Optimization of photon counting CT for cardiac imaging in patients with left ventricular assist devices: An in-depth assessment of metal artifacts.

PURPOSE: Photon counting CT (PCCT) holds promise for mitigating metal artifacts and can produce virtual mono-energetic images (VMI), while maintaining temporal resolution, making it a valuable tool for characterizing the heart. This study aimed to evaluate and optimize PCCT for cardiac imaging in patients during left ventricular assistance device (LVAD) therapy by conducting an in-depth objective assessment of metal artifacts and visual grading. METHODS: Various scan and reconstruction settings were tested on a phantom and further evaluated on a patient acquisition to identify the optimal protocol settings. The phantom comprised an empty thoracic cavity, supplemented with heart and lungs from a cadaveric lamb. The heart was implanted with an LVAD (HeartMate 3) and iodine contrast. Scans were performed on a PCCT (NAEOTOM Alpha, Siemens Healthcare). Metal artifacts were assessed by three objective methods: Hounsfield units (HU)/SD measurements (DiffHU and SDARTIFACT), Fourier analysis (AmplitudeLowFreq), and depicted LVAD volume in the images (BloomVol). Radiologists graded metal artifacts and the diagnostic interpretability in the LVAD lumen, cardiac tissue, lung tissue, and spinal cord using a 5-point rating scale. Regression and correlation analysis were conducted to determine the assessment method most closely associated with acquisition and reconstruction parameters, as well as the objective method demonstrating the highest correlation with visual grading. RESULTS: Due to blooming artifacts, the LVAD volume fluctuated between 27.0 and 92.7 cm3. This variance was primarily influenced by kVp, kernel, keV, and iMAR (R2 = 0.989). Radiologists favored pacemaker iMAR, 3 mm slice thickness, and T3D keV and kernel Bv56f for minimal metal artifacts in cardiac tissue assessment, and 110 keV and Qr40f for lung tissue interpretation. The model adequacy for DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was 0.28, 0.76, 0.29, and 0.99 respectively for phantom data, and 0.95, 0.98, 1.00, and 0.99 for in-vivo data. For in-vivo data, the correlation between visual grading (VGSUM) and DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was -0.16, -0.01, -0.48, and -0.40 respectively. CONCLUSION: We found that optimal scan settings for LVAD imaging involved using 120 kVp and IQ level 80. Employing T3D with pacemaker iMAR, the sharpest allowed vascular kernel (Bv56f), and VMI at 110 keV with kernel Qr40 yields images suitable for cardiac imaging during LVAD-therapy. Volumetric measurements of the LVAD for determination of the extent of blooming artifacts was shown to be the best objective method to assess metal artifacts.

Radiographic and fluoroscopic X-ray systems: Quality control of the X-ray tube and automatic exposure control using theoretical spectra to determine air kerma and dose to a homogenous phantom.

PURPOSE: To develop a method to perform quality control (QC) of X-ray tubes and automatic exposure control (AEC) as a part of the QC of the radiographic and fluoroscopic X-ray system. Our aim is to verify the output from the X-ray tube by comparing the measured radiation output, or air kerma, to the theoretical output given the applied exposure settings and geometry, in addition to comparing the measured kV to the nominal kV. The AEC system for fluoroscopic and conventional X-ray systems is assessed by determining the absorbed dose to a homogenous phantom with different thicknesses. METHOD: This study presents a model to verify the X-ray tube measurement results and a method to determine the dose to a homogenous phantom (Dphantom ). The following input is needed: a parameterized model of the X-ray spectrum, the X-ray tube measurements using a multifunctional X-ray meter, the exposure parameters recorded via imaging of polymethyl methacrylate (PMMA) slabs of different thickness that simulate the patient using AEC, and a parameterized model for calculating the dose to water from Monte Carlo simulations. The output is the entrance surface dose (ESD) and absorbed dose in the phantom, Dphantom (µGy). In addition, the parameterized X-ray spectrum is used to compare theoretical and measured air kerma as a part of the QC of the X-ray tube. To verify the proposed method, the X-ray spectrum provided in this study, SPECTRUM, was compared to two commercially available spectra, SpekCalc and Institute of Physics and Engineering in Medicine (IPEM) 78. The fraction of energy imparted to the homogenous phantom was compared to the imparted fraction calculated by PCXMC. RESULTS: The spectrum provided in this study was in good agreement with two previously published X-ray spectra. The absolute percentage differences of the spectra varied from 0.05% to 3.9%, with an average of 1.4%, compared to SpekCalc. Similarly, the deviation from IPEM report 78 varied from 0.02% to 2.3%, with an average of 0.74%. The SPECTRUM was parameterized for calculation of the imparted fraction for target angles of 10°, 12°, and 15°, kV (50-150 kV) with the materials Al (2.2-8 mm), Cu (0-1 mm), and any combination of the filters, PMMA and water. The deviation of energy imparted from the results by PCXMC was less than 8% for all measurements across different kV, filtration, and vendors, obtained by using PMMA to record the exposure parameters, while the dose was calculated based on water with same thicknesses as the PMMA. CONCLUSION: This study presents an accurate and suitable method to perform a part of the QC of fluoroscopic and conventional X-ray systems with respect to the X-ray tube and the associated AEC system. The method is suitable for comparing protocols within and between systems via the absorbed dose.

Novel method to determine recursive filtration and noise reduction in fluoroscopic imaging - a comparison of four different vendors.

PURPOSE: This study attempted to develop a method to measure the applied recursive filtration and to determine the noise reduction of four different fluoroscopic systems. The study also attempted to elucidate the importance of considering the recursive filter for quality control tests concerning signal-to-noise ratio (SNR) or image quality. The vendor's settings for recursive filtration factor (ß) are, unfortunately, often not available. Hence, a method to determine the recursive filtration and associated noise reduction would be useful. METHOD: The recursive filter was determined by using a single fluoroscopic series and the method presented in this study. The theoretical noise reduction based on the choice of ß was presented. In addition, the corresponding noise reduction, evaluated as the ratio of the standard deviation of the pixel value between a series with ß equal to zero (recursive filtration off) and ß > 0, was determined for different pulse rates given by pulses per second (pps), doses (mAs) and recursive filter. The images were acquired using clinically relevant radiation quality and quantity. RESULTS: The presented method to measure the recursive filter exhibited high accuracy (1.08%) and precision (1.48%). The recursive filtration and noise reduction were measured for several settings for each vendor. The recursive filtration settings and associated recursive filtration factors for four different vendors were presented. CONCLUSIONS: This study presented an accurate method to determine applied recursive filtration, which was easy to determine. Hence, for all quality control purposes, including noise evaluation, it was possible to consider the essential noise reduction given by the settings for recursive filtration. It was also possible to compare the recursive filtration settings and associated recursive filtration within and between vendors.

Image quality and radiation dose in planar imaging - Image quality figure of merits from the CDRAD phantom.

PURPOSE: A contrast-detail phantom such as CDRAD is frequently used for quality assurance, optimization of image quality, and several other purposes. However, it is often used without considering the uncertainty of the results. The aim of this study was to assess two figure of merits (FOM) originating from CDRAD regarding the variations of the FOMs by dose utilized to create the x-ray image. The probability of overlapping (assessing an image acquired at a lower dose as better than an image acquired at a higher dose) was determined. METHODS: The CDRAD phantom located underneath 12, 20, and 26 cm PMMA was imaged 16 times at five dose levels using an x-ray system with a flat-panel detector. All images were analyzed by CDRAD Analyser, version 1.1, which calculated the FOM inverse image quality figure (IQFinv ) and gave contrast detail curves for each image. Inherent properties of the CDRAD phantom were used to derive a new FOM h, which describes the size of the hole with the same diameter and depth that is just visible. Data were analyzed using heteroscedastic regression of mean and variance by dose. To ease interpretation, probabilities for overlaps were calculated assuming normal distribution, with associated bootstrap confidence intervals. RESULTS: The proportion of total variability in IQFinv , explained by the dose (R2 ), was 91%, 85%, and 93% for 12, 20, and 26 cm PMMA. Corresponding results for h were 91%, 89%, and 95%. The overlap probability for different mAs levels was 1% for 0.8 vs 1.2 mAs, 5% for 1.2 vs 1.6 mAs, 10% for 1.6 vs 2.0 mAs, and 10% for 2.0 mAs vs 2.5 mAs for 12 cm PMMA. For 20 cm PMMA, it was 0.5% for 10 vs 16 mAs, 13% for 16 vs 20 mAs, 14% for 20 vs 25 mAs, and 14% for 25 vs 32 mAs. For 26 cm PMMA, the probability varied from 0% to 6% for various mAs levels. Even though the estimated probability for overlap was small, the 95% confidence interval (CI) showed relatively large uncertainties. For 12 cm PMMA, the associated CI for 0.8 vs 1.2 mAs was 0.1-3.2%, and the CI for 1.2 vs 1.6 mAs was 2.1-7.8%. CONCLUSIONS: Inverse image quality figure and h are about equally related to dose level. The FOM h, which describes the size of a hole that should be seen in the image, may be a more intuitive FOM than IQFinv . However, considering the probabilities for overlap and their confidence intervals, the FOMs deduced from the CDRAD phantom are not sensitive to dose. Hence, CDRAD may not be an optimal phantom to differentiate between images acquired at different dose levels.

Reject analysis in direct digital radiography.

BACKGROUND: Reject analysis can be used as a quality indicator, and is an important tool in localizing areas where optimization is required. Reducing number of rejects is important yielding reduced patient exposure and increased cost-effectiveness. PURPOSE: To determine rejection rates and causes in direct digital radiography. MATERIAL AND METHODS: Data were collected during a three-month period in spring 2010 at two direct digital laboratories in Norway. All X-ray examinations, types, numbers, and reasons for rejections were obtained using automatic reject analysis software. Thirteen causes for rejection could be selected. RESULTS: Out of the 27,284 acquired images, 3206 were rejected, yielding an overall rejection rate of 12%. Highest rejection rates were found for examination of knees, shoulders, and wrist. In all, 77% of the rejected images arose from positioning errors. CONCLUSION: An overall rejection rate of 12% indicates a need for optimizing radiographic practice in the department.

A novel technique for determination of two dimensional signal-to-noise ratio improvement factor of an antiscatter grid in digital radiography.

PURPOSE: to present a new and simplified method for pixel-wise determination of the signal-to-noise ratio improvement factor KSNR of an antiscatter grid, when used with a digital imaging system. The method was based on approximations of published formulas. The simplified estimate of K(2)SNR may be used as a decision tool for whether or not to use an antiscatter grid. METHODS: the primary transmission of the grid Tp was determined with and without a phantom present using a pattern of beam stops. The Bucky factor B was measured with and without a phantom present. Hence K(2)SNR maps were created based on Tp and B. A formula was developed to calculate K(2)SNR from the measured Bs without using the measured Tp. The formula was applied on two exposures of anthropomorphic phantoms, adult legs and baby chest, and on two homogeneous poly[methyl methacrylate] (PMMA) phantoms, 5 cm and 10 cm thick. The results from anthropomorphic phantoms were compared to those based on the beam stop method. The results for the PMMA-phantoms were compared to a study that used a contrast-detail phantom. RESULTS: 2D maps of K(2)SNR over the entire adult legs and baby chest phantoms were created. The maps indicate that it is advantageous to use the antiscatter grid for imaging of the adult legs. For baby chest imaging the antiscatter grid is not recommended if only the lung regions are of interest.The K(2)SNR maps based on the new method correspond to those from the beam stop method, and the K(2)SNR from the homogenous phantoms arising from two different approaches also agreed well with each other. CONCLUSION: a method to measure 2D K(2)SNR associated with grid use in digital radiography system was developed and validated. The proposed method requires four exposures and use of a simple formula. It is fast and provides adequate estimates for K(2)SNR.