nach oben

Erschienen in:

04.10.2021 | Kidneys, Ureters, Bladder, Retroperitoneum

Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms

verfasst von: Samjhana Thapaliya, Samuel L. Brady, Elanchezhian Somasundaram, Christopher G. Anton, Brian D. Coley, Alexander J. Towbin, Bin Zhang, Jonathan R. Dillman, Andrew T. Trout

Erschienen in: Abdominal Radiology | Ausgabe 1/2022

Einloggen, um Zugang zu erhalten

Abstract

Background

Deep learning Computed Tomography (CT) reconstruction (DLR) algorithms promise to improve image quality but the impact on clinical diagnostic performance remains to be demonstrated. We aimed to compare DLR to standard iterative reconstruction for detection of urolithiasis by unenhanced CT in children and young adults.

Methods

This was an IRB approved retrospective study involving post-hoc reconstruction of clinically acquired unenhanced abdomen/pelvis CT scans. Images were reconstructed with six different manufacturer-standard DLR algorithms and reformatted in 3 planes (axial, sagittal, and coronal) at 3 mm intervals. De-identified reconstructions were loaded as independent examinations for review by 3 blinded radiologists (R1, R2, R3) tasked with identifying and measuring all stones. Results were compared to the clinical iterative reconstruction images as a reference standard. IntraClass correlation coefficients and kappa (k) statistics were used to quantify agreement.

Results

CT data for 14 patients (mean age: 17.3 ± 3.4 years, 5 males and 9 females, weight class: 31-70 kg (n = 6), 71-100 kg (n = 7), > 100 kg (n = 1)) were reconstructed into 84 total exams. 7 patients had urinary tract calculi. Interobserver agreement on the presence of any urinary tract calculus was substantial to almost perfect (k = 0.71–1) for all DLR algorithms. Agreement with the reference standard on number of calculi was excellent (ICC = 0.78–0.96) and agreement on the size of the largest calculus was fair to excellent (ICC = 0.51–0.97) depending on reviewer and DLR algorithm.

Conclusion

Deep learning reconstruction of unenhanced CT images allows similar renal stone detectability compared to iterative reconstruction.

Graphic abstract

Miah T, Kamat D (2017) Pediatric Nephrolithiasis: A Review. Pediatr Ann 46(6):e242–e244. https://doi.org/10.3928/19382359-20170517-02CrossRefPubMed

Brisbane W, Bailey MR, Sorensen MD (2016) An overview of kidney stone imaging techniques. Nat Rev Urol 13(11):654–662. https://doi.org/10.1038/nrurol.2016.154CrossRefPubMedPubMedCentral

Hong JY, Lee DH, Chang IH, Park SB, Kim CW, Chi BH (2018) Inter-observer Agreement between Urologists and Radiologists in Interpreting the Computed Tomography Images of Emergency Patients with Renal Colic. Urol J 15 (2):6–9. https://doi.org/10.22037/uj.v0i0.3906

Bowen DK, Tasian GE (2018) Pediatric Stone Disease. Urol Clin North Am 45(4):539–550. https://doi.org/10.1016/j.ucl.2018.06.002CrossRefPubMed

Li LL, Wang H, Song J, Shang J, Zhao XY, Liu B (2021) A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm. J Xray Sci Technol. https://doi.org/10.3233/XST-200826CrossRefPubMedPubMedCentral

Ichikawa Y, Kanii Y, Yamazaki A, Nagasawa N, Nagata M, Ishida M, Kitagawa K, Sakuma H (2021) Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction. Jpn J Radiol. https://doi.org/10.1007/s11604-021-01089-6CrossRefPubMedPubMedCentral

Cao L, Liu X, Li J, Qu T, Chen L, Cheng Y, Hu J, Sun J, Guo J (2021) A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions. Br J Radiol 94(1118):20201086. https://doi.org/10.1259/bjr.20201086CrossRefPubMed

Tatsugami F, Higaki T, Nakamura Y, Yu Z, Zhou J, Lu Y, Fujioka C, Kitagawa T, Kihara Y, Iida M, Awai K (2019) Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29(10):5322–5329. https://doi.org/10.1007/s00330-019-06183-yCrossRefPubMed

Brady SL, Trout AT, Somasundaram E, Anton CG, Li Y, Dillman JR (2021) Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction. Radiology 298(1):180–188. https://doi.org/10.1148/radiol.2020202317CrossRefPubMed

10.

Coursey CA, Casalino DD, Remer EM, Arellano RS, Bishoff JT, Dighe M, Fulgham P, Goldfarb S, Israel GM, Lazarus E, Leyendecker JR, Majd M, Nikolaidis P, Papanicolaou N, Prasad S, Ramchandani P, Sheth S, Vikram R (2012) ACR Appropriateness Criteria(R) acute onset flank pain–suspicion of stone disease. Ultrasound Q 28(3):227–233. https://doi.org/10.1097/RUQ.0b013e3182625974CrossRefPubMed

11.

Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174CrossRefPubMed

12.

Hallgren KA (2012) Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. Tutor Quant Methods Psychol 8 (1):23–34. https://doi.org/10.20982/tqmp.08.1.p023

13.

Singh R, Digumarthy SR, Muse VV, Kambadakone AR, Blake MA, Tabari A, Hoi Y, Akino N, Angel E, Madan R, Kalra MK (2020) Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT. AJR Am J Roentgenol 214(3):566–573. https://doi.org/10.2214/AJR.19.21809CrossRefPubMed

14.

Jin DH, Lamberton GR, Broome DR, Saaty H, Bhattacharya S, Lindler TU, Baldwin DD (2009) Renal stone detection using unenhanced multidetector row computerized tomography–does section width matter? J Urol 181(6):2767–2773. https://doi.org/10.1016/j.juro.2009.01.092CrossRefPubMed

15.

Kluner C, Hein PA, Gralla O, Hein E, Hamm B, Romano V, Rogalla P (2006) Does ultra-low-dose CT with a radiation dose equivalent to that of KUB suffice to detect renal and ureteral calculi? J Comput Assist Tomogr 30(1):44–50. https://doi.org/10.1097/01.rct.0000191685.58838.efCrossRefPubMed

16.

Roberts MJ, Williams J, Khadra S, Nalavenkata S, Kam J, McCombie SP, Arianayagam M, Canagasingham B, Ferguson R, Khadra M, Varol C, Winter M, Sanaei F, Loh H, Thakkar Y, Dugdale P, Ko R (2020) A prospective, matched comparison of ultra-low and standard-dose computed tomography for assessment of renal colic. BJU Int 126(Suppl 1):27–32. https://doi.org/10.1111/bju.15116CrossRefPubMed

17.

Rodger F, Roditi G, Aboumarzouk OM (2018) Diagnostic Accuracy of Low and Ultra-Low Dose CT for Identification of Urinary Tract Stones: A Systematic Review. Urol Int 100(4):375–385. https://doi.org/10.1159/000488062CrossRefPubMed

18.

Noda Y, Kaga T, Kawai N, Miyoshi T, Kawada H, Hyodo F, Kambadakone A, Matsuo M (2021) Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection. Br J Radiol:20201329. https://doi.org/10.1259/bjr.20201329

19.

Zeng L, Xu X, Zeng W, Peng W, Zhang J, Sixian H, Liu K, Xia C, Li Z (2021) Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose. Eur J Radiol 135:109487. https://doi.org/10.1016/j.ejrad.2020.109487CrossRefPubMed

Titel: Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms
verfasst von: Samjhana Thapaliya
Samuel L. Brady
Elanchezhian Somasundaram
Christopher G. Anton
Brian D. Coley
Alexander J. Towbin
Bin Zhang
Jonathan R. Dillman
Andrew T. Trout
Publikationsdatum: 04.10.2021
Verlag: Springer US
Erschienen in: Abdominal Radiology / Ausgabe 1/2022
Print ISSN: 2366-004X
Elektronische ISSN: 2366-0058
DOI: https://doi.org/10.1007/s00261-021-03274-7

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms