Ex Vivo Renal Stone Characterization with Single-Source Dual-Energy Computed Tomography: A Multiparametric Approach

J. Scott Kriegshauser, Alvin C Silva, Robert G. Paden, Miao He, Mitchell R Humphreys, Steven I. Zell, Yinlin Fu, Teresa Wu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Rationale and Objectives: We aimed to investigate a multiparametric approach using single-source dual-energy computed tomography (ssDECT) for the characterization of renal stones. Materials and Methods: ssDECT scans were performed at 80 and 140 kVp on 32 ex vivo kidney stones of 3-10 mm in a phantom. True composition was determined by infrared spectroscopy to be uric acid (UA; n = 14), struvite (n = 7), cystine (n = 7), or calcium oxalate monohydrate (n = 4). Measurements were obtained for up to 52 variables, including mean density at 11 monochromatic keV levels, effective Z, and multiple material basis pairs. The data were analyzed with five multiparametric algorithms. After omitting 8 stones smaller than 5 mm, the remaining 24-stone dataset was similarly analyzed. Both stone datasets were also analyzed with a subset of 14 commonly used variables in the same fashion. Results: For the 32-stone dataset, the best method for distinguishing UA from non-UA stones was 97% accurate, and for distinguishing the non-UA subtypes was 72% accurate. For the 24-stone dataset, the best method for distinguishing UA from non-UA stones was 100% accurate, and for distinguishing the non-UA subtypes was 75% accurate. Conclusion: Multiparametric ssDECT methods can distinguish UA from non-UA stones of 5 mm or larger with 100% accuracy. The best model to distinguish the non-UA renal stone subtypes was 75% accurate. Further refinement of this multiparametric approach may increase the diagnostic accuracy of separating non-UA subtypes and assist in the development of a clinical paradigm for in vivo use.

Original languageEnglish (US)
JournalAcademic Radiology
DOIs
StateAccepted/In press - Aug 7 2015

Fingerprint

Tomography
Kidney
Calcium Oxalate
Kidney Calculi
Cystine
Uric Acid
Spectrum Analysis
Datasets

Keywords

  • Dual-energy computed tomography
  • Multiparametric data analysis
  • Renal stone composition

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Ex Vivo Renal Stone Characterization with Single-Source Dual-Energy Computed Tomography : A Multiparametric Approach. / Kriegshauser, J. Scott; Silva, Alvin C; Paden, Robert G.; He, Miao; Humphreys, Mitchell R; Zell, Steven I.; Fu, Yinlin; Wu, Teresa.

In: Academic Radiology, 07.08.2015.

Research output: Contribution to journalArticle

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abstract = "Rationale and Objectives: We aimed to investigate a multiparametric approach using single-source dual-energy computed tomography (ssDECT) for the characterization of renal stones. Materials and Methods: ssDECT scans were performed at 80 and 140 kVp on 32 ex vivo kidney stones of 3-10 mm in a phantom. True composition was determined by infrared spectroscopy to be uric acid (UA; n = 14), struvite (n = 7), cystine (n = 7), or calcium oxalate monohydrate (n = 4). Measurements were obtained for up to 52 variables, including mean density at 11 monochromatic keV levels, effective Z, and multiple material basis pairs. The data were analyzed with five multiparametric algorithms. After omitting 8 stones smaller than 5 mm, the remaining 24-stone dataset was similarly analyzed. Both stone datasets were also analyzed with a subset of 14 commonly used variables in the same fashion. Results: For the 32-stone dataset, the best method for distinguishing UA from non-UA stones was 97{\%} accurate, and for distinguishing the non-UA subtypes was 72{\%} accurate. For the 24-stone dataset, the best method for distinguishing UA from non-UA stones was 100{\%} accurate, and for distinguishing the non-UA subtypes was 75{\%} accurate. Conclusion: Multiparametric ssDECT methods can distinguish UA from non-UA stones of 5 mm or larger with 100{\%} accuracy. The best model to distinguish the non-UA renal stone subtypes was 75{\%} accurate. Further refinement of this multiparametric approach may increase the diagnostic accuracy of separating non-UA subtypes and assist in the development of a clinical paradigm for in vivo use.",
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AU - Paden, Robert G.

AU - He, Miao

AU - Humphreys, Mitchell R

AU - Zell, Steven I.

AU - Fu, Yinlin

AU - Wu, Teresa

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N2 - Rationale and Objectives: We aimed to investigate a multiparametric approach using single-source dual-energy computed tomography (ssDECT) for the characterization of renal stones. Materials and Methods: ssDECT scans were performed at 80 and 140 kVp on 32 ex vivo kidney stones of 3-10 mm in a phantom. True composition was determined by infrared spectroscopy to be uric acid (UA; n = 14), struvite (n = 7), cystine (n = 7), or calcium oxalate monohydrate (n = 4). Measurements were obtained for up to 52 variables, including mean density at 11 monochromatic keV levels, effective Z, and multiple material basis pairs. The data were analyzed with five multiparametric algorithms. After omitting 8 stones smaller than 5 mm, the remaining 24-stone dataset was similarly analyzed. Both stone datasets were also analyzed with a subset of 14 commonly used variables in the same fashion. Results: For the 32-stone dataset, the best method for distinguishing UA from non-UA stones was 97% accurate, and for distinguishing the non-UA subtypes was 72% accurate. For the 24-stone dataset, the best method for distinguishing UA from non-UA stones was 100% accurate, and for distinguishing the non-UA subtypes was 75% accurate. Conclusion: Multiparametric ssDECT methods can distinguish UA from non-UA stones of 5 mm or larger with 100% accuracy. The best model to distinguish the non-UA renal stone subtypes was 75% accurate. Further refinement of this multiparametric approach may increase the diagnostic accuracy of separating non-UA subtypes and assist in the development of a clinical paradigm for in vivo use.

AB - Rationale and Objectives: We aimed to investigate a multiparametric approach using single-source dual-energy computed tomography (ssDECT) for the characterization of renal stones. Materials and Methods: ssDECT scans were performed at 80 and 140 kVp on 32 ex vivo kidney stones of 3-10 mm in a phantom. True composition was determined by infrared spectroscopy to be uric acid (UA; n = 14), struvite (n = 7), cystine (n = 7), or calcium oxalate monohydrate (n = 4). Measurements were obtained for up to 52 variables, including mean density at 11 monochromatic keV levels, effective Z, and multiple material basis pairs. The data were analyzed with five multiparametric algorithms. After omitting 8 stones smaller than 5 mm, the remaining 24-stone dataset was similarly analyzed. Both stone datasets were also analyzed with a subset of 14 commonly used variables in the same fashion. Results: For the 32-stone dataset, the best method for distinguishing UA from non-UA stones was 97% accurate, and for distinguishing the non-UA subtypes was 72% accurate. For the 24-stone dataset, the best method for distinguishing UA from non-UA stones was 100% accurate, and for distinguishing the non-UA subtypes was 75% accurate. Conclusion: Multiparametric ssDECT methods can distinguish UA from non-UA stones of 5 mm or larger with 100% accuracy. The best model to distinguish the non-UA renal stone subtypes was 75% accurate. Further refinement of this multiparametric approach may increase the diagnostic accuracy of separating non-UA subtypes and assist in the development of a clinical paradigm for in vivo use.

KW - Dual-energy computed tomography

KW - Multiparametric data analysis

KW - Renal stone composition

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