Fat quantification in dual-layer detector spectral CT: How to handle iron overload, varying tube voltage and radiation dose Indices.

OBJECTIVES: Opposed to other spectral CT techniques, fat quantification in dual-layer detector CT (dlCT) has only recently been developed. The impact of concomitant iron overload and dlCT-specific protocol settings such as the dose right index (DRI), a measure of image noise and tube current, on dlCT fat quantification was unclear. Further, spectral information became newly available <120 kV. Therefore, this study's objective was to evaluate the impact of iron, changing tube voltage, and DRI on dlCT fat quantification. MATERIAL AND METHODS: Phantoms with 0 and 8mg/cm3 iron; 0 and 5mg/cm3 iodine; 0, 10, 20, 35, 50, and 100% fat and liver equivalent, respectively, were scanned with a dlCT (CT7500, Philips, the Netherlands) at 100kV/20DRI, 120kV/20DRI, 140kV/20DRI, and at 120kV/16DRI, 120kV/24DRI. Material decomposition was done for fat, liver, and iodine (A1); for fat, liver, and iron (A2); and for fat, liver, and combined reference values of iodine and iron (A3). All scans were analyzed with reference values from 120kV/20DRI. For statistics, the intraclass correlation coefficient (ICC) and Bland-Altman analyses were used. RESULTS: In phantoms with iron and iodine, results were best for A3 with a mean deviation to phantom fat of 1.3±2.6% (ICC 0.999 [95%-confidence interval 0.996-1]). The standard approach A1 yielded a deviation of -2.5±3.0% (0.998[0.994-0.999]), A2 of 6.1±4.8% (0.991[0.974-0.997]). With A3 and changing tube voltage, the maximal difference between quantified fat and the phantom ground truth occurred at 100kV with 4.6±2.1%. Differences between scans were largest between 100kV and 140kV (2.0%[-7.1-11.2]). The maximal difference of changing DRI occurred between 16 and 24 DRI with 0.4%[-2.2-3.0]. CONCLUSION: For dlCT fat quantification in the presence of iron, material decomposition with combined reference values for iodine and iron delivers the most accurate results. Tube voltage-specific calibration of reference values is advisable while the impact of the DRI on dlCT fat quantification is neglectable.

Fat Quantification in Dual-Layer Detector Spectral Computed Tomography: Experimental Development and First In-Patient Validation.

OBJECTIVES: Fat quantification by dual-energy computed tomography (DECT) provides contrast-independent objective results, for example, on hepatic steatosis or muscle quality as parameters of prognostic relevance. To date, fat quantification has only been developed and used for source-based DECT techniques as fast kVp-switching CT or dual-source CT, which require a prospective selection of the dual-energy imaging mode.It was the purpose of this study to develop a material decomposition algorithm for fat quantification in phantoms and validate it in vivo for patient liver and skeletal muscle using a dual-layer detector-based spectral CT (dlsCT), which automatically generates spectral information with every scan. MATERIALS AND METHODS: For this feasibility study, phantoms were created with 0%, 5%, 10%, 25%, and 40% fat and 0, 4.9, and 7.0 mg/mL iodine, respectively. Phantom scans were performed with the IQon spectral CT (Philips, the Netherlands) at 120 kV and 140 kV and 3 T magnetic resonance (MR) (Philips, the Netherlands) chemical-shift relaxometry (MRR) and MR spectroscopy (MRS). Based on maps of the photoelectric effect and Compton scattering, 3-material decomposition was done for fat, iodine, and phantom material in the image space.After written consent, 10 patients (mean age, 55 ± 18 years; 6 men) in need of a CT staging were prospectively included. All patients received contrast-enhanced abdominal dlsCT scans at 120 kV and MR imaging scans for MRR. As reference tissue for the liver and the skeletal muscle, retrospectively available non-contrast-enhanced spectral CT data sets were used. Agreement between dlsCT and MR was evaluated for the phantoms, 3 hepatic and 2 muscular regions of interest per patient by intraclass correlation coefficients (ICCs) and Bland-Altman analyses. RESULTS: The ICC was excellent in the phantoms for both 120 kV and 140 kV (dlsCT vs MRR 0.98 [95% confidence interval (CI), 0.94-0.99]; dlsCT vs MRS 0.96 [95% CI, 0.87-0.99]) and in the skeletal muscle (0.96 [95% CI, 0.89-0.98]). For log-transformed liver fat values, the ICC was moderate (0.75 [95% CI, 0.48-0.88]). Bland-Altman analysis yielded a mean difference of -0.7% (95% CI, -4.5 to 3.1) for the liver and of 0.5% (95% CI, -4.3 to 5.3) for the skeletal muscle. Interobserver and intraobserver agreement were excellent (>0.9). CONCLUSIONS: Fat quantification was developed for dlsCT and agreement with MR techniques demonstrated for patient liver and muscle. Hepatic steatosis and myosteatosis can be detected in dlsCT scans from clinical routine, which retrospectively provide spectral information independent of the imaging mode.

Improved accuracy in the assessment of vertebral cortical thickness by quantitative computed tomography using the Iterative Convolution OptimizatioN (ICON) method.

Vertebral whole bone strength is substantially affected by cortical bone properties. Disease and therapy may affect cancellous and cortical bone differently. Unlike Dual X-ray Absorptiometry (DXA), Quantitative Computed Tomography (QCT) permits selective assessment of cortical and cancellous bone, but image quality limits the accuracy. We present an image processing method specifically adopted to thin cortices that substantially improves accuracy. Ten human vertebrae embedded in epoxy resin were imaged using clinical QCT and High-Resolution QCT (HR-QCT) protocols, both acquired on a clinical whole body CT scanner, whereas high resolution peripheral QCT (HR-pQCT) was used as gold standard. Microstructural variables and BMD were calculated using in-house software StructuralInsight for QCT and HR-QCT and the manufacturer's µCT evaluation software for HR-pQCT. An adjusted measure, a deconvolved cortical thickness (dcCt.Th), corrected for partial volume effects, was derived applying the new Iterative Convolution OptimizatioN (ICON) method. Direct measurements of cortical thickness (Ct.Th) showed substantial overestimation with mean ±â€¯standard deviation of 1.8 ±â€¯0.5 mm for QCT and 1.5 ±â€¯0.3 mm for HR-QCT compared to 0.37 ±â€¯0.07 mm using HR-pQCT. Correlations of both QCT (r2 = 0.05, p > 0.5.) and HR-QCT (r2 = 0.38, p = 0.060) with the gold standard HR-pQCT were not significant. Also QCT-based BMD and BMC as well as HR-QCT-based BMD did not show a significant correlation with the gold standard approach. Only HR-QCT-based BMC showed a modest correlation (r2 = 0.59, p = 0.01) After applying ICON corrections, dcCt.Th resulted in 0.52 ±â€¯0.09 mm for QCT and 0.43 ±â€¯0.07 mm for HR-QCT, both significantly correlated to HR-pQCT (r2 = 0.75, p = 0.0012 and r2 = 0.93, p < 0.0001, respectively). The average overestimation bias of Ct.Th was reduced from (402 ±â€¯157)% to (45 ±â€¯17)% for QCT and from (330 ±â€¯69)% to (19 ±â€¯8)% for HR-QCT. Due to inaccurate segmentation uncorrected QCT-based Ct.Th measures as well as BMD and BMC showed no correlation to HR-pQCT and thus such bias cortical data can be misleading. The application of ICON reduced random overestimation bias to about 50 µm and 20 µm for QCT and HR-QCT, respectively, leading to a recovery of a significant correlation with the reference data of HR-pQCT. This reveals the potential for fairly accurate assessment of cortical thickness, allowing to better characterize cortical mechanical competence. These results warrant testing of the performance in vivo.

Skeletal assessment with finite element analysis: relevance, pitfalls and interpretation.

PURPOSE OF REVIEW: Finite element models simulate the mechanical response of bone under load, enabling noninvasive assessment of strength. Models generated from quantitative computed tomography (QCT) incorporate the geometry and spatial distribution of bone mineral density (BMD) to simulate physiological and traumatic loads as well as orthopaedic implant behaviour. The present review discusses the current strengths and weakness of finite element models for application to skeletal biomechanics. RECENT FINDINGS: In cadaver studies, finite element models provide better estimations of strength compared to BMD. Data from clinical studies are encouraging; however, the superiority of finite element models over BMD measures for fracture prediction has not been shown conclusively, and may be sex and site dependent. Therapeutic effects on bone strength are larger than for BMD; however, model validation has only been performed on untreated bone. High-resolution modalities and novel image processing methods may enhance the structural representation and predictive ability. Despite extensive use of finite element models to study orthopaedic implant stability, accurate simulation of the bone-implant interface and fracture progression remains a significant challenge. SUMMARY: Skeletal finite element models provide noninvasive assessments of strength and implant stability. Improved structural representation and implant surface interaction may enable more accurate models of fragility in the future.