Quantitative SPECT/CT imaging of actinium-225 for targeted alpha therapy of glioblastomas.

BACKGROUND: A new, alternative option for patients with recurrent glioblastoma is targeted alpha therapy (TAT), in the form of a local administration of substance P (neurokinin type 1 receptor ligand, NK-1) labelled with 225Ac. The purpose of the study was to confirm the feasibility of quantitative SPECT imaging of 225Ac, in a model reproducing specific conditions of TAT. In particular, to present the SPECT calibration methodology used, as well as the results of validation measurements and their accuracy. Additionally, to discuss the specific problems related to high noise in the presented case. MATERIALS AND METHODS: All SPECT/CT scans were conducted using the Symbia T6 equipped with HE collimators, and acquired with multiple energy windows (three main windows: 440 keV, 218 keV, and 78 keV, with three lower scatter energy windows). A Jaszczak phantom with fillable cylindrical sources of various sizes was used to investigate quantitative SPECT/CT imaging characteristics. The planar sensitivity of the camera, an imaging calibration factor, and recovery coefficients were determined. Additionally, the 3D printed model of the glioblastoma tumour was developed and imaged to evaluate the accuracy of the proposed protocol. RESULTS: Using the imaging calibration factor and recovery coefficients obtained with the Jaszczak phantom, we were able to quantify the activity in a 3D-printed model of a glioblastoma tumour with uncertainty of no more than 10% and satisfying accuracy. CONCLUSIONS: It is feasible to perform quantitative 225Ac SPECT/CT imaging. However, there are still many more challenges that should be considered for further research on this topic (among others: accurate determination of ICF in the case of high background noise, better method of background estimation for recovery coefficient calculations, other methods for scatter correction than the dual-energy window scatter-compensation method used in this study).

Use of superpixels for improvement of inter-rater and intra-rater reliability during annotation of medical images.

In the context of automatic medical image segmentation based on statistical learning, raters' variability of ground truth segmentations in training datasets is a widely recognized issue. Indeed, the reference information is provided by experts but bias due to their knowledge may affect the quality of the ground truth data, thus hindering creation of robust and reliable datasets employed in segmentation, classification or detection tasks. In such a framework, automatic medical image segmentation would significantly benefit from utilizing some form of presegmentation during training data preparation process, which could lower the impact of experts' knowledge and reduce time-consuming labeling efforts. The present manuscript proposes a superpixels-driven procedure for annotating medical images. Three different superpixeling methods with two different number of superpixels were evaluated on three different medical segmentation tasks and compared with manual annotations. Within the superpixels-based annotation procedure medical experts interactively select superpixels of interest, apply manual corrections, when necessary, and then the accuracy of the annotations, the time needed to prepare them, and the number of manual corrections are assessed. In this study, it is proven that the proposed procedure reduces inter- and intra-rater variability leading to more reliable annotations datasets which, in turn, may be beneficial for the development of more robust classification or segmentation models. In addition, the proposed approach reduces time needed to prepare the annotations.

Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study.

OBJECTIVES: This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer. METHODS: The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms. RESULTS: The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05). CONCLUSION: Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

Fully automated 3D body composition analysis and its association with overall survival in head and neck squamous cell carcinoma patients.

Objectives: We developed a method for a fully automated deep-learning segmentation of tissues to investigate if 3D body composition measurements are significant for survival of Head and Neck Squamous Cell Carcinoma (HNSCC) patients. Methods: 3D segmentation of tissues including spine, spine muscles, abdominal muscles, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and internal organs within volumetric region limited by L1 and L5 levels was accomplished using deep convolutional segmentation architecture - U-net implemented in a nnUnet framework. It was trained on separate dataset of 560 single-channel CT slices and used for 3D segmentation of pre-radiotherapy (Pre-RT) and post-radiotherapy (Post-RT) whole body PET/CT or abdominal CT scans of 215 HNSCC patients. Percentages of tissues were used for overall survival analysis using Cox proportional hazard (PH) model. Results: Our deep learning model successfully segmented all mentioned tissues with Dice's coefficient exceeding 0.95. The 3D measurements including difference between Pre-RT and post-RT abdomen and spine muscles percentage, difference between Pre-RT and post-RT VAT percentage and sum of Pre-RT abdomen and spine muscles percentage together with BMI and Cancer Site were selected and significant at the level of 5% for the overall survival. Aside from Cancer Site, the lowest hazard ratio (HR) value (HR, 0.7527; 95% CI, 0.6487-0.8735; p = 0.000183) was observed for the difference between Pre-RT and post-RT abdomen and spine muscles percentage. Conclusion: Fully automated 3D quantitative measurements of body composition are significant for overall survival in Head and Neck Squamous Cell Carcinoma patients.

Performance of Fully Automated Algorithm Detecting Bone Marrow Edema in Sacroiliac Joints.

This study evaluates the performance of a fully automated algorithm to detect active inflammation in the form of bone marrow edema (BME) in iliac and sacral bones, depending on the quality of the coronal oblique plane in patients with axial spondyloarthritis (axSpA). The results were assessed based on the technical correctness of MRI examination of the sacroiliac joints (SIJs). A total of 173 patients with suspected axSpA were included in the study. In order to verify the correctness of the MRI, a deviation angle was measured on the slice acquired in the sagittal plane in the T2-weighted sequence. This angle was located between the line drawn between the posterior edges of S1 and S2 vertebrae and the line that marks the actual plane in which the slices were acquired in T1 and STIR sequences. All examinations were divided into quartiles according to the deviation angle measured in degrees as follows: 1st group [0; 2.2], 2nd group (2.2; 5.7], 3rd group (5.7; 10] and 4th group (10; 29.2]. Segmentations of the sacral and iliac bones were acquired manually and automatically using the fully automated algorithm on the T1 sequence. The Dice coefficient for automated bone segmentations with respect to reference manual segmentations was 0.9820 (95% CI [0.9804, 0.9835]). Examinations of BME lesions were assessed using the SPARCC scale (in 68 cases SPARCC > 0). Manual and automatic segmentations of the lesions were performed on STIR sequences and compared. The sensitivity of detection of BME ranged from 0.58 (group 1) to 0.83 (group 2) versus 0.76 (total), while the specificity was equal to 0.97 in each group. The study indicates that the performance of the algorithm is satisfactory regardless of the deviation angle.

Deep Learning Algorithm for Differentiating Patients with a Healthy Liver from Patients with Liver Lesions Based on MR Images.

The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver.

Measurement Systems for Use in the Navigation of the Cannula-Guide Assembly within the Deep Regions of the Bronchial Tree.

BACKGROUND: The purpose of this paper is to present the spatial navigation system prototype for localizing the distal tip of the cannula-guide assembly. This assembly is shifted through the channel of a bronchoscope, which is fixed in relation to the patient. The navigation is carried out in the bronchial tree, based on maneuvers of the aforementioned assembly. METHODS: The system consists of three devices mounted on the guide handle and at the entrance to the bronchoscope working channel. The devices record the following values: cannula displacement, rotation of the guide handle, and displacement of the handle ring associated with the bending of the distal tip of the guide. RESULTS: In laboratory experiments, we demonstrate that the cannula displacement can be monitored with an accuracy of 2 mm, and the angles of rotation and bending of the guide tip with an accuracy of 10 and 20 degrees, respectively, which outperforms the accuracy of currently used methods of bronchoscopy support. CONCLUSIONS: This accuracy is crucial to ensure that we collect the material for histopathological examination from a precisely defined place. It makes it possible to reach cancer cells at their very early stage.

Benchmarking of Deep Architectures for Segmentation of Medical Images.

In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using identical conditions to disentangle the influence of model architecture, model training, and parameter settings on the performance of a trained model. For this purpose each of these six segmentation architectures is trained on the same nine data sets. The data sets are selected to cover various imaging modalities (X-rays, computed tomography, magnetic resonance imaging), single- and multi-class segmentation problems, and single- and multi-modal inputs. During the training, it is ensured that the data preprocessing, data set split into training, validation, and testing subsets, optimizer, learning rate change strategy, architecture depth, loss function, supervision and inference are exactly the same for all the architectures compared. Performance is evaluated in terms of Dice coefficient, surface Dice coefficient, average surface distance, Hausdorff distance, training, and prediction time. The main contribution of this experimental study is demonstrating that the architecture variants do not improve the quality of inference related to the basic U-Net architecture while resource demand rises.

Isodoses-a set theory-based patient-specific QA measure to compare planned and delivered isodose distributions in photon radiotherapy.

BACKGROUND: The gamma index and dose-volume histogram (DVH)-based patient-specific quality assurance (QA) measures commonly applied in radiotherapy planning are unable to simultaneously deliver detailed locations and magnitudes of discrepancy between isodoses of planned and delivered dose distributions. By exploiting statistical classification performance measures such as sensitivity or specificity, compliance between a planned and delivered isodose may be evaluated locally, both for organs-at-risk (OAR) and the planning target volume (PTV), at any specified isodose level. Thus, a patient-specific QA tool may be developed to supplement those presently available in clinical radiotherapy. MATERIALS AND METHODS: A method was developed to locally establish and report dose delivery errors in three-dimensional (3D) isodoses of planned (reference) and delivered (evaluated) dose distributions simultaneously as a function the dose level and of spatial location. At any given isodose level, the total volume of delivered dose containing the reference and the evaluated isodoses is locally decomposed into four subregions: true positive-subregions within both reference and evaluated isodoses, true negative-outside of both of these isodoses, false positive-inside the evaluated isodose but not the reference isodose, and false negatives-inside the reference isodose but not the evaluated isodose. Such subregions may be established over the whole volume of delivered dose. This decomposition allows the construction of a confusion matrix and calculation of various indices to quantify the discrepancies between the selected planned and delivered isodose distributions, over the complete range of values of dose delivered. The 3D projection and visualization of the spatial distribution of these discrepancies facilitates the application of the developed method in clinical practice. RESULTS: Several clinical photon radiotherapy plans were analyzed using the developed method. In some plans at certain isodose levels, dose delivery errors were found at anatomically significant locations. These errors were not otherwise highlighted-neither by gamma analysis nor by DVH-based QA measures. A specially developed 3D projection tool to visualize the spatial distribution of such errors against anatomical features of the patient aids in the proposed analysis of therapy plans. CONCLUSIONS: The proposed method is able to spatially locate delivery errors at selected isodose levels and may supplement the presently applied gamma analysis and DVH-based QA measures in patient-specific radiotherapy planning.

DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo software.

BACKGROUND: Any Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any single accelerator are unique and generally unknown, an appropriate model of an electron beam must be assumed before MC simulations can be run. The purpose of the present study is to develop a flexible framework with suitable regression models for estimating parameters of the model of primary electron beam in simulators of medical linear accelerators using real reference dose profiles measured in a water phantom. METHODS: All simulations were run using PRIMO MC simulator. Two regression models for estimating the parameters of the simulated primary electron beam, both based on machine learning, were developed. The first model applies Principal Component Analysis to measured dose profiles in order to extract principal features of the shapes of the these profiles. The PCA-obtained features are then used by Support Vector Regressors to estimate the parameters of the model of the electron beam. The second model, based on deep learning, consists of a set of encoders processing measured dose profiles, followed by a sequence of fully connected layers acting together, which solve the regression problem of estimating values of the electron beam parameters directly from the measured dose profiles. Results of the regression are then used to reconstruct the dose profiles based on the PCA model. Agreement between the measured and reconstructed profiles can be further improved by an optimization procedure resulting in the final estimates of the parameters of the model of the primary electron beam. These final estimates are then used to determine dose profiles in MC simulations. RESULTS: Analysed were a set of actually measured (real) dose profiles of 6 MV beams from a real Varian 2300 C/D accelerator, a set of simulated training profiles, and a separate set of simulated testing profiles, both generated for a range of parameters of the primary electron beam of the Varian 2300 C/D PRIMO simulator. Application of the two-stage procedure based on regression followed by reconstruction-based minimization of the difference between measured (real) and reconstructed profiles resulted in achieving consistent estimates of electron beam parameters and in a very good agreement between the measured and simulated photon beam profiles. CONCLUSIONS: The proposed framework is a readily applicable and customizable tool which may be applied in tuning virtual primary electron beams of Monte Carlo simulators of linear accelerators. The codes, training and test data, together with readout procedures, are freely available at the site: https://github.com/taborzbislaw/DeepBeam .

Statistical approach to the selection of the tolerances for distance to agreement improves the quality control of the dose delivery in radiotherapy.

In the study, a local approach to setting reference tolerance values for the distance-to-agreement (DTA) component of the gamma index is proposed. The reference tolerance values are calculated in simulations, following a dose delivery model presented in a previous work. An analytical model for determining the quantiles of DTA distribution is also proposed and verified. It is shown that the distributions of DTA values normalized with either quantiles or standard deviation of DTA distributions are universal over analyzed plans and points within a single plan. This enables statistically sound inference about the quality of dose delivery. In particular, based on the normalized distributions the comparison of planned and delivered doses can be formulated within the framework of statistical inference as a problem of multiple statistical testing. For every evaluated point P of a plan, one may formulate and test a null hypothesis that there is no delivery error against an alternative hypothesis that there is a delivery error in P. It is also shown that the proposed approach is more sensitive than the current standard approach to shift errors in high dose gradient regions.

The semi-automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis.

The aim of the study was to create the efficient tool for semi-automated detection of bone marrow oedema lesions in patients with axial spondyloarthritis (axSpA). MRI examinations of 22 sacroiliac joints of patients with confirmed axSpA-related sacroiliitis (median SPARCC score: 14 points) were included into the study. Design of our algorithm is based on Maksymowych et al. evaluation method and consists of the following steps: manual segmentation of bones (T1W sequence), automated detection of reference signal region, sacroiliac joint central lines and ROIs, a division of ROIs into quadrants, automated detection of inflammatory changes (STIR sequence). As a gold standard, two sets of manual lesion delineations were created. Two approaches to the performance assessment of lesion detection were considered: pixel-wise (detections compared pixel by pixel) and quadrant-wise (quadrant to quadrant). Statistical analysis was performed using Spearman's correlation coefficient. Correlation coefficient obtained for pixel-wise comparison of semi-automated and manual detections was 0.87 (p = 0.001), while for quadrant-wise analysis was 0.83 (p = 0.001). The correlation between two sets of manual detections was 0.91 for pixel-wise comparison (p = 0.001) and 0.88 (p = 0.001) for quadrant-wise approach. Spearman's correlation between two manual assessments was not statistically different from the correlation between semi-automated and manual evaluations, both for pixel- (p = 0.14) and quadrant-wise (p = 0.17) analysis. Average single slice processing time: 0.64 ± 0.30 s. Our method allows for objective detection of bone marrow oedema lesions in patients with axSpA. The quantification of affected pixels and quadrants has comparable reliability to manual assessment.

A simulation-based method for evaluating geometric tests of a linac c-arm in quality control in radiotherapy.

PURPOSE: Assessment of the accuracy of geometric tests of a linac used in external beam therapy is crucial for ensuring precise dose delivery. In this paper, a new simulation-based method for assessing accuracy of such geometric tests is proposed and evaluated on a set of testing procedures. METHODS: Linac geometry testing methods used in this study are based on an established design of a two-module phantom. Electronic portal imaging device (EPID) images of fiducial balls contained in these modules can be used to automatically reconstruct linac geometry. The projection of the phantom modules fiducial balls onto the EPID detector plane is simulated for assumed nominal geometry of a linac. Then, random errors are added to the coordinates of the projections of the centers of the fiducial balls and the linac geometry is reconstructed from these data. RESULTS: Reconstruction is performed for a set of geometric test designs and it is shown how the dispersion of the reconstructed values of geometric parameters depends on the design of a geometric test. Assuming realistic accuracy of EPID image analysis, it is shown that for selected testing plans the reconstruction accuracy of geometric parameters can be significantly better than commonly used action thresholds for these parameters. CONCLUSIONS: Proposed solution has the potential to improve geometric testing design and practice. It is an important part of a fully automated geometric testing solution.

Use of statistical approaches to improve the quality control of the dose delivery in radiotherapy.

The gamma index is a measure used routinely for the quality control of dose delivery in radiotherapy, implemented in commercial systems for the verification of treatment plans. It involves comparison of the difference between planned and delivered doses to a single reference. The same reference value is selected for all points in the plan that can potentially hide dose delivery errors, especially in medium and low dose areas. In this study, a receiver operating characteristic analysis is used to demonstrate the limits of the performance of the global gamma index as a method for detecting dose delivery errors. The performance of a global gamma index is compared with two approaches based on statistical tests for outlier detection. Two statistical approaches are considered: according to the first, the distribution of the delivered doses is estimated based on an appropriate calibration procedure. According to the second, the distribution of the delivered doses is estimated based on the detection of relatively homogeneous regions of a plan and analyzing the distributions of planned doses within these regions. The performance of the three approaches is compared based on analytical considerations and in simulations in which errors are intentionally introduced to the plan delivery and noise related to dose delivery is modeled. We have shown that a statistics-based approach to gamma analysis generally leads to better detection of true delivery errors. The results of analytical consideration coincide with the simulations. In simulations, we observe that both statistical approaches are better detectors of true delivery errors than the global method for the gamma-index passing rate in the range from 0.9-1.0. It is shown that the global gamma index is a weak detector of dose delivery errors, which in some circumstances behaves only slightly better than a purely random classifier.

The impact of polymers on 3D microstructure and controlled release of sildenafil citrate from hydrophilic matrices.

Sildenafil citrate has short biological half-life in humans. Thus, matrix tablets of controlled release were designed and prepared by compaction on the basis of hydrophilic polymers, i.e. HPMC, sodium alginate, carbomer, poloxamer and their mixtures. The impact of these polymers on sildenafil release in vitro and its pharmacokinetics in vivo was evaluated. Since drug release rate from hydrophilic matrices can be govern by the porosity of the matrix, the microstructure of tablets was studied using X-ray microcomputed tomography. 3D network of either open (percolating) or closed (non-percolating) pores was reconstructed. The tortuosity and the diameter of both kinds of pores were determined. Their spatial distribution within the matrix was analyzed in linear and radial direction. Polymer-dependent characteristics of the open pores (Ø > 2 µm) architecture was shown. The release profiles of sildenafil from matrix tablets fitted to Korsmeyer-Peppas model (r2: 0.9331-0.9993) with either Fickian diffusion or anomalous transport involved. Mean dissolution time (MDT) from tablets made of HPMC, carbomer or a mixture of HPMC and sodium alginate (2:1) was ca. 100 min, which was more than twelve times longer as compared to matrices prepared of silicified microcrystalline cellulose (MDT = 8 min). MDT correlated with the number of the open pores (Pearson's r = 0.94). Sustained release of sildenafil from ground carbomer tablets reflected in the slow absorption of the drug (tmax = 5.0 ±â€¯1.2 h) in vivo and the relative bioavailability of 151%. Interestingly, the relative bioavailability of sildenafil from binary matrices composed of HPMC and sodium alginate (2:1) was almost four times higher than that of sildenafil alone.

A generic multimodule phantom for testing geometry of a linac c-arm as a part of quality control in radiotherapy.

PURPOSE: To develop an assumption-free methodology for testing geometry of linacs. METHODS: The problem of projecting a fiducial positioned in a predefined point in a 3D space and attached rigidly to a treatment table of a radiotherapeutic device onto an imaging plane with unknown characteristics from a source with unknown coordinates is formulated. The problem of determining these unknowns is formulated as an optimization problem. The problem of determining the gantry/the collimator rotation axis and angle from projection of additional fiducials is also formulated and solved. Analytical methodology is developed for determining isocenter position and an error of estimating isocenter position. The developed methodology is tested in simulations. RESULTS: Very good agreement between preset and calculated values of quantities of interest was found in the simulations. In all cases, the proposed schemes enabled determination of the geometric characteristics of a radiotherapeutic device with accuracy better than one hundredth of a millimeter and one hundredth of a degree. CONCLUSIONS: A concept of a multimodule multifiducial phantom has been introduced. Analytical framework has been developed to extract geometric characteristics of radiotherapy devices from projection images of a phantom. The phantom design and the methodology developed have been tested in simulations.

In vitro and in vivo behavior of ground tadalafil hot-melt extrudates: How the carrier material can effectively assure rapid or controlled drug release.

Different types of ground hot-melt extrudates loaded with 10, 20 or 30 % of the poorly water-soluble drug tadalafil were prepared and characterized in vitro and in vivo (in rats). Soluplus was used as an amorphous carrier material, whereas mannitol and lactitol were studied as crystalline matrix formers. The systems were characterized using X-ray powder diffraction, thermogravimetric analysis coupled with quadruple mass spectrometry, differential scanning calorimetry, X-ray computed microtomography, in vitro drug release measurements and monitoring of drug plasma levels upon oral administration to rats. The pure drug substance and physical mixtures of tadalafil with the carrier materials were used as references. Importantly, the bioavailability of this poorly water-soluble drug could be substantially increased with the proposed formulations, and the in vitro and in vivo release rates could be effectively adjusted by choosing the appropriate type of carrier material: Whereas mannitol-based ground hot-melt extrudates rapidly released the drug and led to an early rise in drug plasma concentrations, Soluplus-based systems released tadalafil more slowly, resulting in delayed plasma peaks. These behaviors could be explained by the rapid disintegration/dissolution of the porous mannitol-based formulations, whereas Soluplus significantly swelled and the dissolved drug had to diffuse through the polymeric network prior to release. Blending these formulations can be expected to allow providing elevated drug concentrations in vivo during prolonged periods of time upon one single administration with a rapid onset of drug action.

Automated measurement of parameters related to the deformities of lower limbs based on x-rays images.

Measurement of the deformation of the lower limbs in the current standard full-limb X-rays images presents significant challenges to radiologists and orthopedists. The precision of these measurements is deteriorated because of inexact positioning of the leg during image acquisition, problems with selecting reliable anatomical landmarks in projective X-ray images, and inevitable errors of manual measurements. The influence of the random errors resulting from the last two factors on the precision of the measurement can be reduced if an automated measurement method is used instead of a manual one. In the paper a framework for an automated measurement of various metric and angular quantities used in the description of the lower extremity deformation in full-limb frontal X-ray images is described. The results of automated measurements are compared with manual measurements. These results demonstrate that an automated method can be a valuable alternative to the manual measurements.

Automated assessment of synovitis in 0.2T magnetic resonance images of the wrist.

According to the current recommendations in diagnosis of rheumatoid arthritis (RA), Magnetic Resonance (MR) images of wrist joints are used to evaluate three main types of lesions: synovitis, bone edema and bone erosions. In the clinical practice, the RA-related lesions seen in MR images are assessed manually with the semi-quantitative RAMRIS scoring system. In this paper we present an automated method for inflamed synovial membrane volume determination, based on the analysis of pre- and post-contrast MR images and segmentation of wrist bones seen in MR images. We found that the correlation between the automatically quantified volume of synovitis and RAMRIS scores was in the range from 0.76 to 0.87 for the total RAMRIS synovitis score. This can be compared with the correlation between the manually quantified volume of synovitis and RAMRIS scores which was in the range from 0.75 to 0.81 for the total synovitis score. The results of the study demonstrate that computer assisted methods for assessment of synovitis have great potential for clinical applications.