In-vitro comparison of different slice thicknesses and kernel settings for measurement of urinary stone size by computed tomography

Umbach, Roland; Müller, J. A.; Wendt‐Nordahl, Gunnar; Knoll, Thomas; Jessen, Jan Peter

doi:10.1007/s00240-019-01109-1

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…However, the detectability of stones is usually higher when using sharp kernels and bone window, since the high attenuation of stones in these settings is more prominent compared to soft tissue settings. Umbach et al and Danilovic et al proposed using bone window and small slice thickness to determine the stone size due to a higher accuracy compared to measurements in soft tissue window settings [ 32 , 33 ]. However, they did not provide information on the employed reconstruction filter.…”

Section: Discussionmentioning

confidence: 99%

Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones

et al. 2022

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Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenuation, and objective image quality of reno-ureteric stones denoised using DL-software in comparison to traditionally reconstructed low-dose abdominal CT-images and evaluated its clinical impact. In this institutional review-board-approved retrospective study, 45 patients with renal and/or ureteral stones were included. All patients had undergone abdominal CT between August 2019 and October 2019. CT-images were reconstructed using the following three methods: filtered back-projection, iterative reconstruction, and PixelShine (DL-software) with both sharp and soft kernels. Stone size, CT attenuation, and objective image quality (signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) were evaluated and compared using Bonferroni-corrected Friedman tests. Objective image quality was measured in six regions-of-interest. Stone size ranged between 4.4 × 3.1–4.4 × 3.2 mm (sharp kernel) and 5.1 × 3.8–5.6 × 4.2 mm (soft kernel). Mean attenuation ranged between 704–717 Hounsfield Units (HU) (soft kernel) and 915–1047 HU (sharp kernel). Differences in measured stone sizes were ≤1.3 mm. DL-processed images resulted in significantly higher CNR and SNR values (p < 0.001) by decreasing image noise significantly (p < 0.001). DL-software significantly improved objective image quality while maintaining both correct stone size and CT-attenuation values. Therefore, the clinical impact of stone assessment in denoised image data sets remains unchanged. Through the relevant noise suppression, the software additionally offers the potential to further reduce radiation exposure.

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Section: Discussionmentioning

confidence: 99%

Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones

et al. 2022

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“…On the other hand, it appears surprising that slice thickness of axial reformatations did not significantly impact size measurements in our dataset, since it was previously reported that smaller slice thicknesses resulted in more accurate and less variable stone size measurements as well as more accurate volume measurements 12 , 22 , 25 . Last but not least, our results are in line with few earlier studies showing more accurate and less variable results using a bone window setting over a soft-tissue window setting 12 , 13 .…”

Section: Discussionmentioning

confidence: 58%

“…Aside from the retrospective study design, some limitations of this study need to be discussed. First, we only included a limited number of kidney stones which became necessary to the required amount of conducted measurements; yet, the sample size is comparable to earlier investigations and considered to be sufficient with regards to stone composition, shape and size 1 , 12 , 13 , 22 . Second, we adapted radiation dose from previous in-vivo and ex-vivo studies as well as from our institutional low-dose protocol for unenhanced urolithiasis CT 10 , 27 , 30 .…”

Section: Discussionmentioning

confidence: 99%

Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings

Reimer

Klein

Rinneburger

et al. 2021

Sci Rep

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Computed tomography in suspected urolithiasis provides information about the presence, location and size of stones. Particularly stone size is a key parameter in treatment decision; however, data on impact of reformatation and measurement strategies is sparse. This study aimed to investigate the influence of different image reformatations, slice thicknesses and window settings on stone size measurements. Reference stone sizes of 47 kidney stones representative for clinically encountered compositions were measured manually using a digital caliper (Man-M). Afterwards stones were placed in a 3D-printed, semi-anthropomorphic phantom, and scanned using a low dose protocol (CTDIvol 2 mGy). Images were reconstructed using hybrid-iterative and model-based iterative reconstruction algorithms (HIR, MBIR) with different slice thicknesses. Two independent readers measured largest stone diameter on axial (2 mm and 5 mm) and multiplanar reformatations (based upon 0.67 mm reconstructions) using different window settings (soft-tissue and bone). Statistics were conducted using ANOVA ± correction for multiple comparisons. Overall stone size in CT was underestimated compared to Man-M (8.8 ± 2.9 vs. 7.7 ± 2.7 mm, p < 0.05), yet closely correlated (r = 0.70). Reconstruction algorithm and slice thickness did not significantly impact measurements (p > 0.05), while image reformatations and window settings did (p < 0.05). CT measurements using multiplanar reformatation with a bone window setting showed closest agreement with Man-M (8.7 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05, r = 0.83). Manual CT-based stone size measurements are most accurate using multiplanar image reformatation with a bone window setting, while measurements on axial planes with different slice thicknesses underestimate true stone size. Therefore, this procedure is recommended when impacting treatment decision.

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“…This decision was informed by a number of studies from 2000 and onward that have shown that thicker slices lead to less accurate size measurements due to the partial volume effect. 29,[36][37][38] In studies with ground truth stones embedded in phantoms, larger slice thicknesses lead to a decrease in both the measured size and maximum intensity of stones. 36,37 Additionally, slice thicknesses greater than 3 mm can lead to small stones (<3 mm diameter) being missed.…”

Section: Methodsmentioning

confidence: 99%

“…We use the original scan thickness 1.0 mm for the NNMC‐CTC dataset and 1.0–1.25 mm for the UW‐CTC dataset, even though resampling to thicker slices would reduce image noise. This decision was informed by a number of studies from 2000 and onward that have shown that thicker slices lead to less accurate size measurements due to the partial volume effect 29,36–38 . In studies with ground truth stones embedded in phantoms, larger slice thicknesses lead to a decrease in both the measured size and maximum intensity of stones 36,37 .…”

Section: Methodsmentioning

confidence: 99%

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans

et al. 2022

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Purpose Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning‐based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low‐dose CT or tested on a large‐scale external dataset. Methods We used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient‐level labels were used as an external validation set. A 3D U‐Net model was employed to segment the kidneys, followed by gradient‐based anisotropic denoising, thresholding, and region growing. A 13 layer convolutional neural network classifier was then applied to distinguish kidney stones from false positive regions. Results The system achieved a sensitivity of 0.86 at 0.5 false positives per scan on a challenging test set of low‐dose CT with many small stones, an improvement over an earlier work that obtained a sensitivity of 0.52. The stone volume measurements correlated well with manual measurements (r2=0.95$r^2 = 0.95$). For patient‐level classification, the system achieved an area under the receiver‐operating characteristic of 0.95 on an external validation set (sensitivity = 0.88, specificity = 0.91 at the Youden point). A common cause of false positives were small atherosclerotic plaques in the renal sinus that simulated kidney stones. Conclusions Our deep‐learning‐based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation set.

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In-vitro comparison of different slice thicknesses and kernel settings for measurement of urinary stone size by computed tomography

Cited by 11 publications

References 18 publications

Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones

Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones

Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans

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