Stent imaging on a clinical dual-source photon-counting detector CT system-impact of luminal attenuation and sharp kernels on lumen visibility

OBJECTIVES

To assess the impact of scan modes and reconstruction kernels using a novel dual-source photon-counting detector CT (PCD-CT) on lumen visibility and sharpness of different stent sizes.

METHODS

A phantom containing six balloon-expandable stents (2.5 to 9 mm diameter) in silicone tubing was scanned on a PCD-CT with standard (0.6 mm and 0.4 mm thicknesses) and ultra-high-resolution (0.2 mm thickness) modes. With the use of increasing contrast medium concentrations, densities of 0, 200, 400, and 600 HU were achieved. Standard-resolution scans were reconstructed using increasing sharpness kernels, using both polyenergetic quantitative soft tissue "conventional" ((Qr40_c(0.6 mm), Qr40_c(0.4 mm), Qr72_c(0.2 mm)) and vascular (Bv) virtual monoenergetic reconstructions (Bv44_m(0.4 mm), Bv60_m(0.4 mm)) at 70 keV. In-stent lumen visibility, sharpness (max. ΔHU of the stent measured in profile plots), and in-stent noise (standard deviation of HU) were measured.

RESULTS

In-stent lumen visibility was highest for Qr72_c(0.2 mm) (86.5 ± 2.8% to 88.3 ± 2.6%) and in Bv60_m(0.4 mm) reconstructions (77.3 ± 2.9 to 82.7 ± 2.5%). Lumen visibility was lowest in the smallest stent (2.5 mm) ranging from 54.1% in Qr40_c(0.6 mm) to 74.1% in Qr72_c(0.2 mm) and highest in the largest stent (9 mm) ranging from 93.8% in Qr40c(0.6 mm) to 99.1% in the Qr72_c(0.2 mm) series. Lumen visibility decreased by 2.1% for every 200-HU increase in lumen attenuation. Max. ΔHU between stents and stent lumen was highest in Qr72_c(0.2 mm) (ΔHU 892 ± 504 to 1526 ± 517) and Bv60_m(0.4 mm) series (ΔHU 480 ± 357 to 1030 ± 344). Improvement of lumen visibility and sharpness in UHR and Bv60_m(0.4 mm) series was strongest in smaller stent sizes.

CONCLUSION

UHR acquisition mode and sharp reconstruction kernels on a novel PCD-CT system significantly improve in-stent lumen visibility and sharpness-especially for smaller stent sizes.

KEY POINTS

• In-stent lumen visibility and sharpness of stents significantly improve using sharp reconstruction kernels (Bv60) and ultra-high-resolution mode in photon-counting detector computed tomography. • The observed improvement of stent-lumen visibility was highest in smaller stent sizes.

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

OBJECTIVE

To identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications.

METHODS

CT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction.

RESULTS

Fifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness-kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20).

CONCLUSION

CT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy.

KEY POINTS

• The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability. • Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings. • ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).

A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study

OBJECTIVE

Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies.

MATERIALS AND METHODS

T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions.

RESULTS

Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms.

DISCUSSION

A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Objective

To identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications.

Methods

CT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction.

Results

Fifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness–kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20).

Conclusion

CT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy.

Key Points

• The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability.

• Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings.

• ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07174-0) contains supplementary material, which is available to authorized users.

Dual-energy CT: a phantom comparison of different platforms for abdominal imaging

OBJECTIVES

Evaluation of imaging performance across dual-energy CT (DECT) platforms, including dual-layer CT (DLCT), rapid-kVp-switching CT (KVSCT) and dual-source CT (DSCT).

METHODS

A semi-anthropomorphic abdomen phantom was imaged on these DECT systems. Scans were repeated three times for CTDIvol levels of 10 mGy, 20 mGy, 30 mGy and different fat-simulating extension rings. Over the available range of virtual-monoenergetic images (VMI), noise as well as quantitative accuracy of hounsfield units (HU) and iodine concentrations were evaluated.

RESULTS

For all VMI levels, HU values could be determined with high accuracy compared to theoretical values. For KVSCT and DSCT, a noise increase was observed towards lower VMI levels. A patient-size dependent increase in the uncertainty of quantitative iodine concentrations is observed for all platforms. For a medium patient size the iodine concentration root-mean-square deviation at 20 mGy is 0.17 mg/ml (DLCT), 0.30 mg/ml (KVSCT) and 0.77mg/ml (DSCT).

CONCLUSION

Noticeable performance differences are observed between investigated DECT systems. Iodine concentrations and VMI HUs are accurately determined across all DECT systems. KVSCT and DLCT deliver slightly more accurate iodine concentration values than DSCT for investigated scenarios. In DLCT, low-noise and high-image contrast at low VMI levels may help to increase diagnostic information in abdominal CT.

KEY POINTS

• Current dual-energy CT platforms provide accurate, reliable quantitative information. • Dual-energy CT cross-platform evaluation revealed noticeable performance differences between different systems. • Dual-layer CT offers constant noise levels over the complete energy range.

Objective criteria for acceptability and constancy tests of digital subtraction angiography

PURPOSE

Demonstrate an objective procedure to quantify image quality in digital subtraction angiography (DSA) and suggest thresholds for acceptability and constancy tests.

METHODS

Series of images were obtained in a DSA system simulating a small (paediatric) and a large patient using the dynamic phantom described in the IEC and DIN standards for acceptance tests of DSA equipment. Image quality was quantified using measurements of contrast-to-noise ratio (CNR). Overall scores combining the CNR of 10-100 mg/ml Iodine at a vascular diameter of 1-4 mm in a homogeneous background were defined. Phantom entrance surface air kerma (Ka,e) was measured with an ionisation chamber.

RESULTS

The visibility of a low-contrast vessel in DSA images has been identified with a CNR value of 0.50 ± 0.03. Despite using 14 times more Ka,e (8.85 vs 0.63 mGy/image), the protocol for large patients showed a decrease in the overall score CNRsum of 67% (4.21 ± 0.06 vs 2.10 ± 0.05). The uncertainty in the results of the objective method was below 5%.

CONCLUSION

Objective evaluation of DSA images using CNR is feasible with dedicated phantom measurements. An objective methodology has been suggested for acceptance tests compliant with the IEC/DIN standards. The defined overall scores can serve to fix a reproducible baseline for constancy tests, as well as to study the device stability within one acquisition series and compare different imaging protocols. This work provides aspects that have not been included in the recent European guidelines on Criteria for Acceptability of Medical Radiological Equipment.