How to measure CT image quality: Variations in CT-numbers, uniformity and low contrast resolution for a CT quality assurance phantom

Improving Hounsfield Unit accuracy in dental CBCT through the integration of 2D anti-scatter grid

OBJECTIVE

Lack of Hounsfield Unit (HU) accuracy leads to misrepresentation of tissue densities and causes image artifacts in dental cone beam computed tomography (CBCT) images. This work investigates the improvement in HU accuracy in dental CBCT by suppressing scatter with a novel two-dimensional anti-scatter grid (2D ASG) approach.

METHODS

A 2D ASG prototype was developed and integrated into an experimental CBCT system, emulating dental CBCT geometry. CBCT scans were acquired using a multidetector CT (MDCT), a clinical dental CBCT, and the proposed 2D ASG-based experimental CBCT system. Subsequently, HU accuracy, nonuniformity, and contrast-to-noise ratio (CNR) were evaluated in image quality phantoms benchmarked against MDCT images. The effect of scatter suppression on the implant-induced HU degradation was also studied.

RESULTS

2D ASG reduced scatter content up to a factor of 6.6 in CBCT scans and HU nonuniformity in soft tissue-like regions was reduced from 23 to 4 HU. HU errors in high-density structures were within 65 HU of values measured in the gold standard MDCT images. Robust scatter suppression also reduced streak artifacts caused by implants. CNR increased up to 47% in images acquired with the 2D ASG.

CONCLUSION

HU accuracy in dental CBCT may reach the accuracy of MDCT with the proposed 2D ASG. Moreover, artifacts are reduced, and contrast visualization can be improved. Hence, this approach may enable a more accurate assessment of tissue densities in CBCT images, and improved image quality may positively impact the diagnostic capabilities of dental CBCT systems.

Accuracy of Volumetric Bone Mineral Density Measurement in Weight Bearing, Cone Beam Computed Tomography

BACKGROUND

Weight bearing computed tomography (WBCT) utilizes cone beam CT technology to provide assessments of lower limb joint structures while they are functionally loaded. Grey-scale values indicative of X-ray attenuation that are output from cone beam CT are challenging to calibrate, and their use for bone mineral density (BMD) measurement remains debatable. To determine whether WBCT can be reliably used for cortical and trabecular BMD assessment, we sought to establish the accuracy of BMD measurements at the knee using modern WBCT by comparing them to measurements from conventional CT.

METHODS

A hydroxyapatite phantom with three inserts of varying densities was used to systematically quantify signal uniformity and BMD accuracy across the acquisition volume. We evaluated BMD in vivo (n = 5, female) using synchronous and asynchronous calibration techniques in WBCT and CT. To account for variation in attenuation along the height (z-axis) of acquisition volumes, we tested a height-dependent calibration approach for both WBCT and CT images.

RESULTS

Phantom BMD measurement error in WBCT was as high as 15.3% and consistently larger than CT (up to 5.6%). Phantom BMD measures made under synchronous conditions in WBCT improved measurement accuracy by up to 3% but introduced more variability in measured BMD. We found strong correlations (R = 0.96) as well as wide limits of agreement (-324 mgHA/cm3 to 183 mgHA/cm3) from Bland-Altman analysis between WBCT and CT measures in vivo that were not improved by height-dependent calibration.

CONCLUSION

Whilst BMD accuracy from WBCT was found to be dependent on apparent density, accuracy was independent of the calibration technique (synchronous or asynchronous) and the location of the measurement site within the field of view. Overall, we found strong correlations between BMD measures from WBCT and CT and in vivo measures to be more accurate in trabecular bone regions. Importantly, WBCT can be used to distinguish between anatomically relevant differences in BMD, however future work is necessary to determine the repeatability and sensitivity of BMD measures in WBCT.

The Dose Optimization and Evaluation of Image Quality in the Adult Brain Protocols of Multi-Slice Computed Tomography: A Phantom Study

Computed tomography examinations have caused high radiation doses for patients, especially for CT scans of the brain. This study aimed to optimize the radiation dose and image quality in adult brain CT protocols. Images were acquired using a Catphan 700 phantom. Radiation doses were recorded as CTDIvol and dose length product (DLP). CT brain protocols were optimized by varying parameters such as kVp, mAs, signal-to-noise ratio (SNR) level, and Clearview iterative reconstruction (IR). The image quality was also evaluated using AutoQA Plus v.1.8.7.0 software. CT number accuracy and linearity had a robust positive correlation with the linear attenuation coefficient (µ) and showed more inaccurate CT numbers when using 80 kVp. The modulation transfer function (MTF) showed a higher value in 100 and 120 kVp protocols (p < 0.001), while high-contrast spatial resolution showed a higher value in 80 and 100 kVp protocols (p < 0.001). Low-contrast detectability and the contrast-to-noise ratio (CNR) tended to increase when using high mAs, SNR, and the Clearview IR protocol. Noise decreased when using a high radiation dose and a high percentage of Clearview IR. CTDIvol and DLP were increased with increasing kVp, mAs, and SNR levels, while the increasing percentage of Clearview did not affect the radiation dose. Optimized protocols, including radiation dose and image quality, should be evaluated to preserve diagnostic capability. The recommended parameter settings include kVp set between 100 and 120 kVp, mAs ranging from 200 to 300 mAs, SNR level within the range of 0.7-1.0, and an iterative reconstruction value of 30% Clearview to 60% or higher.

Free automatic software for quality assurance of computed tomography calibration, edges and radiomics metrics reproducibility

PURPOSE

To develop a QA procedure, easy to use, reproducible and based on open-source code, to automatically evaluate the stability of different metrics extracted from CT images: Hounsfield Unit (HU) calibration, edge characterization metrics (contrast and drop range) and radiomic features.

METHODS

The QA protocol was based on electron density phantom imaging. Home-made open-source Python code was developed for the automatic computation of the metrics and their reproducibility analysis. The impact on reproducibility was evaluated for different radiation therapy protocols, and phantom positions within the field of view and systems, in terms of variability (Shapiro-Wilk test for 15 repeated measurements carried out over three days) and comparability (Bland-Altman analysis and Wilcoxon Rank Sum Test or Kendall Rank Correlation Coefficient).

RESULTS

Regarding intrinsic variability, most metrics followed a normal distribution (88% of HU, 63% of edge parameters and 82% of radiomic features). Regarding comparability, HU and contrast were comparable in all conditions, and drop range only in the same CT scanner and phantom position. The percentages of comparable radiomic features independent of protocol, position and system were 59%, 78% and 54%, respectively. The non-significantly differences in HU calibration curves obtained for two different institutions (7%) translated in comparable Gamma Index G (1 mm, 1%, >99%).

CONCLUSIONS

An automated software to assess the reproducibility of different CT metrics was successfully created and validated. A QA routine proposal is suggested.

Effects of convolution filter with beam hardening correction on computed tomography image quality

PURPOSE

To quantify the effects of convolution filters (FC) with beam hardening correction (BHC) compared to FC without BHC on the computed tomography (CT) image quality.

METHODS

This study was conducted on a Canon® Aquilion Lightning scanner. The exposure protocol includes acquisitions at 120 and 100 kVp. Sixteen FCs (8 with and 8 without BHC) were investigated using a Catphan®504 phantom. Uniformity, slice thickness, spatial resolution, Hounsfield unit and noise were analysed using the SPICE-CT ImageJ plugin and the noise power spectrum was analysed using the Imquest software.

RESULTS

It was observed that the BHC did not significantly influence the uniformity, slice thickness, noise and noise power spectrum. Comparisons of 10% MTF between FC01 and FC11 showed relative differences of -29% and -5% at 120 and 100 kVp, respectively, while those between FC09 and FC19 were -55% and -25%. The Hounsfield unit of the Catphan's region of highest electron density was reduced by -7.29% at 120 kVp for FC with BHC. In both cases (FC with and without BHC), the noise values agreed with CT operating manual. At 120 kVp, FC11 and FC09 presented the maximum and minimum noise values, respectively.

CONCLUSION

In CT procedures that quantitatively evaluate the bone or calcium Hounsfield unit, FC with BHC should be avoided due to its effects on Hounsfield units, in special at higher voltage, such as 120 kVp.

µ-CT Investigation of Hydrogen-Induced Cracks and Segregation Effects in Austenitic Stainless Steel

Hydrogen can drastically degrade the mechanical properties of a variety of metallic materials. The so-called hydrogen environment embrittlement of austenitic CrNi-type steels is usually accompanied by the formation of secondary surface cracks, which can be investigated in order to assess the embrittlement process. The occurrence of hydrogen-induced cracks is often related to element segregation effects that locally impact the austenite stability. Since there is as yet a lack of investigation methods that can visualize both structures three-dimensionally, the present study investigates the imageability of hydrogen-induced cracks and element segregation structures in austenitic CrNi-steel via micro-computed tomography (CT). In order to improve the X-ray visibility of segregation structures, modified versions of the reference steel, X2CrNi18-9, that contain W and Si are designed and investigated. The investigations demonstrated that small differences in the X-ray attenuation, caused by the W or Si modifications, can be detected via CT, although segregation structures could not be imaged due to their small size scale and image noise. Hydrogen-induced cracks were characterized successfully; however, the detection of the smaller cracks is limited by the resolution capability.

The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images

This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians’ diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.

Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images

Purpose

We have developed a software to automatically find the contrast–detail (C–D) curve based on the statistical low‐contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics.

Methods

A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2‐pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C–D curve was obtained and the target size at an MDC of 0.6% (i.e., 6‐HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer.

Results

The developed system was able to measure C–D curves from different phantoms and scanners. We found that the C–D curves follow a power‐law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low‐contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD.

Conclusions

The statistical LCD measurement method can generate a C–D curve automatically, quickly, and objectively.

Hardware Optimizations of the X-ray Pre-Processing for Interventional Computed Tomography Using the FPGA

In computed tomography imaging, the computationally intensive tasks are the pre-processing of 2D detector data to generate total attenuation or line integral projections and the reconstruction of the 3D volume from the projections. This paper proposes the optimization of the X-ray pre-processing to compute total attenuation projections by avoiding the intermediate step to convert detector data to intensity images. In addition, to fulfill the real-time requirements, we design a configurable hardware architecture for data acquisition systems on FPGAs, with the goal to have a “on-the-fly” pre-processing of 2D projections. Finally, this architecture was configured for exploring and analyzing different arithmetic representations, such as floating-point and fixed-point data formats. This design space exploration has allowed us to find the best representation and data format that minimize execution time and hardware costs, while not affecting image quality. Furthermore, the proposed architecture was integrated in an open-interface computed tomography device, used for evaluating the image quality of the pre-processed 2D projections and the reconstructed 3D volume. By comparing the proposed solution with the state-of-the-art pre-processing algorithm that make use of intensity images, the latency was decreased 4.125×, and the resources utilization of ∼6.5×, with a mean square error in the order of 10−15 for all the selected phantom experiments. Finally, by using the fixed-point representation in the different data precisions, the latency and the resource utilization were further decreased, and a mean square error in the order of 10−1 was reached.

The impacts of vertical off-centring, tube voltage, and phantom size on computed tomography numbers: An experimental study

INTRODUCTION

This experimental study explored the effect of vertical off-centring on computed tomography (CT) numbers in combination with various tube voltages and phantom sizes for two CT units.

METHODS

CIRS Model 062 Electron Density and system performance phantoms were imaged on Siemens Emotion 16-slice CT and GEMINI-GXL scanners, respectively. Uniformity and accuracy were evaluated as a function of vertical off-centring (20, 40, 60, and 80 mm above the gantry isocentre) using different water phantom sizes (18, 20, and 30 cm) and tube voltages (80, 90, 110, 120, 130 and 140 kVp).

RESULTS

Vertical off-centring and phantom size accounted for 92% of the recorded variance and the resultant change in CT numbers. The uniformity test recorded maximum changes of 14 and 27.2 HU for peripheral ROIs across the X- and Y-axes for an 80 mm phantom shift above the gantry isocentre on the GEMINI GXL and Siemens scanners, respectively. The absolute CT number differences between the superior and inferior ROIs were 13.7 HU for the 30 cm phantom and 4.8 HU for the 20 cm phantom for 80 mm vertical off-centring. The largest differences were observed at lower tube voltages.

CONCLUSIONS

It is essential to highlight the significance of CT number variation in clinical decision-making. Phantom off-centring affected the uniformity of these numbers, which were further impacted by the ROI position in this experimental study. CT number variation was more evident in peripheral phantom areas, lower tube voltages and larger phantom sizes.

IMPLICATIONS FOR PRACTICE

CT number is observed to be a variable under certain common conditions. This significantly impacts several applications where clinical decisions depend on CT number accuracy for tissue lesion characterisation.

Feasibility of a method for low contrast CT image quality assessment using difference detail curves for abdominal scans

This work describes a measurement method for assessing dose-related image-quality of CT scans based on the difference detail curve (DDC) method, and showcases its use in a low contrast setting. The method is based on a phantom consisting of elliptical slices of different sizes into which contrast object modules can be inserted. These modules contain contrast objects based on (synthetic) resin mixtures with sucrose (native) or sodium iodine (contrast medium). Mixing ratios are provided to achieve a range of clinically relevant CT-numbers with these materials. The phantom is characterized in terms of contrast accuracy, energy dependency and long-term drift with satisfying results. Contrast accuracy and energy dependency are similar to that of water or soft tissue. Image quality of 655 scans of the phantom acquired at 30 different clinical institutions and with 16 different CT scanner models from 4 manufacturers was assessed by calculating a difference detail curve (DDC) from evaluation of up to 5 human observers using a custom-made software (RadiVates) described in this work. Based on these measurements, inter-observer variability was quantified using a bootstrap method and was shown to be a large contributor to the overall variability. This work demonstrates that assessment of CT image quality is feasible with the aforementioned phantom and DDC method.

Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study

BACKGROUND AND PURPOSE

To investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening by deep-learning image reconstruction (DLIR) based on a multi-phantom and patient study.

MATERIALS AND METHODS

High-contrast spatial resolution, low-contrast detectability, modulation function test (MTF), noise power spectrum (NPS), and image noise were evaluated for physical image quality on Caphan 500 phantom. Three calcium hydroxyapatite (HA) inserts were used for accurate BMD measurement on European Spine Phantom (ESP). CT images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction-veo 50% (ASiR-V50%), and three levels of DLIR(L/M/H). Subjective evaluation of the image high-contrast spatial resolution and low-contrast detectability were compared visually by qualified radiologists, whilst the statistical difference in the objective evaluation of the image high-contrast spatial resolution and low-contrast detectability, image noise, and relative measurement error were compared using one-way analysis of variance (ANOVA). Cohen's kappa coefficient (k) was performed to determine the interobserver agreement in qualitative evaluation between two radiologists.

RESULTS

Overall, for three levels of DLIR, 50% MTF was about 4.50 (lp/cm), better than FBP (4.12 lp/cm) and ASiR-V50% (4.00 lp/cm); the 2 mm low-contrast object was clearly resolved at a 0.5% contrast level, while 3mm at FBP and ASiR-V50%. As the strength level decreased and radiation dose increased, DLIR at three levels showed a higher NPS peak frequency and lower noise level, leading to leftward and rightward shifts, respectively. Measured L1, L2, and L3 were slightly lower than that of nominal HA inserts (44.8, 95.9, 194.9 versus 50.2, 100.6, 199.2mg/cm³) with a relative measurement error of 9.84%, 4.08%, and 2.60%. Coefficients of variance for the L1, L2, and L3 HA inserts were 1.51%, 1.41%, and 1.18%. DLIR-M and DLIR-H scored significantly better than ASiR-V50% in image noise (4.83 ± 0.34, 4.50 ± 0.50 versus 4.17 ± 0.37), image contrast (4.67 ± 0.73, 4.50 ± 0.70 versus 3.80 ± 0.99), small structure visibility (4.83 ± 0.70, 4.17 ± 0.73 versus 3.83 ± 1.05), image sharpness (3.83 ± 1.12, 3.53 ± 0.90 versus 3.27 ± 1.16), and artifacts (3.83 ± 0.90, 3.42 ± 0.37 versus 3.10 ± 0.83). The CT value, image noise, contrast noise ratio, and image artifacts in DLIR-M and DLIR-H outperformed ASiR-V50% and FBP (P<0.001), whilst it showed no statistically significant between DLIR-L and ASiR-V50% (P>0.05). The prevalence of osteoporosis was 74 (24.67%) in women and 49 (11.79%) in men, whilst the osteoporotic vertebral fracture rate was 26 (8.67%) in women and (5.29%) in men.

CONCLUSION

Image quality with DLIR was high-qualified without affecting the accuracy of BMD measurement. It has a potential clinical utility in osteoporosis screening.

A phantom study comparing low-dose CT physical image quality from five different CT scanners

BACKGROUND

To systematically evaluate the physical image quality of low-dose computed tomography (LDCT) on CT scanners from 5 different manufacturers using a phantom model.

METHODS

CT images derived from a Catphan 500 phantom were acquired using manufacturer-specific iterative reconstruction (IR) algorithms and deep learning image reconstruction (DLIR) on CT scanners from 5 different manufacturers and compared using filtered back projection with 2 radiation doses of 0.25 and 0.75 mGy. Image high-contrast spatial resolution and image noise were objectively characterized by modulation transfer function (MTF) and noise power spectrum (NPS). Image high-contrast spatial resolution and image low-contrast detectability were compared directly by visual evaluation. CT number linearity and image uniformity were compared with intergroup differences using one-way analysis of variance (ANOVA).

RESULTS

The CT number linearity of 4 insert materials were as follows: acrylic (95% CI: 120.35 to 121.27; P=0.134), low-density polyethylene (95% CI: -98.43 to -97.43; P=0.070), air (95% CI: -996.16 to -994.51; P=0.018), and Teflon (95% CI: 984.40 to 986.87; P=0.883). The image uniformity values of GE Healthcare (95% CI: 3.24 to 3.83; P=0.138), Philips (95% CI: 2.62 to 3.70; P=0.299), Siemens (95% CI: 2.10 to 3.59; P=0.054), Minfound (95% CI: 2.35 to 3.65; P=0.589), and Neusoft (95% CI: 2.63 to 3.37; P=0.900) were evaluated and found to be within ±4 Hounsfield units (HU), with a range of 0.99-2.76 HU for standard deviations. There was no statistically significant difference in CT number linearity and image uniformity across the 5 CT scanners under different radiation doses with IR and DLIR algorithms (P>0.05). The resolution level at 10% MTF was 6.98 line-pairs-per-centimeter (lp/cm) on average, which was similar to the subjective evaluation results (mostly up to 7 lp/cm). DLIR at all 3 levels had the highest 50% MTF values among all reconstruction algorithms. For image low-contrast detectability, the minimum diameter of distinguishable contrast holes reached 4 mm at a 0.5% resolution. Increasing the radiation dose and IR strength reduced the image noise and NPS curve peak frequency while improving image low-contrast detectability.

CONCLUSIONS

This study demonstrated that the image quality of CT scanners from 5 different manufacturers in LDCT is comparable and that the CT number linearity is unbiased and can contribute to accurate bone mineral density quantification.

A NIM PET/CT phantom for evaluating the PET image quality of micro-lesions and the performance parameters of CT

Background

The commonly used NEMA IEC Body phantom has a number of defects, hindering its application for detecting micro-lesions and measuring the performance parameters of computed tomography (CT). This study aimed to propose a PET/CT phantom designed by National Institute of Metrology (NIM), China, which is capable of simultaneously testing the performance of PET and CT systems, and to evaluate the quality of imaging.

Methods

The phantom developed in the present study, the NIM PET/CT phantom, is composed of a PET imaging module and a CT imaging module, and these modules are connected together through bolts, which can simultaneously measure the imaging performance of PET and CT systems. Hot spheres were filled with 4:1 sphere-to-background activity concentration using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG), and cold spheres were filled with non-radioactive water. We compared the results of imaging obtained from the NIM PET/CT phantom and the NEMA IEC Body phantom to assess their diagnostic efficacy. In order to evaluate the generalization ability of the NIM PET/CT phantom, three different PET/CT systems were used to scan on the same scanning protocol. To evaluate the effects of image reconstruction algorithms on image quality assessment, ordered subset expectation maximization (OSEM), OSEM-point-spread function (PSF), OSEM-TOF, and OSEM-PSF-TOF algorithms were employed.

Results

The imaging quality of the NIM PET/CT phantom and the NEMA IEC Body phantom was relatively consistent. The NIM PET/CT phantom could detect 7 mm spheres without influencing the imaging quality. It was found that PSF reconstruction exhibited to reduce the speed of convergence, the contrast and background variability of spheres (13–28 mm) were significantly improved after two iterations. In addition to improve the image contrast and background variability, TOF could markedly improve the overall image quality and instrument detection limit. TOF-PSF could noticeably reduce noise level, enhance imaging details, and improve quality of imaging.

Conclusions

The results showed that in comparison with the NEMA IEC Body phantom, the NIM PET/CT phantom outperformed in evaluating the PET image quality of micro-lesions and the performance parameters of CT.

Effects of tube potential selection together with computed tomography automatic tube current modulation on CT imaging performance

The effects of tube potential selection with a computed tomography (CT) automatic tube current modulation (ATCM) system on radiation dose and image quality have been investigated on a Canon CT scanner. The use of different values of tube voltage for imaging, and the appropriate settings of the ATCM system, were evaluated. The custom-made phantom consisted of three sections of different sizes with inserts of various materials. It was scanned using tube potentials of 80-140 kV and different image quality ATCM settings. CTDI_voland image quality in terms of noise, contrast, and contrast-to-noise ratio (CNR) for air, polyethylene (PE), acrylic, polyoxymethylene (POM) and polyvinylchloride (PVC) were analysed. A figure of merit (FOM) was estimated by combining CNR and CTDI_vol. CTDI_volvalues were similar for all values of tube voltage and individual image quality ATCM settings when tube current was not restricted by the maximum value. The contrasts were independent of ATCM image quality setting, but CNR increased at the higher image quality level as image noise decreased. Both contrast and CNR decreased with increasing tube voltage for PVC and PE, but increased for POM and acrylic. PVC was the only insert material for which there was a significant improvement in contrast at lower tube potentials. FOM indicated that standard (SD = 10) and low dose (SD = 12.5) ATCM settings might be appropriate. The optimum tube voltage settings for imaging the PVC was 80-100 kV, but not for the lower contrast POM and acrylic, for which the standard tube voltage setting of 120 kV was better. The tube potential should be carefully set to gain radiological protection optimisation and keep the radiation dose as low as possible. Results indicate that 100 kV is likely to be appropriate for imaging small and medium-sized Thai patients when iodine contrast is used.

Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters

PURPOSE

Virtual monoenergetic images (VMI) obtained from Dual-Energy Computed Tomography (DECT) with iodinated contrast are used in radiotherapy of the Head and Neck to improve the delineation of target volumes and organs at-risk (OAR). The energies used to vary from 40 to 70 keV, but noise at low keV and the use of Single Energy CT (SECT) at low kV_p settings may shrink this interval. There is no guide about how to find out the optimal range where VMI has a significant improvement related to SECT images. Our study proposes a procedure to determine this optimal range, based on common image quality parameters, and establishes this range in a Siemens Somatom Confidence and a Head and Neck protocol.

METHODS

We compared the quality of the VMI series at 40-60 keV versus single X-ray tube voltage computed tomography (SECT) at 80 and 120 kV_p . Our reference was 120 kV_p . DECT images were sequentially acquired using the Siemens Somatom Confidence RT Pro CT according to the head and neck protocol in our department. VMI series were constructed using the Syngo Via software Monoenergetic+ algorithm. Quality parameters were: image uniformity, high- and low-contrast resolution, noise, and sensitivity to the iodinated contrast. We used the Catphan 604 phantom for quality control, except when assessing iodine sensitivity. To evaluate high contrast resolution, we calculated the modulation transfer function (MTF) using the point spread function estimation of a point bead and the slanted edge methods. For the low-contrast resolution, we used a statistical method for assessing differences between contrast structures and local noise. To measure the absolute value of noise and compare its texture, we used the standard deviation and the noise power spectrum. We measured iodine sensitivity by dissolving the Optiray Ultraject iodinated contrast in water in concentrations of 0 to 4500 mg/l and then compared the contrast to noise ratio (CNR) and analyzed the linear correlation between concentration and HU.

RESULTS

The entire series met the minimum quality requirements. However, the one at 40 keV presented uniformity at the limits of acceptability. The high- and low-contrast resolutions were similar between series. The noise of the VMI series decreased with increasing energy, while sensitivity to the contrast displayed the opposite behavior. All series showed linearity of HUs from very low iodine concentrations. Images at 60 keV presented lower iodine sensitivity than SECT at 80 kV_p , while those at 55 keV were similar to them.

CONCLUSIONS

Our method of image comparison based on standard quality parameters in phantom gave clear results about the optimal range and can be used as a guide to characterize any other DECT imaging protocols. The optimal range for using VMI images in iodinated contrasts in the Siemens system was 45-55 keV. Lower energies lacked noise and uniformity, while higher ones could be substituted by SECT images at low kilovoltage (80 kV_p ).

Automated procedure for slice thickness verification of computed tomography images: Variations of slice thickness, position from iso-center, and reconstruction filter

Purpose

The purpose of this study is to automate the slice thickness verification on the AAPM CT performance phantom and validate it for variations of slice thickness, position from iso‐center, and reconstruction filter.

Methods

An automatic procedure for slice thickness verification on AAPM CT performance phantom was developed using MATLAB R2015b. The stair object image within the phantom was segmented, and the middle stair object was located. Its angle was determined using the Hough transformation, and the image was rotated accordingly. The profile through this object was obtained, and its full‐width of half maximum (FWHM) was automatically measured. The FWHM indicated the slice thickness of the image. The automated procedure was applied with variations in three independent parameters, i.e., the slice thickness, the distance from the phantom to the iso‐center, and the reconstruction filter. The automated results were compared to manual measurements made using electronic calipers.

Results

The differences of the automated results from the nominal slice thicknesses were within 1.0 mm. The automated results are comparable to those from manual approach (i.e., the difference of both is within 12%). The automatic procedure accurately obtained slice thickness even when the phantom was moved from the iso‐center position by up to 4 cm above and 4 cm below the iso‐center. The automated results were similar (to within 0.1 mm) for various reconstruction filters.

Conclusions

We successfully developed an automated procedure of slice thickness verification and confirmed that the automated procedure provided accurate results. It provided an easy and effective method of determining slice thickness.

A Novel Phantom and a Dedicated Developed Software for Image Quality Controls in X-Ray Intraoral Devices

Background:

Periodic quality control (QC) procedures are important in order to guarantee the image quality of radiological equipment and are also conducted using phantoms simulating human body.

Objective:

To perform (QC) measurements in intraoral imaging devices, a new and simple phantom was manufactured. Besides, to simplify QC procedures, computerized LabView-based software has been devised, enabling determination of image quantitative parameters in real time or during post processing.

Material and Methods:

In this experimental study, the novel developed phantom consists of a Polymethyl methacrylate (PMMA) circular insert. It is able to perform a complete QC image program of X-ray intraoral equipment and also causes the evaluation of image uniformity, high and low contrast spatial resolution, image linearity and artefacts, with only two exposures.

Results:

Three raters analyzed the images using the LabView dedicated software and determined the quantitative and qualitative parameters in an innovative and accurate way. Statistical analysis evaluated the reliability of this study. Good accuracy of the quantitative and qualitative measurements for the different intraoral systems was obtained and no statistical differences were found using the inter-rater analysis.

Conclusion:

The achieved results and the related statistical analysis showed the validity of this methodology, which could be proposed as an alternative to the commonly adopted procedures, and suggested that the novel phantom, coupled with the LabView based software, could be considered as an effective tool to carry out a QC image program in a reproducible manner.

Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study

PURPOSE

To compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR).

METHOD

Totally 251 patients underwent two consecutive abdominopelvic unenhanced CT scans of the same range, including standard and reduced doses, respectively. In group A, standard-dose data were reconstructed by (blend 30 %) IR. In group B, reduced-dose data were reconstructed by filtered back projection reconstruction to obtain group B1 images, and post-processed using the DL algorithm (NeuAI denosing, Neusoft medical, Shenyang, China) with 50 % and 100 % weights to obtain group B2 and B3 images, respectively. Then, CT values of the liver, the second lumbar vertebral centrum, the erector spinae and abdominal subcutaneous fat were measured. CT values, noise levels, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), radiation doses and subjective scores of image quality were compared. Subjective evaluations of low-density liver lesions were compared by diagnostic results from enhanced CT or Magnetic Resonance Imaging.

RESULTS

Groups B3 and B1 showed the lowest and highest noise levels, respectively (P < 0.001). The SNR and CNR in group B3 were highest (P < 0.001). The radiation dose in group B was reduced by 71.5 % on average compared to group A. Subjective scores in groups A and B2 were highest (P < 0.001). Diagnostic sensitivity and confidence for liver metastases in groups A and B2 were highest (P < 0.001).

CONCLUSIONS

Reduced-dose abdominopelvic unenhanced CT combined with DL post-processing could ensure image quality and satisfy diagnostic needs.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

OBJECTIVE

Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%).

MATERIALS AND METHODS

This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated.

RESULTS

Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001).

CONCLUSION

DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

Recommendations of megavoltage computed tomography settings for the implementation of adaptive radiotherapy on helical tomotherapy units

Megavoltage computed tomography (MVCT) image quality metrics were evaluated on an Accuray Radixact unit to recommend scan settings for the implementation of a consistent adaptive radiotherapy program. Megavoltage computed tomography image quality was evaluated and compared to a kilovoltage CT (kVCT) simulator using a commercial cone beam computed tomography image quality phantom. Megavoltage computed tomographies were acquired on the Accuray Radixact using fine, normal, and coarse pitches, with all available reconstruction slice thicknesses, each of which were reconstructed using standard and iterative reconstruction (IR). Image quality metrics (IQM) were evaluated using DoseLab: automatically and manually calculated spatial resolution, subject contrast, and contrast‐to‐noise ratio (CNR). Scanning time was 15.6 s/cm for fine, 8.1 s/cm for normal, and 5.6 s/cm for coarse pitch. Automatically evaluated spatial resolutions ranged from 0.39, 0.41, to 0.42 lp/mm for standard reconstruction and from 0.24, 0.21, to 0.18 lp/mm for soft‐tissue IR, respectively, with general IR yielding values in between these. Spatial resolution for kVCT was measured to be at least 0.42 lp/mm. Contrast was consistent across MVCT settings with 8.1 ± 0.2%, while kVCT contrast was 10.27 ± 0.05%. CNR was calculated to be 3.3 ± 0.4 for standard reconstruction, 7.4 ± 0.4 for general IR, and 12.0 ± 1.9 for soft‐tissue IR. It was found that increasing reconstruction slice thickness for a given pitch does not improve IQMs. Based on the consistency of contrast metrics across pitch values and the only slightly reduced spatial resolution using normal compared to fine pitch, we recommend the use of normal pitch with 2 mm slice thickness to maximize image quality for ART while limiting scanning time. Only for sites for which improved CNR is required and reduced spatial resolution is acceptable, soft‐tissue IR is recommended.

Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans

The increasing incidence of pancreatic cancer will make it the second deadliest cancer in 2030. Imaging based early diagnosis and image guided treatment are emerging potential solutions. Artificial intelligence (AI) can help provide and improve widespread diagnostic expertise and accurate interventional image interpretation. Accurate segmentation of the pancreas is essential to create annotated data sets to train AI, and for computer assisted interventional guidance. Automated deep learning segmentation performance in pancreas computed tomography (CT) imaging is low due to poor grey value contrast and complex anatomy. A good solution seemed a recent interactive deep learning segmentation framework for brain CT that helped strongly improve initial automated segmentation with minimal user input. This method yielded no satisfactory results for pancreas CT, possibly due to a sub-optimal neural network architecture. We hypothesize that a state-of-the-art U-net neural network architecture is better because it can produce a better initial segmentation and is likely to be extended to work in a similar interactive approach. We implemented the existing interactive method, iFCN, and developed an interactive version of U-net method we call iUnet. The iUnet is fully trained to produce the best possible initial segmentation. In interactive mode it is additionally trained on a partial set of layers on user generated scribbles. We compare initial segmentation performance of iFCN and iUnet on a 100CT dataset using dice similarity coefficient analysis. Secondly, we assessed the performance gain in interactive use with three observers on segmentation quality and time. Average automated baseline performance was 78% (iUnet) versus 72% (FCN). Manual and semi-automatic segmentation performance was: 87% in 15 min. for manual, and 86% in 8 min. for iUNet. We conclude that iUnet provides a better baseline than iFCN and can reach expert manual performance significantly faster than manual segmentation in case of pancreas CT. Our novel iUnet architecture is modality and organ agnostic and can be a potential novel solution for semi-automatic medical imaging segmentation in general.

Design of a Monte Carlo model based on dual-source computed tomography (DSCT) scanners for dose and image quality assessment using the Monte Carlo N-Particle (MCNP5) code

The purpose of this work was to develop and validate a Monte Carlo model for a Dual Source Computed Tomography (DSCT) scanner based on the Monte Carlo N-particle radiation transport computer code (MCNP5). The geometry of the Siemens Somatom Definition CT scanner was modeled, taking into consideration the x-ray spectrum, bowtie filter, collimator, and detector system. The accuracy of the simulation from the dosimetry point of view was tested by calculating the Computed Tomography Dose Index (CTDI) values. Furthermore, typical quality assurance phantoms were modeled in order to assess the imaging aspects of the simulation. Simulated projection data were processed, using the MATLAB software, in order to reconstruct slices, using a Filtered Back Projection algorithm. CTDI, image noise, CT-number linearity, spatial and low contrast resolution were calculated using the simulated test phantoms. The results were compared using several published values including IMPACT, NIST and actual measurements. Bowtie filter shapes are in agreement with those theoretically expected. Results show that low contrast and spatial resolution are comparable with expected ones, taking into consideration the relatively limited number of events used for the simulation. The differences between simulated and nominal CT-number values were small. The present attempt to simulate a DSCT scanner could provide a powerful tool for dose assessment and support the training of clinical scientists in the imaging performance characteristics of Computed Tomography scanners.

[Quantitative evaluation of image quality of megavoltage computed tomography for guiding helical tomotherapy]

OBJECTIVE

To quantitatively analyze image quality of two sets of phantom (CatPhan504 and Cheese) Megavoltage computed tomography (MVCT) images acquired by Helical Tomotherapy with three scanning modes (Fine, Normal and Coarse), and to explore and validate a semi-automatic quality assurance procedure for MVCT images of Helical Tomotherapy.

METHODS

On Helical Tomotherapy, CatPan504 and Cheese phantoms were scanned with three pitch levels (Fine, Normal, Coarse: 4 mm, 8 mm, 12 mm/circle) respectively. Pylinac, Matlab and Eclipse were used to calculate and compare spatial resolution, noise level and low contrast resolution of images obtained under three scanning modes respectively. The spatial resolution can be evaluated by the blurring of line-pair CT value in the images of CatPhan504's CTP528 module. The noise level can be evaluated by the integral non-uniformity in the images of Cheese's uniformity module. the low contrast resolution can be evaluated by contrast-to-noise ratio of both phantoms' plug-in module, or visibility of the region of interest (Supra-Slice) in the images of CatPhan504's CTP515 module.

RESULTS

Analyses on CatPhan504's line pair module(CTP528 module) showed that the first three line pairs(the gap size are 0.500 cm, 0.250 cm and 0.167 cm respectively) could be clearly observed but blurring began to occur from the fourth line pair(the gap size is 0.125 cm) under Coarse mode. Meanwhile, the first four line pairs were all observable under the Normal and Fine modes. Integral non-integrity index(the value negatively correlated with the noise level) were 0.155 7, 0.136 8 and 0.122 9 for Coarse, Normal and Fine modes respectively. None of the Supra-Slice in CatPhan504's CTP515 module could be observed under three imaging modes. Low contrast contrast-to-noise ratio of Cheese phantom was similar under three modes and the insert visibility exhibited nearly linear growth with the increasing difference between CT average value of the insert material and background.

CONCLUSION

Superiority and inferiority of three image modes in terms of the three image quality index was not consistent. Evaluation results above could provide reference for more rational decision on scanning modes selection of helical tomotherapy, which was based on image visualization demands in clinical practice. The proposed method could also provide guidance for similar image quality assessment and periodic quality assurance.

Quality evaluation of monoenergetic images generated by dual-energy computed tomography for radiotherapy: A phantom study

PURPOSE

Quantification analysis for monoenergetic computed tomography (CT) images obtained from dual-energy CT scanning was performed in the light of their potential use for structures delineation during radiotherapy.

METHODS

Parameters that describe the quality of the images are: linearity, low and high contrast resolution, uniformity, noise and signal to noise ratio (SNR). To evaluate these parameters, a Catphan phantom was scanned using a dual-energy mode at Somatom Definition AS. Based on the polyenergetic CT images, sixteen monoenergetic series (ranged from 40 keV to 190 keV) were created by CT scanner software and automatically analyzed using Artiscan software.

RESULTS

Analysis of linearity shows that a potential use of any monoenergetic images in radiotherapy planning requires that individual calibration curves are implemented for each of them. While the results of the high contrast resolution analysis were comparable for each energy (5 lp/cm), the results of the analyses for uniformity, low contrast resolution, noise and SNR allowed us to select the best imaging energies. The highest relative uniformity was detected for images reconstructed for energies of 60 keV and 70 keV (98.54% and 98.61%). Similar results were observed for low contrast resolution, where the largest number of disks was detected for these energies, and the noise values (0.42% for 60 keV, 0.44% for 70 keV). The best SNR was observed for images reconstructed for energy of 60 keV.

CONCLUSIONS

Taking into account these results, the energy of 70 keV was selected as potentially the best for reconstruction of monoenergetic images used for structures delineation during radiotherapy.

Assessment and comparison of radiation dose and image quality in multi-detector CT scanners in non-contrast head and neck examinations

Purpose

To assess and compare radiation dose and image quality from non-contrast head and neck computed tomography (CT) examinations from four different multi-detector CT (MDCT) scanners.

Material and methods

Four CT scanners with different numbers of detector rows including one 4-MDCT, a 6-MDCT, a 16-MDCT, and a 64-MDCT were investigated. Common CT dose descriptors including volumetric CT dose index (CTDIv), dose length product (DLP), and the effective dose (ED), and image quality parameters include image noise, uniformity, and spatial resolution (SR) were estimated for each CT scanner with standard tools and methods. To have a precise comparison between CT scanners and related doses and image quality parameters, the ImPACT Q-factor was used.

Results

Minimum and maximum CTDIv, DLP, and ED in the head scan were 18 ± 3 and 49 ± 4 mGy, 242 ± 28 and 692 ± 173 mGy × cm, 0.46 ± 0.4 and 1.31 ± 0.33 mSv for 16-MDCT and 64-MDCT, respectively. And 16 ± 2 to 27 ± 3, 286 ± 127 to 645 ± 79 and 1.46 ± 0.65 to 3.29 ± 0.40 for neck scan, respectively. The Q-factor in head scan was 2.4, 3.3, 4.4 and 5.6 for 4-MDCT, 6-MDCT, 16-MDCT and 64-MDCT, respectively. The Q-factor in neck scan was 3.4, 4.6, 4.7 and 6.0 for 4-MDCT, 6-MDCT, 16-MDCT and 64-MDCT, respectively.

Conclusions

The results clearly indicate an increasing trend in the Q-factor from 4-MDCT to 64-MDCT units in both head and neck examinations. This increasing trend is due to a better SR and less noise of images taken and/or fewer doses in 64-MDCT.

The impact of mass density variations on an electron Monte Carlo algorithm for radiotherapy dose calculations

Background and Purpose

A key step in electron Monte Carlo dose calculation requires converting Computed Tomography (CT) numbers from a tomographic acquisition to a mass density. This study investigates the dosimetric consequences of perturbations applied to a calibration table between CT number and mass density.

Materials and Methods

A literature search was performed to define lower and upper bounds for physically reasonable perturbations to a reference CT number to mass density calibration table. Electron beam dose was calculated for ten patients using these variations and the results were compared to clinical plans originally derived with a reference calibration table. Dose differences both globally and in the Planning Target Volume (PTV) were assessed using dose- and volume-based metrics and 3- dimensional gamma analysis for each patient.

Results

Small but statistically significant differences were observed between perturbations and reference data for certain metrics including volume of the 50% prescription isodose. Upper and lower variations in CT number to mass density calibration yielded mean values of V50% that were 4.4% larger and 2.1% smaller than reference values respectively. Gamma analysis using 3%/3mm criteria indicated >99% passing rate for the PTV for all patients. Global gamma analysis for some patients showed larger discrepancies possibly due to large electron path lengths through inhomogeneities.

Conclusions

In most patients, physically reasonable perturbations in CT number to mass density curves will not induce clinically significant impact on calculated target dose distributions. Strong dependence of electron transport on voxel material may produce dose speckle throughout the volume. Care should be taken in evaluating critical structures at depths beyond the target volume in highly heterogeneous regions.

Design and application of an MR reference phantom for multicentre lung imaging trials

Introduction

As there is an increasing number of multicentre lung imaging studies with MRI in patients, dedicated reference phantoms are required to allow for the assessment and comparison of image quality in multi-vendor and multi-centre environments. However, appropriate phantoms for this purpose are so far not available commercially. It was therefore the purpose of this project to design and apply a cost-effective and simple to use reference phantom which addresses the specific requirements for imaging the lungs with MRI.

Methods

The phantom was designed to simulate 4 compartments (lung, blood, muscle and fat) which reflect the specific conditions in proton-MRI of the chest. Multiple phantom instances were produced and measured at 15 sites using a contemporary proton-MRI protocol designed for an in vivo COPD study at intervals over the course of the study. Measures of signal- and contrast-to-noise ratio, as well as structure and edge depiction were extracted from conventionally acquired images using software written for this purpose.

Results

For the signal to noise ratio, low intra-scanner variability was found with 4.5% in the lung compartment, 4.0% for blood, 3.3% for muscle and 3.7% for fat. The inter-scanner variability was substantially higher, with 41%, 32%, 27% and 32% for the same order of compartments. In addition, measures of structure and edge depiction were found to both vary significantly among several scanner types and among scanners of the same model which were equipped with different gradient systems.

Conclusion

The described reference phantom reproducibly quantified image quality aspects and detected substantial inter-scanner variability in a typical pulmonary multicentre proton MRI study, while variability was greater in lung tissue compared to other tissue types. Accordingly, appropriate reference phantoms can help to detect bias in multicentre in vivo study results and could also be used to harmonize equipment or data.

A simple parametric model observer for quality assurance in computer tomography

Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC). Typically, the AUC is estimated by applying the model observer to a large set of training and test data. However, the recording of these large data sets is not always practical for routine quality assurance. In this paper we propose as an alternative a parametric model observer that is based on a simple phantom, and we provide a Bayesian estimation of its AUC. It is shown that a limited number of repeatedly recorded images (10-15) is already sufficient to obtain results suitable for the quality assessment of an imaging system. A MATLAB® function is provided for the calculation of the results. The performance of the proposed model observer is compared to that of the established channelized Hotelling observer and the nonprewhitening matched filter for simulated images as well as for images obtained from a low-contrast phantom on an x-ray tomography scanner. The results suggest that the proposed parametric model observer, along with its Bayesian treatment, can provide an efficient, practical alternative for the quality assessment of CT imaging systems.

Dose and slice thickness evaluation with nMAG gel dosimeters in computed tomography

Computed tomography (CT) has been widely used in clinical diagnosis. It is important to estimate radiation dose and perform image quality assurance procedures for CT scans. In this study, nMAG gel dosimeters were used to simultaneously measure the 300-mm weighted CT dose index (CTDI) and slice sensitivity profile (SSP) for multiple detector CT (MDCT). Magnetic resonance imaging (MRI) was performed on the irradiated gel to create R₂‒dose response curves for the tube voltages of 120 and 140 kVp. The gel dosimeters were loaded in three home-made cylindrical phantoms to obtain CTDI₁₀₀ and CTDI₃₀₀. The full width at half maximum (FWHM) for 2, 5, 10, 14.4, and 38.4-mm slice thicknesses was measured and compared with the result obtained by radiochromic films. The difference in weighted CTDI₁₀₀ obtained by the gel dosimeter and ionization chamber was less than 1%. The CTDI efficiency at 120 and 140 kVp was in the range of 80.1%-82.5%. The FWHM of SSP measured by the gel dosimeter matched very well with the nominal slice thickness. The use of nMAG gel dosimeters combined with the home-made cylindrical phantoms can provide 300-mm weighted CTDI and slice thickness information, showing potential for quality assurance and clinical applications in MDCT.

100 days with scans of the same Catphan phantom on the same CT scanner

Quality control (QC) of CT scanners is important to evaluate image quality and radiation dose. Different QC phantoms for testing image quality parameters on CT are commercially available, and Catphan phantoms are widely used for this purpose. More data from measured image quality parameters on CT are necessary to assess test methods, tolerance levels, and test frequencies. The aim of this study was to evaluate the stability of essential image quality parameters for axial and helical scans on one CT scanner over time. A Catphan 600 phantom was scanned on a Philips Ingenuity CT scanner for 100 days over a period of 6 months. At each day of testing, one helical scan covering the entire phantom and four axial scans covering four different modules in the phantom were performed. All images were uploaded into Image Owl for automatic analysis of CT numbers, modular transfer function (MTF), low‐contrast resolution, noise, and uniformity. In general, the different image quality parameters for both scan techniques were stable over time compared to given tolerance levels. Average measured CT numbers differed between axial and helical scans, while MTF was almost identical for helical and axial scans. Axial scans had better low‐contrast resolution and less noise than helical scans. The uniformity was relatively similar for axial and helical scans. Most standard deviations of measured values were larger for helical scans compared to axial scans. Test results in this study were stable over time for both scan techniques, but further studies on different CT scanners are required to confirm that this also holds true for other systems.

Patient-specific geometrical modeling of orthopedic structures with high efficiency and accuracy for finite element modeling and 3D printing

We improved the geometrical modeling procedure for fast and accurate reconstruction of orthopedic structures. This procedure consists of medical image segmentation, three-dimensional geometrical reconstruction, and assignment of material properties. The patient-specific orthopedic structures reconstructed by this improved procedure can be used in the virtual surgical planning, 3D printing of real orthopedic structures and finite element analysis. A conventional modeling consists of: image segmentation, geometrical reconstruction, mesh generation, and assignment of material properties. The present study modified the conventional method to enhance software operating procedures. Patient's CT images of different bones were acquired and subsequently reconstructed to give models. The reconstruction procedures were three-dimensional image segmentation, modification of the edge length and quantity of meshes, and the assignment of material properties according to the intensity of gravy value. We compared the performance of our procedures to the conventional procedures modeling in terms of software operating time, success rate and mesh quality. Our proposed framework has the following improvements in the geometrical modeling: (1) processing time: (femur: 87.16 ± 5.90 %; pelvis: 80.16 ± 7.67 %; thoracic vertebra: 17.81 ± 4.36 %; P < 0.05); (2) least volume reduction (femur: 0.26 ± 0.06 %; pelvis: 0.70 ± 0.47, thoracic vertebra: 3.70 ± 1.75 %; P < 0.01) and (3) mesh quality in terms of aspect ratio (femur: 8.00 ± 7.38 %; pelvis: 17.70 ± 9.82 %; thoracic vertebra: 13.93 ± 9.79 %; P < 0.05) and maximum angle (femur: 4.90 ± 5.28 %; pelvis: 17.20 ± 19.29 %; thoracic vertebra: 3.86 ± 3.82 %; P < 0.05). Our proposed patient-specific geometrical modeling requires less operating time and workload, but the orthopedic structures were generated at a higher rate of success as compared with the conventional method. It is expected to benefit the surgical planning of orthopedic structures with less operating time and high accuracy of modeling.