Texture analysis of the radiographic trabecular bone pattern in osteoporosis

Texture is an image property which is difficult to grasp. It can be described as a "homogeneous visual pattern"l. but there exists no formal definition of texture. Intuitively people can discriminate between different textures. referring to visual clues like coarseness, orientation. periodicity, and regularity. Using such concepts, several authors have tried to quantify these aspects of texture'. However. texture encompasses more than these more or less random aspects to which the human eye is sensitive. Therefore, the majority of texture analysis algorithms is based on an image model. in which certain characteristics of the image texture are condensed. Using this image model, texture features can be derived, most of which cannot be related to visual image features. Texture analysis methods are able, in contrast to a human observer, to quantIfy textures objectively. Therefore. texture features can be used for the purpose of characterization, discrimination, and segmentation of textures in. for example, aerial and satellite imagery. Most texture analysis methods have been developed and tested on textures from the collection of texture images in Brodatz' before putting them mto use in a more realistic environment. Since the early seventies, texture analysis methods have also been applied In medical images. For example, Sulton et a!. tried to categorize different stages of pulmonary disease in radiographs4 Since then, the field of application of texture analysis methods in radiology has expanded from chest radiographs to mammograms and bone radiographs. The goal of our study is twofold: in the first place to assess the suitability of different texture analysis methods for usc in radiographs, secondly to select or develop texture features which are able to quantify the changes in the radiographic trabecular pattern occurring in osteoporosis. Osteoporosis is defined as "a disease characterized by low bone mass and microarchitectural changes of bone tissue, leading to enhanced bone fragility and a consequent increase in fracture risk." (WHO, 1994)

The teaching practices with GIS in Hyogo Prefecture, Japan

This study aims to quantify the heterogeneity of tumour enhancement in dynamic contrast-enhanced MRI (DCE-MRI) using texture analysis methods. The suitability of the coherence and the fractal dimension to monitor tumour response was evaluated in 18 patients with limb sarcomas imaged by DCE-MRI pre- and post-treatment. According to the histopathology, tumours were classified into responders and non-responders. Pharmacokinetic (K(trans)) and heuristic model-based parametric maps (slope, max enhancement, AUC) were computed from the DCE-MRI data. A substantial correlation was found between the pharmacokinetic and heuristic model-based parametric maps: ρ = 0.56 for the slope, ρ = 0.44 for maximum enhancement, and ρ = 0.61 for AUC. From all four parametric maps, the enhancing fraction, and the heterogeneity features (i.e. coherence and fractal dimension) were determined. In terms of monitoring tumour response, using both pre- and post-treatment DCE-MRI, the enhancing fraction and the coherence showed significant differences between the response group and the non-response group (i.e. the highest sensitivity (91%) for K(trans), and the highest specificity (83%) for max enhancement). In terms of treatment prediction, using solely the pre-treatment DCE-MRI, the enhancing fraction and coherence discriminated between responders and non-responders. For prediction, the highest sensitivity (91%) was shared by K(trans), slope and max enhancement, and the highest specificity (71%) was achieved by K(trans). On average, tumours that responded showed a high enhancing fraction and high coherence on the pre-treatment scan. These results suggest that specific heterogeneity features, computed from both pharmacokinetic and heuristic model-based parametric maps, show potential as a biomarker for monitoring tumour response

Introducing a new method for classifying skull shape abnormalities related to craniosynostosis

We present a novel technique for classification of skull deformities due to most common craniosynostosis. We included 5 children of every group of the common craniosynostoses (scaphocephaly, brachycephaly, trigonocephaly, and right- and left-sided anterior plagiocephaly) and additionally 5 controls. Our outline-based classification method is described, using the software programs OsiriX, MeVisLab, and Matlab. These programs were used to identify chosen landmarks (porion and exocanthion), create a base plane and a plane at 4 cm, segment outlines, and plot resulting graphs. We measured repeatability and reproducibility, and mean curves of groups were analyzed. All raters achieved excellent intraclass correlation scores (0.994–1.000) and interclass correlation scores (0.989–1.000) for identifying the external landmarks. Controls, scaphocephaly, trigonocephaly, and brachycephaly all have the peak of the forehead in the middle of the curve (180°). In contrary, in anterior plagiocephaly, the peak is shifted (to the left of graph in right-sided and vice versa). Additionally, controls, scaphocephaly, and trigonocephaly have a high peak of the forehead; scaphocephaly has the lowest troughs; in brachycephaly, the width/frontal peak ratio has the highest valu

Optimization of combined temozolomide and peptide receptor radionuclide therapy (PRRT) in mice after multimodality molecular imaging studies

Background: Successful treatments of patients with somatostatin receptor (SSTR)-overexpressing neuroendocrine tumours (NET) comprise somatostatin-analogue lutetium-177-labelled octreotate (177Lu-TATE) treatment, also referred to as peptide receptor radionuclide therapy (PRRT), and temozolomide (TMZ) treatment. Their combination might result in additive effects. Using MRI and SPECT/CT, we studied tumour characteristics and therapeutic responses after different (combined) administration schemes in a murine tumour model in order to identify the optimal treatment schedule for PRRT plus TMZ. Methods: We performed molecular imaging studies in mice bearing SSTR-expressing H69 (humane small cell lung cancer) tumours after single intravenous (i.v.) administration of 30 MBq 177Lu-TATE or

Imaging heterogeneity of peptide delivery and binding in solid tumors using SPECT imaging and MRI

Background: As model system, a solid-tumor patient-derived xenograft (PDX) model characterized by high peptide receptor expression and histological tissue homogeneity was used to study radiopeptide targeting. In this solid-tumor model, high tumor uptake of targeting peptides was expected. However, in vivo SPECT images showed substantial heterogeneous radioactivity accumulation despite homogenous receptor distribution in the tumor xenografts as assessed by in vitro autoradiography. We hypothesized that delivery of peptide to the tumor cells is dictated by adequate local tumor perfusion. To study this relationship, sequential SPECT/CT and MRI were performed to assess the role of vascular functionality in radiopeptide accumulation. Methods: High-resolution SPECT and dynamic contrast-enhanced (DCE)-MRI were acquired in six mice bearing PC295 PDX tumors expressing the gastrin-releasing peptide (GRP) receptor. Two hours prior to SPECT imaging, animals received 25 MBq 111In(DOTA-(βAla)2-JMV594) (25 pmol). Images were acquired using multipinhole SPECT/CT. Directly after SPECT imaging, MR images were acquired on a 7.0-T dedicated animal scanner. DCE-MR images were quantified using semi-quantitative and quantitative models. The DCE-MR and SPECT images were spatially aligned to compute the correlations between radioactivity and DCE-MRI-derived parameters over the tumor. Results: Whereas histology, in vitro autoradiography, and multiple-weighted MRI scans all showed homogenous tissue characteristics, both SPECT and DCE-MRI showed heterogeneous distribution patterns throughout the tumor. The average Spearman’s correlation coefficient between SPECT and DCE-MRI ranged from 0.57 to 0.63 for the “exchange-related” DCE-MRI perfusion parameters. Conclusions: A positive correlation was shown between exchange-related DCE-MRI perfusion parameters and the amount of radioactivity accumulated as measured by SPECT, demonstrating that vascular function was an important aspect of radiopeptide distribution in solid tumors. The combined use of SPECT and MRI added crucial information on the perfusion efficiency versus radiopeptide upt

Feasibility and relevance of discrete vasculature modeling in routine hyperthermia treatment planning

Purpose: To investigate the effect of patient specific vessel cooling on head and neck hyperthermia treatment planning (HTP). Methods and materials: Twelve patients undergoing radiotherapy were scanned using computed tomography (CT), magnetic resonance imaging (MRI) and contrast enhanced MR angiography (CEMRA). 3D patient models were constructed using the CT and MRI data. The arterial vessel tree was constructed from the MRA images using the ‘graph-cut’ method, combining information from Frangi vesselness filtering and region growing, and the results were validated against manually placed markers in/outside the vessels. Patient specific HTP was performed and the change in thermal distribution prediction caused by arterial cooling was evaluated by adding discrete vasculature (DIVA) modeling to the Pennes bioheat equation (PBHE). Results: Inclusion of arterial cooling showed a relevant impact, i.e., DIVA modeling predicts a decreased treatment quality by on average 0.19 °C (T90), 0.32 °C (T50) and 0.35 °C (T20) that is robust against variations in the inflow blood rate (|ΔT| 0.5 °C) were observed. Conclusion: Addition of patient-specific DIVA into the thermal modeling can significantly change predicted treatment quality. In cases where clinically detectable vessels pass the heated region, we advise to perform DIVA modeling