OBJECTIVE: To evaluate the efficiency of organ-based tube current modulation (OBTCM) in head Computed Tomography (CT) for different radiology departments and manufacturers.
METHODS: Five CT scanners from four radiology departments were evaluated in this study. All scans were performed using a standard and a routine head protocol. A scintillating fiber optic detector was placed directly on the gantry to measure the tube exit kerma. Image quality was quantified on a 16-cm HEAD phantom by measuring the signal-to-noise ratio (SNR) and the standard deviation of the Hounsfield units (HU) of circular regions of interest placed in the phantom. The Noise Power Spectrum (NPS) was also studied. Measured values were compared on images with and without OBTCM.
RESULTS: The reduction rates in tube exit kerma, on the anterior part, vary between 11 % and 74 % depending on the CT scanner and the protocol used. The tube exit kerma on the posterior part remains unchanged in GE and Canon CT scanners. On the contrary, the tube exit kerma to the posterior part increases by up to 39 % in Siemens CT scanner. Image noise and SNR increase by up to 10 % in the five CT scanners. Nonetheless, the differences in noise and SNR are statistically significant (p-value < 0.05).The analysis of the NPS indicates that the noise texture remains unchanged.
CONCLUSIONS: OBTCM reduces the tube exit kerma to the anterior part of the gantry without reducing substantially image quality for head protocols.

In this study, an evaluation of the compliance test data from 684 computed tomography (CT)-scanners in Indonesia for the 2019-22 test period was carried out. The study was aimed to describe the performance profile of CT-scanners in Indonesia and evaluate the testing protocol. A total of 87.8% of the CT-scanners unconditionally passed the tests, 8.8% passed the tests with conditions and 3.4% failed the tests. Of the devices conditionally passed the tests, the top two causes were water CT number accuracy (45.2%) and laser position accuracy (41.9%). Meanwhile, 75.0% of the failed devices were due to failing to meet the patient dose test criteria. The failure of the test for the water CT number accuracy parameter was caused by variations in the type of phantom used in the test, where several types of phantoms did not use water as material of the homogeneity module. Failures in laser position accuracy test were caused by the passing criteria that adjust to the minimum slice thickness, so that modern CT-scanner with small detector sizes and collimations tend not to pass. On the other hand, the failure on dose aspects was due to the frequent unavailability of baseline values for comparison. Of these top three failure causes, two of them, namely the CT number and dose test parameters, have been accommodated in the latest regulation (BAPETEN Regulation No. 2/2022) with a change in the evaluation method, while for the laser position accuracy test it is recommended to alter the passing criteria to an absolute value, namely 1 mm.

We introduce a new calibration method for dual energy CT (DECT) based on material decomposition (MD) maps, specifically iodine and water MD maps. The aim of this method is to provide the first DECT calibration based on MD maps. The experiments were carried out using a general electric (GE) revolution CT scanner with ultra-fast kV switching and used a density phantom by GAMMEX for calibration and evaluation. The calibration process involves several steps. First, we tested the ability of MD values to reproduce Hounsfield unit (HU) values of single energy CT (SECT) acquisitions and it was found that the errors were below 1%, validating their use for HU reproduction. Next, the different definitions of computedZvalues were compared and the robustness of the approach based on the materials\' composition was confirmed. Finally, the calibration method was compared with a previous method by Bourqueet al, providing a similar level of accuracy and superior performance in terms of precision. Overall, this novel DECT calibration method offers improved accuracy and reliability in determining tissue-specific physical properties. The resulting maps can be valuable for proton therapy treatments, where precise dose calculations and accurate tissue differentiation are crucial for optimal treatment planning and delivery.

UNASSIGNED: Photon-counting computed tomography (PCCT) offers better high-resolution and noise performance than energy integrating detector (EID) CT. In this work, we compared both technologies for imaging of the temporal bone and skull base. A clinical PCCT system and 3 clinical EID CT scanners were used to image the American College of Radiology image quality phantom using a clinical imaging protocol with matched CTDI vol (CT dose index-volume) of 25 mGy. Images were used to characterize the image quality of each system across a series of high-resolution reconstruction options. Noise was calculated from the noise power spectrum, whereas resolution was quantified with a bone insert by calculating a task transfer function. Images of an anthropomorphic skull phantom and 2 patient cases were examined for visualization of small anatomical structures. Across measured conditions, PCCT had a comparable or smaller average noise magnitude (120 Hounsfield units [HU]) to the EID systems (144-326 HU). Photon-counting CT also had comparable resolution (task transfer function f25 : 1.60 mm -1 ) to the EID systems (1.34-1.77 mm -1 ). Imaging results supported quantitative findings as PCCT more clearly showed the 12-lp/cm bars from the fourth section of the American College of Radiology phantom and better represented the vestibular aqueduct and oval and round windows when compared with the EID scanners. A clinical PCCT system was able to image the temporal bone and skull base with improved spatial resolution and lower noise than clinical EID CT systems at matched dose.

OBJECTIVE: To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures.
METHODS: Imaging Biomarker Explorer (IBEX) expansion for the abbreviation IBEX extracted 51 radiomic features of 4 categories from 11 textures image regions of interest (ROI) of the phantom. 19 software pre-processing algorithms processed each CCR phantom ROI. All ROI texture processed image features were retrieved. Pre-processed CT image radiomic features were compared to non-processed features to measure its textural influence. Wilcoxon T-tests measured the pre-processing relevance of CT radiomic features on various textures. Hierarchical cluster analysis (HCA) was performed to cluster processer potency and texture impression likeness.
RESULTS: The pre-processing filter, CT texture Cartridge, and feature category affect the CCR phantom CT image\'s radiomic properties. Pre-processing is statistically unaltered by Gray Level Run Length Matrix (GLRLM ) expansion  for the abbreviation GLRLM and Neighborhood Intensity Difference matrix (NID) expansion for the abbreviation NID feature categories. The 30%, 40%, and 50% honeycomb are regular directional textures and smooth 3D-printed plaster resin, most of the image pre-processing feature alterations exhibited significant p-values in the histogram feature category. The Laplacian Filter, Log Filter, Resample, and Bit Depth Rescale Range pre-processing algorithms hugely influenced histogram and Gray Level Co-occurrence Matrix (GLCM) image features.
CONCLUSIONS: We found that homogenous intensity phantom inserts, CT radiomic feature, are less sensitive to feature swaps during pre-processing than normal directed honeycomb and regular projected smooth 3D-printed plaster resin CT image textures. Because they lose fewer information during image enhancement, This feature concentration empowerment of the images also enhances texture pattern recognition.

OBJECTIVE: The purpose of this study was to assess the impact of a tin filter on the image quality of ultra-low dose (ULD) chest computed tomography (CT) on three different CT systems.
METHODS: An image quality phantom was scanned on three CT systems including two split-filter dual-energy CT (SFCT-1 and SFCT-2) scanners and one dual-source CT scanner (DSCT). Acquisitions were performed with a volume CT dose index (CTDIvol) of 0.4 mGy, first at 100 kVp without tin filter (Sn), and second, at Sn100/Sn140 kVp, Sn100/Sn110/Sn120/Sn130/Sn140/Sn150 kVp and Sn100/Sn150 kVp for SFCT-1, SFCT-2 and DSCT respectively. Noise-power-spectrum and task-based transfer function were computed. The detectability index (d\') was computed to model the detection of two chest lesions.
RESULTS: For DSCT and SFCT-1, noise magnitude values were higher with 100kVp than with Sn100 kVp and with Sn140 kVp or Sn150 kVp than with Sn100 kVp. For SFCT-2, noise magnitude increased from Sn110 kVp to Sn150 kVp and was higher at Sn100 kVp than at Sn110 kVp. For most kVp with the tin filter, the noise amplitude values were lower than those obtained at 100 kVp. For each CT system, noise texture and spatial resolution values were similar with 100 kVp and with all kVp used with a tin filter. For all simulated chest lesions, the highest d\' values were obtained at Sn100 kVp for SFCT-1 and DSCT and at Sn110 kVp for SFCT-2.
CONCLUSIONS: For ULD chest CT protocols, the lowest noise magnitude and highest detectability values for simulated chest lesions are obtained with Sn100 kVp for the SFCT-1 and DSCT CT systems and at Sn110 kVp for SFCT-2.

Objectives: The purpose of this study was to assess the feasibility of the adoption of a machine learning (ML) algorithm in support of the investment decisions regarding high cost medical devices based on available clinical and epidemiological evidence. Methods: Following a literature search, the set of epidemiological and clinical need predictors was established. Both the data from The Central Statistical Office and The National Health Fund were used. An evolutionary algorithm (EA) model was developed to obtain the prediction of the need for CT scanners across local counties in Poland (hypothetical scenario). The comparison between the historical allocation and the scenario developed by the EA model based on epidemiological and clinical need predictors was established. Only counties with available CT scanners were included in the study. Results: In total, over 4 million CT scan procedures performed across 130 counties in Poland between 2015 and 2019 were used to develop the EA model. There were 39 cases of agreement between historical data and hypothetical scenarios. In 58 cases, the EA model indicated the need for a lower number of CT scanners than the historical data. A greater number of CT procedures required compared with historical use was predicted for 22 counties. The remaining 11 cases were inconclusive. Conclusions: Machine learning techniques might be successfully applied to support the optimal allocation of limited healthcare resources. Firstly, they enable automatization of health policy making utilising historical, epidemiological, and clinical data. Secondly, they introduce flexibility and transparency thanks to the adoption of ML to investment decisions in the healthcare sector as well.

UNASSIGNED: The diagnosis accuracy of computed tomography (CT) systems and the reliability of calculated Hounsfield Units (HUs) are critical in tumor detection and cancer patients\' treatment planning. This study evaluated the effects of scan parameters (Kilovoltage peak or kVp, milli-Ampere-second or mAS reconstruction kernels and algorithms, reconstruction field of view, and slice thickness) on image quality, HUs, and the calculated dose in the treatment planning system (TPS).
UNASSIGNED: A quality dose verification phantom was scanned several times by a 16-slice Siemens CT scanner. The DOSIsoft ISO gray TPS was applied for dose calculations. The SPSS.24 software was used to analyze the results and the P-value <0.05 was considered significant.
UNASSIGNED: Reconstruction kernels and algorithms significantly affected noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The noise increased and CNR decreased by raising the sharpness of reconstruction kernels. SNR and CNR had considerable increments at iterative reconstruction compared with the filtered back-projection algorithm. The noise decreased by raising mAS in soft tissues. Also, KVp had a significant effect on HUs. TPS--calculated dose variations were less than 2% for mediastinum and backbone and less than 8% for rib.
UNASSIGNED: Although HU variation depends on image acquisition parameters across a clinically feasible range, its dosimetric impact on the calculated dose in TPS can be neglected. Hence, it can be concluded that the optimized values of scan parameters can be applied to obtain the maximum diagnostic accuracy and calculate HUs more precisely without affecting the calculated dose in the treatment planning of cancer patients.

Objective. The purpose of this study is to assess its human images and its unique capabilities such as the \'on demand\' higher spatial resolution and multi-spectral imaging of photon-counting-detector (PCD)-CT.Approach. In this study, the FDA 510(k) cleared mobile PCD-CT (OmniTom Elite) was used. To this end, we imaged internationally certified CT phantoms and a human cadaver head to evaluate the feasibility of high resolution (HR) and multi-energy imaging. We also demonstrate the performance of PCD-CT via first-in-human imaging by scanning three human volunteers.Main results. At the 5 mm slice thickness, routinely used in diagnostic head CT, the first human PCD-CT images were diagnostically equivalent to the EID-CT scanner. The HR acquisition mode of PCD-CT achieved a resolution of 11 line-pairs (lp)/cm as compared to 7 lp cm-1using the same kernel (posterior fossa-kernel) in the standard acquisition mode of EID-CT. For the quantitative multi-energy CT performance, the measured CT numbers in virtual mono-energetic images (VMI) of iodine inserts in the Gammex Multi-Energy CT phantom (model 1492, Sun Nuclear Corporation, USA) matched the manufacturer reference values with mean percent error of 3.25%. Multi-energy decomposition with PCD-CT demonstrated the separation and quantification of iodine, calcium, and water.Significance. PCD-CT can achieve multi-resolution acquisition modes without physically changing the CT detector. It can provide superior spatial resolution compared with the standard acquisition mode the conventional mobile EID-CT. Quantitative spectral capability of PCD-CT can provide accurate, simultaneous multi-energy images for material decomposition and VMI generation using a single exposure.

OBJECTIVE: The aim was to evaluate the effect of a combined precision matrix and high sampling rate on the delineation of anatomical structures and objective image quality in single source CT in a qualitative approach.
METHODS: An anthropomorphic thoracic phantom was used to evaluate the objective image quality parameters, including image noise, noise power spectrum, image stepness and Q for different CT scanners including high/standard matrix and framing frequency setups. Scan parameters were standardized over all scanners. Additional subjective quality assessment was also performed.
RESULTS: A linear mixed effects model was used to determine the effect of sampling rate and image matrix on objective image quality parameters. Noise power spectrum and image noise were significantly influenced by both framing frequency and image matrix. There were significant differences between high and standard frequency/matrix acquisitions.
CONCLUSIONS: Higher framing frequency and image matrix allows for improved image noise texture and objective image quality in CT.