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

[Imaging in nephroureterolithasis]

In the acute diagnostics of a suspected nephroureterolithiasis, ultrasonography should be the examination modality of choice. In cases of suspected urolithiasis, unclear flank pain with fever or in cases of a solitary kidney, a noncontrast computed tomography (CT) scan should always subsequently be performed. If the sonography findings are inconclusive in pregnant women a magnetic resonance imaging (MRI) examination can be considered. If there are indications for urinary diversion, a retrograde imaging study should be performed as part of the urinary diversion. This or CT imaging is also suitable for preinterventional imaging before shock wave lithotripsy, percutaneous nephrolithotomy or ureteroscopy. Postinterventional imaging is not always necessary and sonography is often sufficient. In a conservative treatment approach an abdominal plain X‑ray can be used for follow-up assessment.

[Report template from the German Society of Urology and the German Radiological Society for standardized, structured reporting of native computed tomography scans in the diagnosis of urinary stones]

Standardized structured radiological reporting (SSRB) has been promoted in recent years. The aims of SSRB include that reports be complete, clear, understandable, and stringent. Repetitions or superfluous content should be avoided. In addition, there are advantages in the presentation of chronological sequences, tracking and correlations with structured findings from other disciplines and also the use of artificial intelligence (AI)-based methods. The development of the presented template for SSRB of native computed tomography for urinary stones followed the "process for the creation of quality-assured and consensus-based report templates as well as subsequent continuous quality control and updating" proposed by the German Radiological Society (DRG). This includes several stages of drafts, consensus meetings and further developments. The final version was published on the DRG website ( www.befundung.drg.de ). The template will be checked annually by the steering group and adjusted as necessary. The template contains 6 organ domains (e.g., right kidney) for which entries can be made for a total of 21 different items, mostly with selection windows. If "no evidence of stones" is selected for an organ in the first query, the query automatically jumps to the next organ, so that the processing can be processed very quickly despite the potentially high total number of individual queries for all organs. The German, European, and North American Radiological Societies perceive the establishment of a standardized structured diagnosis of tomographic imaging methods not only in oncological radiology as one of the current central tasks. With the present template for the description of computed tomographic findings for urinary stone diagnostics, we are presenting the first version of a urological template. Further templates for urological diseases are to follow.

Clinical Reproducibility of the Stone Volume Measurement: A "Kidney Stone Calculator" Study

BACKGROUND

An accurate estimation of the stone burden is the key factor for predicting retrograde intra-renal surgical outcomes. Volumetric calculations better stratify stone burden than linear measurements. We developed a free software to assess the stone volume and estimate the lithotrity duration according to 3D-segmented stone volumes, namely the Kidney Stone Calculator (KSC). The present study aimed to validate the KSC's reproducibility in clinical cases evaluating its inter-observer and intra-observer correlations.

METHODS

Fifty patients that harbored renal stones were retrospectively selected from a prospective cohort. For each patient, three urologists with different experience levels in stone management made five measurements of the stone volume on non-contrast-enhanced computed tomography (NCCT) images using the KSC.

RESULTS

the overall inter-observer correlation (Kendall's concordance coefficient) was 0.99 (p < 0.0001). All three paired analyses of the inter-observer reproducibility were superior to 0.8. The intra-observer variation coefficients varied from 4% to 6%, and Kendall's intra-observer concordance coefficient was found to be superior to 0.98 (p < 0.0001) for each participant. Subgroup analyses showed that the segmentation of complex stones seems to be less reproductible.

CONCLUSIONS

The Kidney Stone Calculator is a reliable tool for the stone burden estimation. Its extension for calculating the lithotrity duration is of major interest and could help the practitioner in surgical planning.

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

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.

Imaging in stone diagnosis and surgical planning

PURPOSE OF REVIEW

Radiological imaging techniques and applications are constantly advancing. This review will examine modern imaging techniques in the diagnosis of urolithiasis and applications for surgical planning.

RECENT FINDINGS

The diagnosis of urolithiasis may be done via plain film X-ray, ultrasound (US), or contrast tomography (CT) scan. US should be applied in the workup of flank pain in emergency rooms and may reduce unnecessary radiation exposure. Low dose and ultra-low-dose CT remain the diagnostic standard for most populations but remain underutilized. Single and dual-energy CT provide three-dimensional imaging that can predict stone-specific parameters that help clinicians predict stone passage likelihood, identify ideal management techniques, and possibly reduce complications. Machine learning has been increasingly applied to 3-D imaging to support clinicians in these prognostications and treatment selection.

SUMMARY

The diagnosis and management of urolithiasis are increasingly personalized. Patient and stone characteristics will support clinicians in treatment decision, surgical planning, and counseling.

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

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 (r 2 = 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.

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

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.

Size and volume of kidney stones in computed tomography: Influence of acquisition techniques and image reconstruction parameters

PURPOSE

Computed tomography (CT) is routinely used to assess suspected urolithiasis. Information obtained from CT include presence, location and size of stones, with the latter frequently determining treatment strategy. While there is consensus regarding measurements procedures of kidney stones, influence of radiation dose and reconstruction techniques on stone measurements are unknown. The purpose of this study was to systematically evaluate the influence of these technical determinants on kidney stone size measurements.

METHOD

47 kidney stones of different composition were scanned using a 64-row-multi-detector CT in a 3D-printed, semi-anthropomorphic phantom. Reference stone sizes were measured manually with a digital caliper (Man-M). Stones were imaged with 2 and 10 mGy CTDI. Images were reconstructed using filtered-back-projection, hybrid-iterative and model-based-iterative reconstruction algorithms (FBP, HIR, MBIR) in combination with different kernels and denoising levels. All stones underwent semi-automatic, threshold-based segmentation for computation of maximum diameter and volume. Statistics were conducted using ANOVA ± correction for multiple comparisons.

RESULTS

Overall stone size as compared to manual measurements was overestimated in CT (10.0 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05) yet showing a good correlation (R² = 0.66). Radiation dose and denoising levels did not significantly influence measurements (p > 0.05). MBIR and sharp kernels showed closest agreement with Man-M (9.3 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05). Differences within single stones were as high as 40 % (e.g. Man-M: 5.9 mm, CT: 7.3-12.0 mm).

CONCLUSIONS

CT-based measurements of kidney stone size appear unaffected by radiation dose and denoising technique, whereas reconstruction algorithms and kernels demonstrate a relevant impact on size measurements. Smallest differences were found using MBIR with a sharp kernel.