Multidetector CT urography: techniques, clinical applications, and pitfalls

Akbar, Syed Ali; Mortelé, Koenraad J.; Baeyens, Kathy; Kekelidze, Maka; Silverman, Stuart G.

doi:10.1053/j.sult.2003.11.002

Cited by 52 publications

(21 citation statements)

References 44 publications

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“…1 Multidetector row CT (MDCT) urography is the imaging modality of choice for tracking urinary track abnormalities, as a single exam can be used to evaluate the kidneys, intrarenal collecting systems, and ureters. [2][3][4][5][6] Interpretation of a CT urography (CTU) study, however, requires extensive time. On average, 300 slices are generated for each CTU scan (range: 200-600 slices), and the radiologist interpreting the study has to visually determine if lesions are present within the urinary tracts.…”

Section: Introductionmentioning

confidence: 99%

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

et al. 2016

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Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. C 2016 American Association of Physicists in Medicine. [http://dx

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Section: Introductionmentioning

confidence: 99%

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

et al. 2016

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“…Far from it, dilution of the excreted contrast medium may reduce streak artefacts, thereby increasing diagnostic confidence. Furthermore, oral hydration reduces the reabsorption of water in the tubuli and, therefore, results in stronger diluting of the contrast material and reducing its potential nephrotoxicity [1,15].…”

Section: Discussionmentioning

confidence: 99%

Multi-detector CT urography: effect of oral hydration and contrast medium volume on renal parenchymal enhancement and urinary tract opacification—a quantitative and qualitative analysis

2010

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“…Representative CTU protocols contain either a three-phase acquisition with a single-bolus injection or a two-phase acquisition with a two bolus-injection (split-bolus). [6][7][8][9][10] Our scan protocol consists of an unenhanced scan (scan 1), followed by a split-bolus phase (scan 2), as advocated by different study groups. [4,[11][12][13] Scan 2 was followed by supportive maneuvers and a late phase CT (scan 3).…”

Section: Discussionmentioning

confidence: 99%

CT-Urography: Comparison of different methods for increasing the intra-abdominal pressure

Hage

Boll²,

Brantner³

et al. 2018

IJDI

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Objective: Investigating the influence of increasing the intraabdominal pressure by Valsalva maneuvers and/or abdominal compression to reach best visualization of the entire urinary tract in computed tomography urography (CTU). Methods: After split-bolus technique, Valsalva maneuvers, compression or a combination of both were applied in 60 patients before late phase images were acquired. The degree of opacification and distension of three segments of the urinary tract were evaluated. Results: After split-bolus CT no significant difference among the groups regarding distention and opacification. A significant increase in distension and opacification was found during Valsalva and the combination of Valsalva and compression. Conclusions: A CTU protocol including a late phase scan with prior application of compression, Valsalva maneuvers or a combination of both showed beneficial effects for the distension and opacification, a significant difference was found by taking location as a covariate.

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Multidetector CT urography: techniques, clinical applications, and pitfalls

Cited by 52 publications

References 44 publications

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

Multi-detector CT urography: effect of oral hydration and contrast medium volume on renal parenchymal enhancement and urinary tract opacification—a quantitative and qualitative analysis

CT-Urography: Comparison of different methods for increasing the intra-abdominal pressure

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