Search

Aim: To investigate if the early treatment effects of radiofrequency ablation (RFA) on renal cell carcinoma (RCC) can be detected with dynamic contrast enhanced (DCE)-MRI and to correlate RCC perfusion with RFA treatment time. Materials and methods: 20 patients undergoing RFA of their 21 RCCs were evaluated with DCE-MRI before and at one month after RFA treatment. Perfusion was estimated using the maximum slope technique at two independent sittings. Total RCC blood flow was correlated with total RFA treatment time, tumour location, size and histology. Results: DCE-MRI examinations were successfully evaluated for 21 RCCs (size from 1.3 to 4 cm). Perfusion of the RCCs decreased significantly (p < 0.0001) from a mean of 203 (-80) mL/min/100 mL before RFA to 8.1 (-3.1) mL/min/100 mL after RFA with low intra-observer variability (r ≥ 0.99, p < 0.0001). There was an excellent correlation (r = 0.95) between time to complete ablation and pre-treatment total RCC blood flow. Tumours with an exophytic location exhibit the lowest mean RFA treatment time. Conclusion: DCE-MRI can detect early treatment effects by measuring RCC perfusion before and after RFA. Perfusion significantly decreases in the zone of ablation, suggesting that it may be useful for the assessment of treatment efficacy. Pre-RFA RCC blood flow may be used to predict RFA treatment time. [ABSTRACT FROM AUTHOR]

Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies. [ABSTRACT FROM AUTHOR]

Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular. [ABSTRACT FROM PUBLISHER]

Aims: To determine the reasons for diagnostic error by virtual slides which allow unsupervised study of diagnosis and error. Methods and results: Software was developed to produce visualizations of the diagnostic track followed by pathologists as they viewed virtual slides. These showed the diagnostic path in four dimensions (x, y, time and zoom), areas studied for >1000 ms, and included pathologists’ comments about the areas viewed. The system was used to study two trainee and two expert pathologists diagnosing 60 Barrett’s oesophageal biopsy specimens. Comparisons of the diagnostic tracks showed the reason for errors. Forty-six cases had an expert consensus diagnosis. The trainees made errors in 21 and 15 cases, respectively, of which 11 and nine were clinically significant. Errors were made across the whole spectrum of diagnoses from negative to intramucosal carcinoma. Detailed examination of the tracks showed that in all errors there was incorrect interpretation of information; in three errors there was an additional failure to identify diagnostic features. Conclusions: Tracking with virtual slides is a useful tool in studying diagnosis and error, which has the potential for use in training and assessment. [ABSTRACT FROM AUTHOR]