Two- and three-dimensional CT measurements of urinary calculi length and width: a comparative study.

BACKGROUND: The standard imaging procedure for a patient presenting with renal colic is unenhanced computed tomography (CT). The CT measured size has a close correlation to the estimated prognosis for spontaneous passage of a ureteral calculus. Size estimations of urinary calculi in CT images are still based on two-dimensional (2D) reformats. PURPOSE: To develop and validate a calculus oriented three-dimensional (3D) method for measuring the length and width of urinary calculi and to compare the calculus oriented measurements of the length and width with corresponding 2D measurements obtained in axial and coronal reformats. MATERIAL AND METHODS: Fifty unenhanced CT examinations demonstrating urinary calculi were included. A 3D symmetric segmentation algorithm was validated against reader size estimations. The calculus oriented size from the segmentation was then compared to the estimated size in axial and coronal 2D reformats. RESULTS: The validation showed 0.1 ± 0.7 mm agreement against reference measure. There was a 0.4 mm median bias for 3D estimated calculus length compared to 2D (P < 0.001), but no significant bias for 3D width compared to 2D. CONCLUSION: The length of a calculus in axial and coronal reformats becomes underestimated compared to 3D if its orientation is not aligned to the image planes. Future studies aiming to correlate calculus size with patient outcome should use a calculus oriented size estimation.

Urinary stone size estimation: a new segmentation algorithm-based CT method.

OBJECTIVES: The size estimation in CT images of an obstructing ureteral calculus is important for the clinical management of a patient presenting with renal colic. The objective of the present study was to develop a reader independent urinary calculus segmentation algorithm using well-known digital image processing steps and to validate the method against size estimations by several readers. METHODS: Fifty clinical CT examinations demonstrating urinary calculi were included. Each calculus was measured independently by 11 readers. The mean value of their size estimations was used as validation data for each calculus. The segmentation algorithm consisted of interpolated zoom, binary thresholding and morphological operations. Ten examinations were used for algorithm optimisation and 40 for validation. Based on the optimisation results three segmentation method candidates were identified. RESULTS: Between the primary segmentation algorithm using cubic spline interpolation and the mean estimation by 11 readers, the bias was 0.0 mm, the standard deviation of the difference 0.26 mm and the Bland-Altman limits of agreement 0.0 ± 0.5 mm. CONCLUSIONS: The validation showed good agreement between the suggested algorithm and the mean estimation by a large number of readers. The limit of agreement was narrower than the inter-reader limit of agreement previously reported for the same data. KEY POINTS: The size of kidney stones is usually estimated manually by the radiologist. An algorithm for computer-aided size estimation is introduced. The variability between readers can be reduced. A reduced variability can give better information for treatment decisions.

Fast GPU based adaptive filtering of 4D echocardiography.

Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where time-resolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of graphics processing unit (GPUs) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (open computing language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 s on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data.