Interscanner Comparison Research Articles

Intra-patient variability of iodine quantification across different dual-energy CT platforms: assessment of normalization techniques.

To investigate intra-patient variability of iodine concentration (IC) between three different dual-energy CT (DECT) platforms and to testdifferent normalization approaches. Forty-four patients who underwent portal venous phase abdominal DECT on a dual-source (dsDECT), a rapid kVp switching (rsDECT), and a dual-layer detector platform (dlDECT) during cancer follow-up were retrospectively included. IC in the liver, pancreas, and kidneys and different normalized ICs (NICPV:portal vein; NICAA:abdominal aorta; NICALL:overall iodine load) were compared between the three DECT scanners for each patient. A longitudinal mixed effects analysis was conducted to elucidate the effect of the scanner type, scan order, inter-scan time, and contrast media amount on normalized iodine concentration. Variability of IC was highest in the liver (dsDECT vs. dlDECT 28.96 (14.28-46.87) %, dsDECT vs. rsDECT 29.08 (16.59-62.55) %, rsDECT vs. dlDECT 22.85 (7.52-33.49) %), and lowest in the kidneys (dsDECT vs. dlDECT 15.76 (7.03-26.1) %, dsDECT vs. rsDECT 15.67 (8.86-25.56) %, rsDECT vs. dlDECT 10.92 (4.92-22.79) %). NICALL yielded the best reduction of IC variability throughout all tissues and inter-scanner comparisons, yet did not reduce the variability between dsDECT vs. dlDECT and rsDECT, respectively, in the liver. The scanner type remained a significant determinant for NICALL in the pancreas and the liver (F-values, 12.26 and 23.78; both, p < 0.0001). We found tissue-specific intra-patient variability of IC across different DECT scanner types. Normalization mitigated variability by reducing physiological fluctuations in iodine distribution.Afternormalization, the scanner type stillhad a significant effect on iodine variability in the pancreas and liver. Differences in iodine quantification between dual-energy CT scanners can partly be mitigated by normalization, yet remain relevant for specific tissues and inter-scanner comparisons, which should be taken into account at clinical routine imaging. • Iodine concentration showed the least variability between scanner types in the kidneys (range 10.92-15.76%) and highest variability in the liver (range 22.85-29.08%). • Normalizing tissue-specific iodine concentrations against the overall iodine load yielded the greatest reduction of variability between scanner types for 2/3 inter-scanner comparisons in the liver and for all (3/3) inter-scanner comparisons in the kidneys and pancreas, respectively. • However, even after normalization, the dual-energy CT scanner type was found to be the factor significantly influencing variability of iodine concentration in the liver and pancreas.

Qualitopix: Artificial intelligence-based quantitative quality assurance of immunohistochemistry staining-The Henry Ford Health experience

Abstract Introduction/Objective Immunohistochemistry (IHC) offers crucial patient data for diagnosis, prognosis, and treatment. Pathologists currently assess stain quality subjectively, comparing control sections to patient tissue. Qualitopix (Visiopharm, Denmark) is a cloud-based platform that enables objective, quantitative analysis of staining consistency through external cell line controls. Methods/Case Report Over six months, we tested 1121 slides from five cell-line blocks with epitopes of different intensities for ER, PR, Ki-67, Her-2 Neu, and PD-L1. Slides were stained using Ventana Benchmark Ultra (Roche, Basel, Switzerland) for ER, PR, and Her-2 and Dako Omnis (Agilent, Santa Clara, California, United states) for Ki-67 and PD-L1. They were scanned with DP 200 scanner (Roche) and uploaded to Qualitopix for image analysis. Cells were detected using artificial intelligence and classified based on diaminobenzidine (DAB) staining intensity, reported as H-scores (0-100). Outliers were determined at 1 standard deviation. a selection of outliers was rescanned. Inter- scanner comparisons and repeatability studies were performed for ER and 50 within and between the DP 200 and HT scanners (Roche). Results (if a Case Study enter NA) Our analysis revealed a 26-34% rate of outliers at 1 SD for the five stains, 10 times higher than the currently reported number from our lab. Most outliers (70-100%) remained out of range at rescanning, suggesting pre-analytical technical issues. 5-33% of slides failed analysis due to technical errors. Poor discrimination between sequential cores of varying intensity for ER and Ki-67 was noted. Inter-scanner comparison and repeatability studies demonstrated precise and consistent H-scores within and across both scanning platforms for ER and PR. Conclusion Qualitopix offers automated, objective, and quantitative quality assurance for IHC stain quality assessment, a hitherto manual, subjective and qualitative-driven process. Our findings emphasize the need for continuous IHC quality monitoring. The origin of outliers, technical failures, and their correlation with pathologist's subjective findings require further investigation.

Repeatability and reproducibility of various 4D Flow MRI postprocessing software programs in a multi-software and multi-vendor cross-over comparison study

BackgroundDifferent software programs are available for the evaluation of 4D Flow cardiovascular magnetic resonance (CMR). A good agreement of the results between programs is a prerequisite for the acceptance of the method. Therefore, the goal was to compare quantitative results from a cross-over comparison in individuals examined on two scanners of different vendors analyzed with four postprocessing software packages. MethodsEight healthy subjects (27 ± 3 years, 3 women) were each examined on two 3T CMR systems (Ingenia, Philips Healthcare; MAGNETOM Skyra, Siemens Healthineers) with a standardized 4D Flow CMR sequence. Six manually placed aortic contours were evaluated with Caas (Pie Medical Imaging, SW-A), cvi42 (Circle Cardiovascular Imaging, SW-B), GTFlow (GyroTools, SW-C), and MevisFlow (Fraunhofer Institute MEVIS, SW-D) to analyze seven clinically used parameters including stroke volume, peak flow, peak velocity, and area as well as typically scientifically used wall shear stress values. Statistical analysis of inter- and intrareader variability, inter-software and inter-scanner comparison included calculation of absolute and relative error (ER), intraclass correlation coefficient (ICC), Bland–Altman analysis, and equivalence testing based on the assumption that inter-software differences needed to be within 80% of the range of intrareader differences. ResultsSW-A and SW-C were the only software programs showing agreement for stroke volume (ICC = 0.96; ER = 3 ± 8%), peak flow (ICC: 0.97; ER = −1 ± 7%), and area (ICC = 0.81; ER = 2 ± 22%). Results from SW-A/D and SW-C/D were equivalent only for area and peak flow. Other software pairs did not yield equivalent results for routinely used clinical parameters. Especially peak maximum velocity yielded poor agreement (ICC ≤ 0.4) between all software packages except SW-A/D that showed good agreement (ICC = 0.80). Inter- and intrareader consistency for clinically used parameters was best for SW-A and SW-D (ICC = 0.56–97) and worst for SW-B (ICC = -0.01–0.71). Of note, inter-scanner differences per individual tended to be smaller than inter-software differences. ConclusionsOf all tested software programs, only SW-A and SW-C can be used equivalently for determination of stroke volume, peak flow, and vessel area. Irrespective of the applied software and scanner, high intra- and interreader variability for all parameters have to be taken into account before introducing 4D Flow CMR in clinical routine. Especially in multicenter clinical trials a single image evaluation software should be applied.

Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer's Disease and Controls.

Magnetic Resonance Imaging (MRI) has become part of the clinical routine for diagnosing neurodegenerative disorders. Since acquisitions are performed at multiple centers using multiple imaging systems, detailed analysis of brain volumetry differences between MRI systems and scan-rescan acquisitions can provide valuable information to correct for different MRI scanner effects in multi-center longitudinal studies. To this end, five healthy controls and five patients belonging to various stages of the AD continuum underwent brain MRI acquisitions on three different MRI systems (Philips Achieva dStream 1.5T, Philips Ingenia 3T, and GE Discovery MR750w 3T) with harmonized scan parameters. Each participant underwent two subsequent MRI scans per imaging system, repeated on three different MRI systems within 2 h. Brain volumes computed by icobrain dm (v5.0) were analyzed using absolute and percentual volume differences, Dice similarity (DSC) and intraclass correlation coefficients, and coefficients of variation (CV). Harmonized scans obtained with different scanners of the same manufacturer had a measurement error closer to the intra-scanner performance. The gap between intra- and inter-scanner comparisons grew when comparing scans from different manufacturers. This was observed at image level (image contrast, similarity, and geometry) and translated into a higher variability of automated brain volumetry. Mixed effects modeling revealed a significant effect of scanner type on some brain volumes, and of the scanner combination on DSC. The study concluded a good intra- and inter-scanner reproducibility, as illustrated by an average intra-scanner (inter-scanner) CV below 2% (5%) and an excellent overlap of brain structure segmentation (mean DSC > 0.88).

Robustness of dual-energy CT-derived radiomic features across three different scanner types.

To investigate the robustness of radiomic features between three dual-energy CT (DECT) systems. An anthropomorphic body phantom was scanned on three different DECT scanners, a dual-source (dsDECT), a rapid kV-switching (rsDECT), and a dual-layer detector DECT (dlDECT). Twenty-four patients who underwent abdominal DECT examinations on each of the scanner types during clinical follow-up were retrospectively included (n = 72 examinations). Radiomic features were extracted after standardized image processing, following ROI placement in phantom tissues and healthy appearing hepatic, splenic and muscular tissue of patients using virtual monoenergetic images at 65keV (VMI65keV) and virtual unenhanced images (VUE). In total, 774 radiomic features were extracted including 86 original features and 8 wavelet transformations hereof. Concordance correlation coefficients (CCC) and analysis of variances (ANOVA) were calculated to determine inter-scanner robustness of radiomic features with a CCC of ≥ 0.9 deeming a feature robust. None of the phantom-derived features attained the threshold for high feature robustness for any inter-scanner comparison. The proportion of robust features obtained from patients scanned on all three scanners was low both in VMI65keV (dsDECT vs. rsDECT:16.1% (125/774), dlDECT vs. rsDECT:2.5% (19/774), dsDECT vs. dlDECT:2.6% (20/774)) and VUE (dsDECT vs. rsDECT:11.1% (86/774), dlDECT vs. rsDECT:2.8% (22/774), dsDECT vs. dlDECT:2.7% (21/774)). The proportion of features without significant differences as per ANOVA was higher both in patients (51.4-71.1%) and in the phantom (60.6-73.4%). The robustness of radiomic features across different DECT scanners in patients was low and the few robust patient-derived features were not reflected in the phantom experiment. Future efforts should aim to improve the cross-platform generalizability of DECT-derived radiomics. • Inter-scanner robustness of dual-energy CT-derived radiomic features was on a low level in patients who underwent clinical examinations on three DECT platforms. • The few robust patient-derived features were not confirmed in our phantom experiment. • Limited inter-scanner robustness of dual-energy CT derived radiomic features may impact the generalizability of models built with features from one particular dual-energy CT scanner type.

Phantom based quality assurance for long-term stability and inter-scanner compatibility in T1, T2 and T2* measurements

Abstract Funding Acknowledgements Type of funding sources: None. Background The quantitative measurement of T1, T2 and T2* relaxation parameters is prone to numerous physical variables related to scanner performance and installation environment. These variables include the presence of eddy currents, main magnetic field inhomogeneity and gradient field performance. Thus, it is important to verify that the measured relaxation parameters are inter-scanner compatible and there are no drift in the measured values over time. Purpose In this study, we produced a phantom for the relaxation parameter measurements. With the phantom, long-time stability and inter-scanner comparison of parameters were studied with preliminary quality assurance protocol. Methods A cylindrical quality assurance phantom with 16 inserts filled with ammonium iron(II) ((NH4)2Fe(SO4)2 · 6H2O) doped gel was produced. The (NH4)2Fe(SO4)2 concentrations were from 0,0119 mmol/g to 0,5498 mmol/g, corresponding to approximate T1 values of 10–1030 ms, T2 values of 16–78 ms and T2* values of 5–61 ms. The phantom was measured quarterly for a one-year period with three scanners from two different vendors to follow the scanner stability and inter-scanner compatibility. The used imaging sequences included T1 MOLLI -based sequence, T2 prep -based sequence, and multi-echo gradient echo sequence for T2* calculation. Simulated ECG signal with 60 beats per minute was used in the scanning to mimic a typical rest heart rate of an adult patient. Results The range of variation between scanners from average relaxation coefficient in T1 was -2% to 4% and in T2 and T2* measurements -7% to 5% and -32% to 18%, respectively. In long-term stability test, the range of variation from average relaxation coefficient in T1 was -1% to 2% and in T2 and T2* measurements -1% to 1% and -15% to 16%, respectively. Conclusion(s) There were significant differences in the measured T1, T2, and T2* relaxation coefficients in both inter-scanner and long-term stability. However, the detected variations were acceptable compared to the typical physiological uncertainties of the measured parameters but have to be taken into account when the reliability of the study is assessed. Abstract Figure. Results and phantom image

Impact of gradient imperfections on bone water quantification with UTE MRI.

The impact of gradient imperfections on UTE images and UTE image-derived bone water quantification was investigated at 3 T field strength. The effects of simple gradient time delays and eddy currents on UTE images, as well as the effects of gradient error corrections, were studied with simulation and phantom experiments. The k-space trajectory was mapped with a 2D sequence with phase encoding on both spatial axes by measuring the phase of the signal in small time increments during ramp-up of the read gradient. In vivo 3D UTE images were reconstructed with and without gradient error compensation to determine the bias in bone water quantification. Finally, imaging was performed on 2 equally configured Siemens TIM Trio systems (Siemens Medical Solutions, Erlangen, Germany) to investigate the impact of such gradient imperfections on inter-scanner measurement bias. Compared to values derived from UTE images with full gradient error compensation, total bone water was found to deviate substantially with no (up to 17%) or partial (delay-only) compensation (up to 10.8%). Bound water, obtained with inversion recovery-prepared UTE, was somewhat less susceptible to gradient errors (up to 2.2% for both correction strategies). Inter-scannercomparisonindicated a statistically significant bias between measurements from the 2 MR systems for both total and bound water, which either vanished or was substantially reduced following gradient error correction. Gradient imperfections impose spatially dependent artifacts on UTE images, which compromise not only bone water quantification accuracy but also inter-scanner measurement agreement if left uncompensated.

Effects of alterations in positron emission tomography imaging parameters on radiomics features.

Radiomics studies require large patient cohorts, which often include patients imaged using different imaging protocols. We aimed to determine the impact of variability in imaging protocol parameters and interscanner variability using a phantom that produced feature values similar to those of patients. Positron emission tomography (PET) scans of a Hoffman brain phantom were acquired on GE Discovery 710, Siemens mCT, and Philips Vereos scanners. A standard-protocol scan was acquired on each machine, and then each parameter that could be changed was altered individually. The phantom was contoured with 10 regions of interest (ROIs). Values for 45 features with 2 different preprocessing techniques were extracted for each image. To determine the impact of each parameter on the reliability of each radiomics feature, the intraclass correlation coefficient (ICC) was calculated with the ROIs as the subjects and the parameter values as the raters. For interscanner comparisons, we compared the standard deviation of each radiomics feature value from the standard-protocol images to the standard deviation of the same radiomics feature from PET scans of 224 patients with non-small cell lung cancer. When the pixel size was resampled prior to feature extraction, all features had good reliability (ICC > 0.75) for the field of view and matrix size. The time per bed position had excellent reliability (ICC > 0.9) on all features. When the filter cutoff was restricted to values below 6 mm, all features had good reliability. Similarly, when subsets and iterations were restricted to reasonable values used in clinics, almost all features had good reliability. The average ratio of the standard deviation of features on the phantom scans to that of the NSCLC patient scans was 0.73 using fixed-bin-width preprocessing and 0.92 using 64-level preprocessing. Most radiomics feature values had at least good reliability when imaging protocol parameters were within clinically used ranges. However, interscanner variability was about equal to interpatient variability; therefore, caution must be used when combining patients scanned on equipment from different vendors in radiomics data sets.

Interscanner and Intrascanner Comparison of Virtual Unenhanced Attenuation Values Derived From Twin Beam Dual-Energy and Dual-Source, Dual-Energy Computed Tomography

The aim of the current study was to evaluate the reliability and comparability of virtual unenhanced (VUE) attenuation values derived from scans of a single-source, dual-energy computed tomography using a split-filter (tbDECT) to a dual-source dual-energy CT (dsDECT). In this retrospective study, comparisons for tbDECT and dsDECT were made within and between different dual-energy platforms. For the interscanner comparison, 126 patients were scanned with both scanners within a time interval of 224 ± 180 days; for the intrascanner comparison, another 90 patients were scanned twice with the same scanner within a time interval of 136 ± 140 days. Virtual unenhanced images were processed off of venous phase series. Attenuation values of 7 different tissues were recorded. Disagreement for VUE HU measurements greater than 10 HU between 2 scans was defined as inadequate. The interscanner analysis showed significant difference between tbDE and dsDE VUE CT values (P < 0.01) for 6 of 7 organs. Percentage of cases that had more than 10 HU difference between tbDE and dsDE for an individual patient ranged between 15% (left kidney) and 62% (spleen).The intrascanner analysis showed no significant difference between repeat scans for both tbDECT and dsDECT (P > 0.05). However, intrascanner disagreements for the VUE HU measurements greater than 10 HU were recorded in 10% of patients scanned on the tbDECT and 0% of patients scanned on the dsDECT. The organs with the highest portion of greater than 10 HU errors were the liver and the aorta (both 20%). Dual-energy techniques vary in reproducibility of VUE attenuation values. In the current study, tbDECT demonstrated higher variation in VUE HU measurements in comparison to a dsDECT. Virtual unenhanced HU measurements cannot be reliably compared on follow-up CT, if these 2 different dual-energy CT platforms are used.

Intra- and Inter-Scanner Reliability of Voxel-Wise Whole-Brain Analytic Metrics for Resting State fMRI.

As the multi-center studies with resting-state functional magnetic resonance imaging (RS-fMRI) have been more and more applied to neuropsychiatric studies, both intra- and inter-scanner reliability of RS-fMRI are becoming increasingly important. The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) are 3 main RS-fMRI metrics in a way of voxel-wise whole-brain (VWWB) analysis. Although the intra-scanner reliability (i.e., test-retest reliability) of these metrics has been widely investigated, few studies has investigated their inter-scanner reliability. In the current study, 21 healthy young subjects were enrolled and scanned with blood oxygenation level dependent (BOLD) RS-fMRI in 3 visits (V1 – V3), with V1 and V2 scanned on a GE MR750 scanner and V3 on a Siemens Prisma. RS-fMRI data were collected under two conditions, eyes open (EO) and eyes closed (EC), each lasting 8 minutes. We firstly evaluated the intra- and inter-scanner reliability of ALFF, ReHo, and DC. Secondly, we measured systematic difference between two scanning visits of the same scanner as well as between two scanners. Thirdly, to account for the potential difference of intra- and inter-scanner local magnetic field inhomogeneity, we measured the difference of relative BOLD signal intensity to the mean BOLD signal intensity of the whole brain between each pair of visits. Last, we used percent amplitude of fluctuation (PerAF) to correct the difference induced by relative BOLD signal intensity. The inter-scanner reliability was much worse than intra-scanner reliability; Among the VWWB metrics, DC showed the worst (both for intra-scanner and inter-scanner comparisons). PerAF showed similar intra-scanner reliability with ALFF and the best reliability among all the 4 metrics. PerAF reduced the influence of BOLD signal intensity and hence increase the inter-scanner reliability of ALFF. For multi-center studies, inter-scanner reliability should be taken into account.

Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels.

Many radiomics features were originally developed for non-medical imaging applications and therefore original assumptions may need to be reexamined. In this study, we investigated the impact of slice thickness and pixel spacing (or pixel size) on radiomics features extracted from Computed Tomography (CT) phantom images acquired with different scanners as well as different acquisition and reconstruction parameters. The dependence of CT texture features on gray-level discretization was also evaluated. A texture phantom composed of 10 different cartridges of different materials was scanned on eight different CT scanners from three different manufacturers. The images were reconstructed for various slice thicknesses. For each slice thickness, the reconstruction Field Of View (FOV) was varied to render pixel sizes ranging from 0.39 to 0.98mm. A fixed spherical region of interest (ROI) was contoured on the images of the shredded rubber cartridge and the 3D printed, 20% fill, acrylonitrile butadiene styrene plastic cartridge (ABS20) for all phantom imaging sets. Radiomic features were extracted from the ROIs using an in-house program. Features categories were: shape (10), intensity (16), GLCM (24), GLZSM (11), GLRLM (11), and NGTDM (5), fractal dimensions (8) and first-order wavelets (128), for a total of 213 features. Voxel-size resampling was performed to investigate the usefulness of extracting features using a suitably chosen voxel size. Acquired phantom image sets were resampled to a voxel size of 1×1×2 mm3 using linear interpolation. Image features were therefore extracted from resampled and original datasets and the absolute value of the percent coefficient of variation (%COV) for each feature was calculated. Based on the %COV values, features were classified in 3 groups: (1) features with large variations before and after resampling (%COV >50); (2) features with diminished variation (%COV <30) after resampling; and (3) features that had originally moderate variation (%COV <50%) and were negligibly affected by resampling. Group 2 features were further studied by modifying feature definitions to include voxel size. Original and voxel-size normalized features were used for interscanner comparisons. A subsequent analysis investigated feature dependency on gray-level discretization by extracting 51 texture features from ROIs from each of the 10 different phantom cartridges using 16, 32, 64, 128, and 256 gray levels. Out of the 213 features extracted, 150 were reproducible across voxel sizes, 42 improved significantly (%COV <30, Group 2) after resampling, and 21 had large variations before and after resampling (Group 1). Ten features improved significantly after definition modification effectively removed their voxel-size dependency. Interscanner comparison indicated that feature variability among scanners nearly vanished for 8 of these 10 features. Furthermore, 17 out of 51 texture features were found to be dependent on the number of gray levels. These features were redefined to include the number of gray levels which greatly reduced this dependency. Voxel-size resampling is an appropriate pre-processing step for image datasets acquired with variable voxel sizes to obtain more reproducible CT features. We found that some of the radiomics features were voxel size and gray-level discretization-dependent. The introduction of normalizing factors in their definitions greatly reduced or removed these dependencies.

MONITORING DOSE-LENGTH PRODUCT IN COMPUTED TOMOGRAPHY OF THE CHEST CONSIDERING SEX AND BODY WEIGHT.

Dose-length product (DLP) is widely used as an indicator of the radiation dose in computed tomography. The aim of this study was to investigate the significance of sex and body weight in DLP-based monitoring of the radiation dose. Eight hundred computed tomographies of the chest performed using four different scanners were analysed. The DLP was compared with body weight by linear regression in men and women separately. The DLP was positively correlated with body weight, and dependence on sex and weight differed among scanners. Standard DLP values adjusted for sex and weight facilitated interscanner comparison of the radiation dose and its dependence on sex and weight. Adjusting the DLP for sex and weight allowed one to identify examinations with possibly excessive doses independently of weight. Monitoring the DLP in relation to sex and body weight appears to aid detailed comparison of the radiation dose among imaging protocols and scanners and daily observations to find unexpected variance.

Volume transition analysis: A new approach to resolve reclassification of brain tissue in repeated MRI scans

BackgroundVariability in brain tissue volumes derived from magnetic resonance images is attributable to various sources. In quantitative comparisons it is therefore crucial to distinguish between biologically and methodically conditioned variance and to take spatial accordance into account. New methodWe introduce volume transition analysis as a method that not only provides details on numerical and spatial accordance of tissue volumes in repeated scans but also on voxel shifts between tissue types. Based on brain tissue probability maps, mono- and bidirectional voxel shifts can be examined by explicitly separating volume transitions into source and target. We apply the approach to a set of subject data from repeated intra-scanner (one week and 30 month interval) as well as inter-scanner measurements. ResultsIn all measurement scenarios, we found similar inter-class transitions of 9.9–15.9% of intracranial volume. The percentage of monodirectional net volume transition however increases from 0.3% in short term intra-scanner to 1.6% in long term intra-scanner and 9.3% in inter-scanner comparisons. Comparison with existing methodsUnlike most routinely used variability measures volume transition analysis is able to monitor reclassifications and thus to quantify not only balanced flows but also the amount of monodirectional net flows between tissue classes. The approach is independent from group analysis and can thus be applied in as few as two images. ConclusionsThe proposed method is an easily applicable tool that is useful in discovering intra-individual brain changes and assists in separating biological from technical variance in structural brain measures.

Interscanner Comparison of Dynamic Contrast-Enhanced MRI in Prostate Cancer

The aim of the study was the comparison of the diagnostic potential of dynamic contrast-enhanced magnetic resonance imaging to differentiate between prostate carcinoma and normal prostate tissue as well as prostatitis at 2 different field strengths: 1.5 versus 3 T. Sixty-six patients with biopsy and/or prostatectomy of the prostate were included in the study. Magnetic resonance imaging was performed at 1.5 T in 20 patients with biopsy-proven prostate cancer (PC) and in 8 patients with prostatitis; at 3 T, we analyzed 27 patients with prostatectomy-proven PC and 11 patients with prostatitis. All examinations were performed using a combined body and endorectal coil protocol and a 2-dimensional TurboFLASH T1-weighted gradient echo sequence to calculate plasma flow (PF) and mean transit time (MTT) values. A total of 28 of 38 areas of normal prostate tissue, 20 of 27 areas of PC, and 8 of 11 prostatitis were analyzed at 1.5 or 3T. For the normalization, we calculated PC/normal and prostatitis/normal tissue ratios of PF and MTT for each patient. Prostate cancer showed higher PF (P < 0.0001) and shorter MTT (P < 0.0001) at 3 T and at 1.5 T (P < 0.0001 for PF and P = 0.0016 for MTT) compared with the normal tissue. In comparison with the normal tissue, prostatitis had a statistically significant higher PF at 1.5 T (P = 0.0156) but not at 3 T (P = 0.17) and no significantly shorter MTT values both at 3 (P = 0.15) and 1.5 T (P = 0.25). Sensitivity and specificity for differentiating PC from prostatitis with PF were 46% and 88% at 1.5 T (cutoff ratio, 2.3) and 89% and 73% at 3 T (cutoff ratio, 1.2), respectively. Sensitivity and specificity for MTT were 77% and 100% at 1.5 T (cutoff ratio, 0.7) and 70% and 100% at 3 T (cutoff ratio, 0.6), respectively. We found no significant relationship between the Gleason score and PF/MTT (P = 0.17/0.11 for 1.5 T and P = 0.23/0.18 for 3 T). The differentiation between PC and the normal tissue is possible with both field strengths. Prostate cancer can be better distinguished from prostatitis at 3 T compared with 1.5 T. The differentiation between prostatitis and the normal tissue is limited at both field strengths.

Inter-scan variability of coronary artery calcium scoring assessed on 64-multidetector computed tomography vs. dual-source computed tomography: a head-to-head comparison

Coronary artery calcium (CAC) scoring has emerged as a tool for risk stratification and potentially for monitoring response to risk factor modification. Therefore, repeat measurements should provide robust results and low inter-scanner variability for allowing meaningful comparison. The purpose of this study was to investigate inter-scanner variability of CAC for Agatston, volume, and mass scores by head-to-head comparison using two different cardiac computed tomography scanners: 64-detector multislice CT (MSCT) and 64-slice dual-source CT (DSCT). Thirty patients underwent CAC measurements on both 64-MSCT (GE LightSpeed XT scanner: 120 kV, 70 mAs, 2.5 mm slices) and 64-DSCT (Siemens Somatom Definition: 120 kV, 80 mAs, 3 mm slices) within <100 days (0-97). Retrospective intra-scan comparison revealed an excellent correlation. The excellent intra-scan (inter-observer) agreement was documented by narrow limits of agreement and a correlation coefficient of variation (COV) of r ≥ 0.99 (P < 0.001) for all CAC scores with a low COV for both scanners (64-MSCT/64-DSCT), i.e. Agatston (2.0/2.1%), mass (3.0/2.0%), and volume (4.7/3.9%). Inter-scanner comparison revealed larger Bland-Altman (BA) limits of agreement, despite high correlation (r ≥ 0.97) for all scores, with COV at 15.1, 21.6, and 44.9% for Agatston, mass, and volume scores. The largest BA limits were observed for volume scores (-1552.8 to 574.2), which was massively improved (-241.0 to 300.4, COV 11.5%) after reanalysing the 64-DSCT scans (Siemens) with GE software/workstation (while Siemens software/workstation does not allow cross-vendor analysis). Phantom measurements confirmed overestimation of volume scores by 'syngo Ca-Scoring' (Siemens) software which should therefore be reviewed (vendor has been notified). Intra- and inter-scan agreement of CAC measurement in a given data set is excellent. Inter-scanner variability is reasonable, particularly for Agatston units in the clinically most relevant range <1000. The use of different software solutions has a greater influence particularly on volume scores than the use of different scanner types.