Advances in CT Imaging for Urolithiasis

Overview

Journal Indian J Urol

Specialty Urology

Date 2015 Jul 14

PMID 26166961

Citations 20

Authors

Yasir Andrabi

Manuel Patino

Chandan J Das

Brian Eisner

Dushyant V Sahani

Avinash Kambadakone

Affiliations

Soon will be listed here.

Abstract

Urolithiasis is a common disease with increasing prevalence worldwide and a lifetime-estimated recurrence risk of over 50%. Imaging plays a critical role in the initial diagnosis, follow-up and urological management of urinary tract stone disease. Unenhanced helical computed tomography (CT) is highly sensitive (>95%) and specific (>96%) in the diagnosis of urolithiasis and is the imaging investigation of choice for the initial assessment of patients with suspected urolithiasis. The emergence of multi-detector CT (MDCT) and technological innovations in CT such as dual-energy CT (DECT) has widened the scope of MDCT in the stone disease management from initial diagnosis to encompass treatment planning and monitoring of treatment success. DECT has been shown to enhance pre-treatment characterization of stone composition in comparison with conventional MDCT and is being increasingly used. Although CT-related radiation dose exposure remains a valid concern, the use of low-dose MDCT protocols and integration of newer iterative reconstruction algorithms into routine CT practice has resulted in a substantial decrease in ionizing radiation exposure. In this review article, our intent is to discuss the role of MDCT in the diagnosis and post-treatment evaluation of urolithiasis and review the impact of emerging CT technologies such as dual energy in clinical practice.

Citing Articles

Improved display and detection of small renal stones using photon-counting detector CT compared to conventional energy-integrating detector CT.

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PMID: 39865183 DOI: 10.1007/s00261-024-04781-z.

Are renal stone protocol computed tomography reports giving us enough information?.

Bayley C, Hogarth D, McLarty R, De S, Schuler T Can Urol Assoc J. 2024; 18(10):310-315.

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Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.

Drews M, Demircioglu A, Neuhoff J, Haubold J, Zensen S, Opitz M Diagnostics (Basel). 2024; 14(6).

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Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.

Choi H, Kim J, Whangbo T, Eun S Int Neurourol J. 2023; 27(Suppl 2):S99-103.

PMID: 38048824 PMC: 10715832. DOI: 10.5213/inj.2346292.146.

Deep-Learning-Based Image Denoising in Imaging of Urolithiasis: Assessment of Image Quality and Comparison to State-of-the-Art Iterative Reconstructions.

Terzis R, Reimer R, Nelles C, Celik E, Caldeira L, Heidenreich A Diagnostics (Basel). 2023; 13(17).

PMID: 37685359 PMC: 10486912. DOI: 10.3390/diagnostics13172821.

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