteristics representing different patterns of the bone. Experimental results demonstrate the feasibility of 3D-texture analysis and trabecular bone clustering on HR-pQCT images of the ultradistal radius. These experiments include a preliminary application of the technique to a small set of HR-pQCT scans of postmenopausal women with and without fragility fractures. In addition, the clinical applicability of our method using a routine clinical multi detector computed tomography (MDCT) with optimized scan protocols shows promising results in advanced osteoporosis imaging and assessment.Entscheidend bei der Erkennung von Osteoporose ist das Frakturrisiko vorherzubestimmen. Mit neuen Bildgebendenverfahren, wie etwa dem hochauflösenden peripheren quantitativen CT (HR-pQCT), ist es möglich eine nicht-invasive Beurteilung der kortikalen und trabekulären Mikroarchitektur des Knochens zu erlangen. Es besteht großes Forschungsinteresse, die Bildeigenschaften dieser hochauflösenden Bilder zu beschreiben, die über die Standardbeurteilung von Osteoporose hinausgehen. Die 3D-Texturanalyse ist ein wichtiger Ansatz in der Computer Vision und könnte bei der Quantifizierung von struktur-basierten Metriken im Knochen von Bedeutung sein. Motiviert durch die hohe isotrope räumliche Auflösung und die enorme Informationsdichte der HR-pQCT Aufnahmen, ist das Ziel dieser Arbeit einen Nachbearbeitungsalgorithmus zu entwickeln, welcher die Knochenmikroarchitektur mittels Texturanalyse und Clustering quantifiziert. Da wir nur am trabekulären Knochen interessiert sind, besteht die Notwendigkeit eines akkuraten und reproduzierbaren Segmentierungsalgorithmus. Aus diesem Grund wird eine reproduzierbarere und voll automatische Segmentierungsmethode für HR-pQCT Daten eingesetzt, die nicht auf einem Schwellwertverfahren basiert, sondern mittels Einbindung lokaler Texturmerkmale erfolgt. Texturanalyseverfahren werden eingesetzt, um Texturemerkmale der Knochenmikroarchitektur statistisch zu beschreiben. Dabei werden die Verteilung und das Verhältnis der Bildintensitäten erfasst, die dann weiter verbreitet werden können. Clustering wird auf diesen extrahierten Texturmerkmalen eingesetzt, die zur Bestimmung charakteristischer "Trabekulärer Mikroarchitektur Klassen" führen. Diese Klassen repräsentieren Regionen im trabekulären Knochen, die gemeinsame Textureigenschaften aufweisen, um die verschiedenen architektonischen Muster des Knochens zu beschreiben. Experimentelle Ergebnisse demonstrieren die Durchführbarkeit von 3D-Texturanalysen und Clustering in trabekulärem Knochen, die an einer kleinen klinischen Kohorte von postmenopausalen Frauen mit und ohne Frakturen getestet worden sind. Darüber hinaus zeigt die klinische Anwendbarkeit der Methode unter Verwendung eines routinemäßigen klinischen Multidetektor-CTs (MDCT) mit optimierten Scan-Protokollen vielversprechende Ergebnisse in der Bildgebung und Osteoporosediagnostik.submitted by Alexander ValentinitschAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheAutomated threshold-independent cortex segmentation by 3D-texture analysis of HR-pQCT scans; Bone. 2012 Sep;51(3):480-7 / Computational identification and quantification of trabecular microarchitecture classes by 3-D texture analysis-based clustering; Bone. 2013 May;54(1):133-40Wien, Med. Univ., Diss., 2014OeBB(VLID)171355

Analysis and quantification of bone healing after open wedge high tibial osteotomy

Background: The aim of this study was to analyze radiographic imaging techniques and to quantify bone ossification in the osteotomy gap after high tibial osteotomy. ------ Material and methods: Study phase 1: high tibial osteotomy was performed on six lower extremities of human body donors and experimental X‑rays and computed tomography (CT) scans were applied. Different techniques were evaluated by three specialists for best representation of the osteotomy gap. Study phase 2: optimized radiological techniques were used for follow-up on 12 patients. The radiographs were examined by 3 specialists measuring 10 different parameters. The CT scans were analyzed with semiautomatic computer software for quantification of bone ossification. ----- Results: The osteotomy gap was best represented in 30° of flexion in the knee and 20° internal rotation of the leg. There were significant changes of the medial width over time (p < 0.019) as well as of the length of fused osteotomy, the Schröter score, sclerosis, trabecular structure and zone area measurements. Sclerosis, medial width of the osteotomy and area measurements were detected as reproducible parameters. Bone mineral density was calculated using CT scans, showing a significantly higher value 12 weeks postoperatively (112.5 mg/cm3) than at baseline (54.6 mg/cm3). The ossification of the gap was visualized by color coding. ----- Conclusion: Sclerosis and medial width of the osteotomy gap as well as area measurements were determined as reproducible parameters for evaluation of bone healing. Quantification of bone ossification can be calculated with CT scans using a semiautomatic computer program and should be used for research in bone healing

Diagnostic Potential of Pulsed Arterial Spin Labeling in Alzheimer's Disease

Alzheimers disease (AD) is the most common cause of dementia. Although the underlying pathology is still not completely understood, several diagnostic methods are available. Frequently, the most accurate methods are also the most invasive. The present work investigates the diagnostic potential of Pulsed Arterial Spin Labeling (PASL) for AD: a non-invasive, MRI-based technique for the quantification of regional cerebral blood flow (rCBF). In particular, we propose a pilot computer aided diagnostic (CAD) procedure able to discriminate between healthy and diseased subjects, and at the same time, providing visual informative results. This method encompasses the creation of a healthy model, the computation of a voxel-wise likelihood function as comparison between the healthy model and the subject under examination, and the correction of the likelihood function via prior distributions. The discriminant analysis is carried out to maximize the accuracy of the classification. The algorithm has been trained on a dataset of 81 subjects and achieved a sensitivity of 0.750 and a specificity of 0.875. Moreover, in accordance with the current pathological knowledge, the parietal lobe, and limbic system are shown to be the main discriminant factors

Automated unsupervised multi‐parametric classification of adipose tissue depots in skeletal muscle

PurposeTo introduce and validate an automated unsupervised multi-parametric method for segmentation of the subcutaneous fat and muscle regions to determine subcutaneous adipose tissue (SAT) and intermuscular adipose tissue (IMAT) areas based on data from a quantitative chemical shift-based water-fat separation approach.Materials and methodsUnsupervised standard k-means clustering was used to define sets of similar features (k = 2) within the whole multi-modal image after the water-fat separation. The automated image processing chain was composed of three primary stages: tissue, muscle, and bone region segmentation. The algorithm was applied on calf and thigh datasets to compute SAT and IMAT areas and was compared with a manual segmentation.ResultsThe IMAT area using the automatic segmentation had excellent agreement with the IMAT area using the manual segmentation for all the cases in the thigh (R(2): 0.96) and for cases with up to moderate IMAT area in the calf (R(2): 0.92). The group with the highest grade of muscle fat infiltration in the calf had the highest error in the inner SAT contour calculation.ConclusionThe proposed multi-parametric segmentation approach combined with quantitative water-fat imaging provides an accurate and reliable method for an automated calculation of the SAT and IMAT areas reducing considerably the total postprocessing time