Noninvasive differentiation of uric acid versus non-uric acid kidney stones using dual-energy CT

Abstract

Rationale and objectives: To determine the accuracy and sensitivity for dual-energy computed tomography (DECT) discrimination of uric acid (UA) stones from other (non-UA) renal stones in a commercially implemented product.

Materials and methods: Forty human renal stones comprising uric acid (n=16), hydroxyapatite (n=8), calcium oxalate (n=8), and cystine (n=8) were inserted in four porcine kidneys (10 each) and placed inside a 32-cm water tank anterior to a cadaver spine. Spiral dual-energy scans were obtained on a dual-source, 64-slice computed tomography (CT) system using a clinical protocol and automatic exposure control. Scanning was performed at two different collimations (0.6 mm and 1.2 mm) and within three phantom sizes (medium, large, and extra large) resulting in a total of six image datasets. These datasets were analyzed using the dual-energy software tool available on the CT system for both accuracy (number of stones correctly classified as either UA or non-UA) and sensitivity (for UA stones). Stone characterization was correlated with micro-CT.

Results: For the medium and large phantom sizes, the DECT technique demonstrated 100% accuracy (40/40), regardless of collimation. For the extra large phantom size and the 0.6-mm collimation (resulting in the noisiest dataset), three (two cystine and one small UA) stones could not be classified (93% accuracy and 94% sensitivity). For the extra large phantom size and the 1.2-mm collimation, the dual-energy tool failed to identify two small UA stones (95% accuracy and 88% sensitivity).

Conclusions: In an anthropomorphic phantom model, dual-energy CT can accurately discriminate uric acid stones from other stone types.

Figure 1

Forty human renal stones used in this study. The 12 “small” stones smaller than approximately 3 mm in size are circled.

Figure 2

A simplified description of how the DE algorithm works. If a datapoint corresponding to a stone with unknown composition falls below the bisector line (dashed) dividing the angle between the uric acid (UA) and non-UA line segments, the algorithm will characterize such stone as a UA stone (open circle). If an unknown datapoint falls above the angle bisector line, the corresponding stone will be identified as a non-UA stone (gray circle).

Figure 3

Examples of the color-coded images produced by the dual-energy software tool (Kidney Stones, Syngo DE Viewer, Siemens).

Figure 4

Micro- computed tomography slices showing the four types of stone used in this study and their generally homogeneous nature. Uric acid and cystine stones are generally the purest of the stones, with the uniform shade of gray in the examples shown here indicating such purity. The dark spots in the cystine stone (lower right) indicate the presence of void regions, which are common in this stone type. The layered nature of the apatite stone (upper right) is typical of this stone type, and the lower attenuation layers (darker gray) often contain calcium oxalate. The calcium oxalate monohydrate (COM) example (lower left) shows the typical uniform distribution of mineral in this stone type, with some void regions indicated by the darker grays.