Practical considerations for noise power spectra estimation for clinical CT scanners

Optimizing CT Imaging Parameters: Implications for Diagnostic Accuracy in Nuclear Medicine

X-ray computed tomography (CT) is an important companion modality in molecular imaging, offering attenuation correction (AC) of single-photon emission computed tomography (SPECT) - and positron emission tomography (PET)-data, topographic information in scans as well as changes in morphology in serial follow-up studies. Image quality plays a critical role in delivering an acceptable diagnosis and in medical treatment planning. Variability in protocols can present a considerable challenge in achieving consistent image quality within departments. The differences in CT scanning protocol metrics established by various manufacturers and across different generations of scanners can contribute to this issue, making the standardization of image quality a complex task. This review aims to present relevant literature herein and provide an introduction of the CT imaging parameters, including acquisition factors, reconstruction algorithms, and relevant image quality metrics, and discuss possible ways to implement a robust CT protocol review process in a nuclear medicine department. We also evaluate the potential of iterative reconstruction (IR) and deep learning (DL) for enhancing image quality and minimizing exposure doses. This article points to the need for periodic audit of image quality to guarantee that CT protocols are suited for the intended purpose. Through the creation of local diagnostic reference levels and monitoring performance through protocol management, physicians may aim at delivering high quality imaging services consistently adhering to the principles of ALARA and reduction of dose for both patients and workers.

Optimization of Low-Contrast Detectability in Abdominal Imaging: A Comparative Analysis of PCCT, DECT, and SECT Systems

BACKGROUND

Clear representation of anatomy is essential in the assessment of pathology in computed tomography (CT). With the introduction of photon-counting CT (PCCT) and more advanced iterative reconstruction (IR) algorithms into clinical practice, there is potential to improve low-contrast detectability in CT protocols. As such, it is necessary to perform task-based assessments to optimize protocols and compare image quality between PCCT and energy-integrating CT, like dual-energy CT (DECT) and single-energy CT (SECT).

PURPOSE

This work aimed to assess low-contrast detectability in abdominal protocols used in clinical PCCT, DECT, and SECT, using both model and human observers.

METHODS

Data were acquired with the standard resolution scan mode on a PCCT (NAEOTOM Alpha, Siemens Healthineers, Forchheim, Germany) and a DECT/SECT (SOMATOM Force, Siemens Healthineers, Forchheim, Germany). Detectability was investigated in the CTP 515 low-contrast module of the Catphan 600 phantom, which was surrounded by a fat annulus to simulate an abdomen and resulted in a water equivalent diameter of 298 mm. Supra-slice contrast rods with a nominal 1.0% contrast and diameters of 4, 6, 9, and 15 mm were used. Factory abdominal protocols were adjusted to acquire images with various tube potentials (70, 90, 120, and 140 kV in PCCT; 70/150Sn and 80/150Sn kV in DECT; 100 and 120 kV in SECT), virtual monoenergetic image (VMI) energy levels (40 to 140 keV in PCCT and DECT), doses (5, 10 mGy in PCCT; 10 mGy in DECT and SECT), and IR settings (Br40 kernel, no quantum IR (QIR) and QIR levels 1 to 4 in PCCT; advanced modeled IR (ADMIRE) level 3 in DECT and SECT). Mixed DECT (linear blending of the images at two tube voltages) images were also reconstructed. The noise power spectrum and task transfer function of each scan protocol were quantified; the detectability index for each protocol was also determined using in-house implementations of model observers (non-prewhitening matched filters with internal noise, NPWI, and with an eye filter and internal noise, NPWEI) and human observers (in-house four-alternative forced choice, scoring with 95% confidence intervals).

RESULTS

Results show that the image noise is minimized at a VMI energy corresponding to the applied spectrum's mean energy in PCCT and with VMI settings of 70 and 80 keV for 70/150Sn and 80/150Sn tube potential pairs, respectively, in DECT. With respect to the human observer detectability index calculations, the normalized root-mean-square error for the NPWI and NPWEI model observers was 5% and 12%, respectively. PCCT VMI improves low-contrast detectability. Additionally, detectability can be matched between PCCT protocols by increasing the QIR strength level when reducing the dose. Not only does PCCT VMI outperform DECT VMI, but also DECT VMI outperforms DECT mixed imaging in improving low-contrast detectability.

CONCLUSIONS

Low-contrast detectability is optimized when the appropriate VMI energy level is selected in PCCT and DECT to minimize image noise. PCCT improves low-contrast detectability and may allow for dose reduction in abdominal protocols compared to both DECT and SECT. The non-prewhitening model observer with internal noise better quantified low-contrast detectability without the inclusion of an eye filter.

Assessment and improvement of the quality of radiotherapy treatment planning CT images using a clinically validated phantom based method and a multicentre intercomparison

PURPOSE

To develop a phantom method of image quality assessment for radiotherapy planning CT protocols (head and neck (H&N) and prostate) and validate results against clinical image quality. Test with data from different scanners and suggest protocol adjustments.

METHODS

Macros measured patient water-equivalent diameter and noise from clinical CT images. Target transfer function (TTF), contrast, noise-power spectrum (NPS), detectability index and the edge visibility of a low contrast target were measured using Catphan 604 and bespoke phantoms. Ten centres scanned the phantoms with modified clinical protocols and collected data from patient images using the macros. Clinical experts, ranked the quality of images for contouring and correlated results against phantom metrics.

RESULTS

Clinical image review showed a large range of results from different scanners for H&N scans and fewer differences for prostate. The phantom metrics best correlated with high clinical image scores were, for H&N: high TTF50 (r = 0.73, p = 0.003), contrast (r = 0.58, p = 0.003) and target edge visibility (r = 0.70, p = 0.004); for prostate: high TTF50 (r = 0.83, p = 0.002), low noise (r = 0.37, p = 0.26) and target edge visibility (r = 0.59, p = 0.05). Hence, optimal contrast, resolution and noise are important for good contouring image quality. Reconstruction kernel, field of view and noise, or X-ray tube current and rotation time, are possible parameters for adjustment.

CONCLUSIONS

This phantom method (using Catphan 604) was a good surrogate for clinical quality assessment of CT images for radiotherapy contouring. Results identified the poorest performing scanners, allowing recommendations for image quality improvement and confirming scan protocol optimisation is necessary in some centres.

Photon counting CT versus energy-integrating CT: A comparative evaluation of advances in image resolution, noise, and dose efficiency

BACKGROUND

Photon counting computed tomography (PCCT) employs direct and spectrally resolved counting of individual x-ray quanta, enhancing image quality compared to the standard energy-integrating CT (EICT).

PURPOSE

To evaluate the quantitative improvements in CT image quality metrics by comparing the first medical PCCT with a state-of-the-art EICT.

METHODS

The PCCT versus EICT noise improvement ratio R was derived from the quantum statistics of the measurement process and measured across the clinical x-ray flux range for both systems. Detector and system modulation transfer functions (MTFs) were obtained using tilted-slit and wire phantom measurements. Image root mean square (RMS) noise, noise power spectrum (NPS), and x-ray patient dose were compared using a CatPhan phantom at two identical clinical target resolutions.

RESULTS

The measurement of the PCCT noise improvement ratio R showed an elimination of electronic noise and a 10% noise transfer advantage. The PCCT detector MTF exhibited 3x higher angular resolution limits in comparison to EICT and close to ideal sinc behavior due to the electromagnetic formation of pixels in the PCCT semiconductor detector. This translated to 3.5x enhancements in CT system MTF ratios at 10 LP/cm, reflecting a significant improvement in millimeter range CT imaging. Both the improved quantum detection and the system MTF ratio improvement contribute to the measured 3x enhancements in image NPS at 10 LP/cm for identical image target resolution. An improvement of up to 1.7x in RMS image noise was observed accordingly. For low and ultra-low dose imaging with image filtering, dose efficiency increased between 2x and 10x, demonstrating the PCCT's capability to advance CT ultra-low dose imaging.

CONCLUSION

The direct counting detection in PCCT has been shown to significantly improve sinogram noise and detector MTF ratios compared to energy integrating EICT. The observed translations into CT system MTF, image NPS, image noise, and dose ratios reflect a paradigm shift for CT image quality and dose efficiency.

[A Simple Method for Measuring the Anisotropic Two-dimensional Noise Power Spectrum in Helical CT Scan]

PURPOSE

Recently, a new method has been devised to measure the anisotropic 2-dimensional noise power spectrum (2D-NPS) in computed tomography. The 2D-NPS varied with the X-ray tube angle θ in the helical scan; it was defined as 2D-NPSθ. However, the method requires many scans to obtain the 2D-NPSθ for each θ with less statistical variability and is laborious. In the present study, the 2D-NPSθ was assumed to be an identical anisotropic 2D-NPS that rotated around the origin of the spatial frequency domain in conjunction with the rotation of the X-ray tube. We defined the identical 2D-NPS as 2D-NPSrot and proposed its measurement method with fewer scans.

METHODS

The 2D-NPSθ (θ from 0° to 180° in an increment of 30°) were obtained from noise images acquired by a hundred scans of a water phantom. In the proposed method, the 2D-NPSθ were obtained from noise images by 2 scans, rotated backward around the origin by θ to generate the identical 2D-NPS, and averaged to generate the 2D-NPSrot.

RESULTS

The 2D-NPSrot, when it was rotated by θ, agreed well with the corresponding 2D-NPSθ. Absolute values of the mean and standard deviation of percentage errors of the 2D-NPSrot with the corresponding 2D-NPSθ at each θ were less than 0.70% and 6.12%, respectively.

CONCLUSION

The proposed method was suggested to be valid for simple measurement of anisotropic 2D-NPS.

SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction

Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4-D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction. However, these methods usually suffer from difficulties such as laborious parameter selection and expensive computational costs. In addition, due to the image similarity of different energy bins, spectral CT usually implies a strong low-rank prior, which has been widely adopted in current iterative reconstruction models. Singular value thresholding (SVT) is an effective algorithm to solve the low-rank constrained model. However, the SVT method requires a manual selection of thresholds, which may lead to suboptimal results. To relieve these problems, in this article, we propose a sparse and low-rank unrolling network (SOUL-Net) for spectral CT image reconstruction, that learns the parameters and thresholds in a data-driven manner. Furthermore, a Taylor expansion-based neural network backpropagation method is introduced to improve the numerical stability. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of detail preservation and artifact reduction.

Image Quality Comparison of Three 3D Mobile X-Ray Imaging Guidance Devices Used in Spine Surgery: A Phantom Study

An image-quality CT phantom was scanned with three different 3D X-ray imaging guidance devices in the operating theatre: O-Arm, Loop-X, and Airo TruCT. Default acquisition and reconstruction parameters for lumbar spine procedures were used on each device. The tube current was set to a dose level of around 27 mGy. A task-based image quality assessment was performed by calculating the noise power spectrum (NPS) and task transfer function (TTF). A detectability index (d') was calculated for three simulated bone lesions. The noise magnitude of the O-Arm was higher than the Airo TruCT, and the Loop-X had higher noise than the Airo TruCT. The highest average NPS frequency was for the O-Arm images, and the lowest was for the Loop-X. The TTFs at 50% values were similar for the Airo TruCT and Loop-X devices. Compared to Airo TruCT, the TTF at 50% value increased with the O-Arm by 53.12% and 41.20% for the Teflon and Delrin inserts, respectively. Compared to Airo TruCT, the d' value was lower with Loop-X by -26.73%, -27.02%, and -23.95% for lytic lesions, sclerotic lesions, and high-density bone, respectively. Each 3D-imaging spine surgery guidance device has its own strengths and weaknesses in terms of image quality. Cone-beam CT systems apparently offer the best compromise between noise and spatial resolution for spine surgery.

Universal 120-kV Dual-Source Ultra-High Pitch Protocol on the Photon-Counting CT System for Pediatric Abdomen of All Sizes: A Phantom Investigation Comparing With Energy-Integrating CT

OBJECTIVES

The purpose of this study is to determine if a universal 120-kV ultra-high pitch and virtual monoenergetic images (VMIs) protocol on the photon-counting computed tomography (PCCT) system can provide sufficient image quality for pediatric abdominal imaging, regardless of size, compared with protocols using a size-dependent kV and dual-source flash mode on the energy-integrating CT (EICT) system.

MATERIALS AND METHODS

One solid water insert and 3 iodine (2, 5, 10 mg I/mL) inserts were attached or inserted into phantoms of variable sizes, simulating the abdomens of a newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. Each phantom setting was scanned on an EICT using clinical size-specific kV dual-source protocols with a pitch of 3.0. The scans were performed with fixed scanning parameters, and the CTDI vol values of full dose were 0.30, 0.71, 1.05, and 7.40 mGy for newborn to adult size, respectively. In addition, half dose scans were acquired on EICT. Each phantom was then scanned on a PCCT (Siemens Alpha) using a universal 120-kV protocol with the same full dose and half dose as determined above on the EICT scanner. All other parameters matched to EICT settings. Virtual monoenergetic images were generated from PCCT scans between 40 and 80 keV with a 5-keV interval. Image quality metrics were compared between PCCT VMIs and EICT, including image noise (measured as standard deviation of solid water), contrast-to-noise ratio (CNR) (measured at iodine inserts with solid water as background), and noise power spectrum (measured in uniform phantom regions).

RESULTS

Noise at a PCCT VMI of 70 keV (7.0 ± 0.6 HU for newborn, 14.7 ± 1.6 HU for adult) is comparable ( P > 0.05, t test) or significantly lower ( P < 0.05, t test) compared with EICT (7.8 ± 0.8 HU for newborn, 15.3 ± 1.5 HU for adult). Iodine CNR from PCCT VMI at 50 keV (50.8 ± 8.4 for newborn, 27.3 ± 2.8 for adult) is comparable ( P > 0.05, t test) or significantly higher ( P < 0.05, t test) to the corresponding EICT measurements (57.5 ± 6.7 for newborn, 13.8 ± 1.7 for adult). The noise power spectrum curve shape of PCCT VMI is similar to EICT, despite PCCT VMI exhibiting higher noise at low keV levels.

CONCLUSIONS

The universal PCCT 120 kV with ultra-high pitch and postprocessed VMIs demonstrated equivalent or improved performance in noise (70 keV) and iodine CNR (50 keV) for pediatric abdominal CT, compared with size-specific kV images on the EICT.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study

OBJECTIVES

To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms.

METHODS

An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions.

RESULTS

NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy.

CONCLUSIONS

DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Impact of ROI Size on the Accuracy of Noise Measurement in CT on Computational and ACR Phantoms

BACKGROUND

The effect of region of interest (ROI) size variation on producing accurate noise levels is not yet studied.

OBJECTIVE

This study aimed to evaluate the influence of ROI sizes on the accuracy of noise measurement in computed tomography (CT) by using images of a computational and American College of Radiology (ACR) phantoms.

MATERIAL AND METHODS

In this experimental study, two phantoms were used, including computational and ACR phantoms. A computational phantom was developed by using Matlab R215a software (Mathworks Inc., Natick, MA Natick, MA) with a homogeneously +100 Hounsfield Unit (HU) value and an added-Gaussian noise with various levels of 5, 10, 25, 50, 75, and 100 HU. The ACR phantom was scanned with a Philips MX-16 slice CT scanner in different slice thicknesses of 1.5, 3, 5, and 7 mm to obtain noise variation. Noise measurement was conducted at the center of the phantom images and four locations close to the edge of the phantom images using different ROI sizes from 3 × 3 to 41 × 41 pixels, with an increased size of 2 × 2 pixels.

RESULTS

The use of a minimum ROI size of 21 × 21 pixels shows noise in the range of ± 5% ground truth noise. The measured noise increases above the ± 5% range if the used ROI is smaller than 21 × 21 pixels.

CONCLUSION

A minimum acceptable ROI size is required to maintain the accuracy of noise measurement with a size of 21 × 21 pixels.

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

Effective Spatial Resolution of Photon Counting CT for Imaging of Trabecular Structures is Superior to Conventional Clinical CT and Similar to High Resolution Peripheral CT

OBJECTIVES

Photon counting computed tomography (PCCT) might offer an effective spatial resolution that is significantly improved compared with conventional state-of-the-art computed tomography (CT) and even provide a microstructural level of detail similar to high-resolution peripheral CT (HR-pQCT). The aim of this study was to evaluate the volumetric effective spatial resolution of clinically approved PCCT as an alternative to HR-pQCT for ex vivo or preclinical high-resolution imaging of bone microstructure.

MATERIALS AND METHODS

The experiment contained 5 human vertebrae embedded in epoxy resin, which were scanned 3 times each, and on 3 different clinical CT scanners: a PCCT (Naeotom Alpha), a dual-energy CT (Somatom Force [SF]), and a single-energy CT (Somatom Sensation 40 [S40]), all manufactured by Siemens Healthineers (Erlangen, Germany). Scans were performed with a tube voltage of 120 kVp and, to provide maximum scan performance and minimum noise deterioration, with exposures of 1500 mAs (SF), 2400 mAs (S40), and 4500 mAs (PCCT) and low slice increments of 0.1 (PCCT) and 0.3 mm (SF, S40). Images were reconstructed with sharp and very sharp bone kernels, Br68 and Br76 (PCCT), Br64 (SF), and B65s and B75h (S40). Ground truth information was obtained from an XtremeCT scanner (Scanco, Brüttisellen, Switzerland). Voxel-wise comparison was performed after registration, calibration, and resampling of the volumes to isotropic voxel size of 0.164 mm. Three-dimensional point spread- and modulation-transfer functions were calculated with Wiener's deconvolution in the anatomical trabecular structure, allowing optimum estimation of device- and kernel-specific smoothing properties as well as specimen-related diffraction effects on the measurement.

RESULTS

At high contrast (modulation transfer function [MTF] of 10%), radial effective resolutions of PCCT were 10.5 lp/cm (minimum resolvable object size 476 μm) for kernel Br68 and 16.9 lp/cm (295 μm) for kernel Br76. At low contrast (MTF 5%), radial effective spatial resolutions were 10.8 lp/cm (464 μm) for kernel Br68 and 30.5 lp/cm (164 μm) for kernel Br76. Axial effective resolutions of PCCT for both kernels were between 27.0 (185 μm) and 29.9 lp/cm (167 μm). Spatial resolutions with kernel Br76 might possibly be still higher but were technically limited by the isotropic voxel size of 164 μm. The effective volumetric resolutions of PCCT with kernel Br76 ranged between 61.9 (MTF 10%) and 222.4 (MTF 5%) elements per cubic mm. Photon counting CT improved the effective volumetric resolution by factor 5.5 (MTF 10%) and 18 (MTF 5%) compared with SF and by a factor of 8.7 (MTF 10%) and 20 (MTF 5%) compared with S40. Photon counting CT allowed obtaining similar structural information as HR-pQCT.

CONCLUSIONS

The effective spatial resolution of PCCT in trabecular bone imaging was comparable with that of HR-pQCT and more than 5 times higher compared with conventional CT. For ex vivo samples and when patient radiation dose can be neglected, PCCT allows imaging bone microstructure at a preclinical level of detail.

Denoising of pediatric low dose abdominal CT using deep learning based algorithm

OBJECTIVES

To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.

MATERIALS AND METHODS

LDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets. The trained deep learning method generated virtual SDCT images (VIs) from the original LDCT images (OIs). To test the proposed method, LDCT images (80 kVp, 262 mAs, n = 33) were collected from another CT scanner using iterative reconstruction (IR). Image analyses were performed to evaluate the qualities of VIs in the validation set and to compare the performance of deep learning and IR in the test set.

RESULTS

The noise of the VIs was the lowest in both validation and test sets (all p<0.001). The mean CT number of the VIs for the portal vein and liver was lower than that of OIs in both validation and test sets (all p<0.001) and was similar to those of SDCT. The contrast-to-noise ratio of portal vein and the signal-to-noise ratio (SNR) of portal vein and liver of VIs were higher than those of SDCT (all p<0.05). The SNR of VIs in test sets was the highest among three images.

CONCLUSION

The deep learning method trained by unpaired datasets could reduce noise of LDCT images and showed similar performance to SAFIRE. It can be applied to LDCT images of older CT scanners without IR.

Method to estimate fan-beam CT noise power spectrum using two basis functions with a limited number of noise realizations

PURPOSE

The noise power spectrum (NPS) plays a key role in image quality (IQ) evaluation as it can be used for predicting detection performance or calculating detective quantum efficiency (DQE). Traditionally, the NPS is estimated by ensemble averaging multiple realizations of noise-only images. However, the estimation error increases when there are a limited number of images. Since the estimation error directly affects the IQ index, an accurate NPS estimation method is required. Recent works have proposed NPS estimation methods using the radial 1D NPS as the basis; however, when sharp kernels are used during image reconstruction, these methods cannot accurately estimate the amplitude of each angular spoke of the 2D NPS composed of different cutoff frequencies determined from the complementary projection magnification factors for different spatial regions. In this work, we propose a 2D NPS estimation method that reflects the accurate amplitude of each angular spoke for fan-beam CT images.

METHODS

An angular spoke of the 2D NPS is composed of two basis functions with different cutoff frequencies determined from the complementary projection magnification factors. The proposed method estimates these two weighting factors for each basis function by minimizing the mean-squared error (MSE) between the 2D NPS estimated from 10 noise realizations. Two noise profiles and two types of apodization filters (i.e., rectangular and Hanning) were used to reconstruct the noise-only images. To examine the nonstationary noise property of fan-beam CT images, the 2D NPS was estimated at three different local regions. The estimation accuracy of the proposed method was further improved by estimating the approximate weighting factors with sinusoidal functions, considering that the weighting factors vary slowly throughout the view angles. Regression orders of 1 to 4 were used during these estimations. The approximate weighting factors were then multiplied with each of the basis functions to estimate the 2D NPS. The normalized mean-squared error (NMSE) was used as an index to compare the performance of each NPS estimation method, with the analytical 2D NPS as the reference. Further validation was performed using XCAT phantom data.

RESULTS

We observed that the 2D NPS estimated using two basis functions reflected the accurate amplitude of each angular spoke, whereas the 2D NPS estimated using the radial 1D NPS as the basis could not. The 2D NPS estimated by applying the approximate weighting factors showed improved performance compared with that estimated using two basis functions. In addition, unlike the view-independent noise cases, where a lower regression order showed higher estimation performance, a higher regression order showed higher estimation performance in the view-dependent noise cases.

CONCLUSIONS

In this work, we propose a 2D NPS estimation method that reflects the accurate amplitude of each angular spoke for fan-beam CT images using two basis functions. We observed that the proposed 2D NPS estimation method using two basis functions achieved better estimation performance compared with the method using the radial 1D NPS as the basis. This article is protected by copyright. All rights reserved.

MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction

Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces. Because patch manifolds of medical images have low-dimensional structures, we can build graphs from the manifolds. Then, we simultaneously leverage the spatial convolution to extract the local pixel-level features from the images and incorporate the graph convolution to analyze the nonlocal topological features in manifold space. The experiments show that our proposed method outperforms both the quantitative and qualitative aspects of state-of-the-art methods. In addition, aided by a projection loss component, our proposed method also demonstrates superior performance for semi-supervised learning. The network can remove most noise while maintaining the details of only 10% (40 slices) of the training data labeled.

Comparison of low-contrast detectability between uniform and anatomically realistic phantoms-influences on CT image quality assessment

Objectives

To evaluate the effects of anatomical phantom structure on task-based image quality assessment compared with a uniform phantom background.

Methods

Two neck phantom types of identical shape were investigated: a uniform type containing 10-mm lesions with 4, 9, 18, 30, and 38 HU contrast to the surrounding area and an anatomically realistic type containing lesions of the same size and location with 10, 18, 30, and 38 HU contrast. Phantom images were acquired at two dose levels (CTDIvol of 1.4 and 5.6 mGy) and reconstructed using filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). Detection accuracy was evaluated by seven radiologists in a 4-alternative forced choice experiment.

Results

Anatomical phantom structure impaired lesion detection at all lesion contrasts (p < 0.01). Detectability in the anatomical phantom at 30 HU contrast was similar to 9 HU contrast in uniform images (91.1% vs. 89.5%). Detection accuracy decreased from 83.6% at 5.6 mGy to 55.4% at 1.4 mGy in uniform FBP images (p < 0.001), whereas AIDR 3D preserved detectability at 1.4 mGy (80.7% vs. 85% at 5.6 mGy, p = 0.375) and was superior to FBP (p < 0.001). In the assessment of anatomical images, superiority of AIDR 3D was not confirmed and dose reduction moderately affected detectability (74.6% vs. 68.2%, p = 0.027 for FBP and 81.1% vs. 73%, p = 0.018 for AIDR 3D).

Conclusions

A lesion contrast increase from 9 to 30 HU is necessary for similar detectability in anatomical and uniform neck phantom images. Anatomical phantom structure influences task-based assessment of iterative reconstruction and dose effects.

Key Points

• A lesion contrast increase from 9 to 30 HU is necessary for similar low-contrast detectability in anatomical and uniform neck phantom images.

• Phantom background structure influences task-based assessment of iterative reconstruction and dose effects.

• Transferability of CT assessment to clinical imaging can be expected to improve as the realism of the test environment increases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08248-3.

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 ).

Impact of iterative reconstruction algorithms on the applicability of Fourier-based detectability index for x-ray CT imaging

PURPOSE

The increasing application of iterative reconstruction algorithms in clinical computed tomography to improve image quality and reduce radiation dose, elicits strong interest, and needs model observers to optimize CT scanning protocols objectively and efficiently. The current paradigm for evaluating imaging system performance relies on Fourier methods, which presuppose a linear, wide-sense stationary system. Long-range correlations introduced by iterative reconstruction algorithms may narrow the applicability of Fourier techniques. Differences in the implementation of reconstruction algorithms between manufacturers add further complexity. The present work set out to quantify the errors entailed by the use of Fourier methods, which can lead to design decisions that do not correlate with detectability.

METHODS

To address this question, we evaluated the noise properties and the detectability index of the ideal linear observer using the spatial approach and the Fourier-based approach. For this purpose, a homogeneous phantom was imaged on two scanners: the Revolution CT (GE Healthcare) and the Somatom Definition AS+ (Siemens Healthcare) at different exposure levels. Images were reconstructed using different strength levels of IR algorithms available on the systems considered: Adaptative Statistical Iterative Reconstruction (ASIR-V) and Sinogram Affirmed Iterative Reconstruction (SAFIRE).

RESULTS

Our findings highlight that the spatial domain estimate of the detectability index is higher than the Fourier domain estimate. This trend is found to be dependent on the specific regularization used by IR algorithms as well as the signal to be detected. The eigenanalysis of the noise covariance matrix and of its circulant approximation yields explanation about the evoked trends. In particular, this analysis suggests that the predictive power of the Fourier-based ideal linear observer depends on the ability of each basis analyzed to be relevant to the signal to be detected.

CONCLUSION

The applicability of Fourier techniques is dependent on the specific regularization used by IR algorithms. These results argue for verifying the assumptions made when using Fourier methods since Fourier-task-based detectability index does not always correlate with signal detectability.

AAPM Task Group 198 Report: An implementation guide for TG 142 quality assurance of medical accelerators

The charges on this task group (TG) were as follows: (a) provide specific procedural guidelines for performing the tests recommended in TG 142; (b) provide estimate of the range of time, appropriate personnel, and qualifications necessary to complete the tests in TG 142; and (c) provide sample daily, weekly, monthly, or annual quality assurance (QA) forms. Many of the guidelines in this report are drawn from the literature and are included in the references. When literature was not available, specific test methods reflect the experiences of the TG members (e.g., a test method for door interlock is self-evident with no literature necessary). In other cases, the technology is so new that no literature for test methods was available. Given broad clinical adaptation of volumetric modulated arc therapy (VMAT), which is not a specific topic of TG 142, several tests and criteria specific to VMAT were added.

An Improved Method of Automated Noise Measurement System in CT Images

Background:

It is necessary to have an automated noise measurement system working accurately to optimize dose in computerized tomography (CT) examinations.

Objective:

This study aims to develop an algorithm to automate noise measurement that can be implemented in CT images of all body regions.

Materials and Methods:

In this retrospective study, our automated noise measurement method consists of three steps as follows: the first is segmenting the image of the patient. The second is developing a standard deviation (SD) map by calculating the SD value for each pixel with a sliding window operation. The third step is estimating the noise as the smallest SD from the SD map. The proposed method was applied to the images of a homogenous phantom and a full body adult anthropomorphic phantom, and retrospectively applied to 27 abdominal images of patients.

Results:

For a homogeneous phantom, the noises calculated using our proposed and previous algorithms have a linear correlation with R² = 0.997. It is found that the noise magnitude closely follows the magnitude of the water equivalent diameter (D_w) in all body regions. The proposed algorithm is able to distinguish the noise magnitude due to variations in tube currents and different noise suppression techniques such as strong, standard, mild, and weak ones in a reconstructed image using the AIDR 3D algorithm.

Conclusion:

An automated noise calculation has been proposed and successfully implemented in all body regions. It is not only accurate and easy to implement but also not influenced by the subjectivity of user.

On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise

Robustness is an important aspect when evaluating a method of medical image analysis. In this study, we investigated the robustness of a deep learning (DL)-based lung-nodule classification model for CT images with respect to noise perturbations. A deep neural network (DNN) was established to classify 3D CT images of lung nodules into malignant or benign groups. The established DNN was able to predict malignancy rate of lung nodules based on CT images, achieving the area under the curve of 0.91 for the testing dataset in a tenfold cross validation as compared to radiologists' prediction. We then evaluated its robustness against noise perturbations. We added to the input CT images noise signals generated randomly or via an optimization scheme using a realistic noise model based on a noise power spectrum for a given mAs level, and monitored the DNN's output. The results showed that the CT noise was able to affect the prediction results of the established DNN model. With random noise perturbations at 100 mAs, DNN's predictions for 11.2% of training data and 17.4% of testing data were successfully altered by at least once. The percentage increased to 23.4% and 34.3%, respectively, for optimization-based perturbations. We further evaluated robustness of models with different architectures, parameters, number of output labels, etc, and robustness concern was found in these models to different degrees. To improve model robustness, we empirically proposed an adaptive training scheme. It fine-tuned the DNN model by including perturbations in the training dataset that successfully altered the DNN's perturbations. The adaptive scheme was repeatedly performed to gradually improve DNN's robustness. The numbers of perturbations at 100 mAs affecting DNN's predictions were reduced to 10.8% for training and 21.1% for testing by the adaptive training scheme after two iterations. Our study illustrated that robustness may potentially be a concern for an exemplary DL-based lung-nodule classification model for CT images, indicating the needs for evaluating and ensuring model robustness when developing similar models. The proposed adaptive training scheme may be able to improve model robustness.

Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction

Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose reduction and image quality improvement for pediatric CT. Materials and Methods DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrospective study by using data from CT examinations of pediatric patients (February to December 2018). A comparison of object detectability for 15 objects (diameter, 0.5-10 mm) at four contrast difference levels (50, 150, 250, and 350 HU) was performed by using a non-prewhitening-matched mathematical observer model with eye filter (d'_NPWE), task transfer function, and noise power spectrum analysis. Object detectability was assessed by using area under the curve analysis. Three pediatric radiologists performed an observer study to assess anatomic structures with low object-to-background signal and contrast to noise in the azygos vein, right hepatic vein, common bile duct, and superior mesenteric artery. Observers rated from 1 to 10 (worst to best) for edge definition, quantum noise level, and object conspicuity. Analysis of variance and Tukey honest significant difference post hoc tests were used to analyze differences between reconstruction algorithms. Results Images from 19 patients (mean age, 11 years ± 5 [standard deviation]; 10 female patients) were evaluated. Compared with FBP, SBIR, and MBIR, DLR demonstrated improved object detectability by 51% (16.5 of 10.9), 18% (16.5 of 13.9), and 11% (16.5 of 14.8), respectively. DLR reduced image noise without noise texture effects seen with MBIR. Radiologist ratings were 7 ± 1 (DLR), 6.2 ± 1 (MBIR), 6.2 ± 1 (SBIR), and 4.6 ± 1 (FBP); two-way analysis of variance showed a difference on the basis of reconstruction type (P < .001). Radiologists consistently preferred DLR images (intraclass correlation coefficient, 0.89; 95% CI: 0.83, 0.93). DLR demonstrated 52% (1 of 2.1) greater dose reduction than SBIR. Conclusion The DLR algorithm improved image quality and dose reduction without sacrificing noise texture and spatial resolution. © RSNA, 2020 Online supplemental material is available for this article.

Development of a computational phantom for validation of automated noise measurement in CT images

The purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from -1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (N_G). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (N_M) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the N_Mvalue was much smaller than the N_G. For kernel sizes from 17 × 17 to 21 × 21 pixels, the N_Mvalue was about 90% of N_G. And for kernel sizes of 23 × 23 pixels and above, N_Mis greater than N_G. It was also found that even with small kernel sizes the relationship between N_Mand N_Gis linear with R²more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.

Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT

PURPOSE

Clinical use of dedicated breast computed tomography (bCT) requires relatively short scan times necessitating systems with high frame rates. This in turn impacts the x-ray tube operating range. We characterize the effects of tube voltage, beam filtration, dose, and object size on contrast and noise properties related to soft tissue and iodine contrast agents as a way to optimize imaging protocols for soft tissue and iodine contrast at high frame rates.

METHODS

This study design uses the signal-difference-to-noise ratio (SDNR), noise-equivalent quanta (NEQ), and detectability (d´) as measures of imaging performance for a prototype breast CT scanner that utilizes a pulsed x-ray tube (with a 4 ms pulse width) at 43.5 fps acquisition rate. We assess a range of kV, filtration, breast phantom size, and mean glandular dose (MGD). Performance measures are estimated from images of adipose-equivalent breast phantoms machined to have a representative size and shape of small, medium, and large breasts. Water (glandular tissue equivalent) and iodine contrast (5 mg/ml) were used to fill two cylindrical wells in the phantoms.

RESULTS

Air kerma levels required for obtaining an MGD of 6 mGy ranged from 7.1 to 9.1 mGy and are reported across all kV, filtration, and breast phantom sizes. However, at 50 kV, the thick filters (0.3 mm of Cu or Gd) exceeded the maximum available mA of the x-ray generator, and hence, these conditions were excluded from subsequent analysis. There was a strong positive association between measurements of SDNR and d' (R²  > 0.97) within the range of parameters investigated in this work. A significant decrease in soft tissue SDNR was observed for increasing phantom size and increasing kV with a maximum SDNR at 50 kV with 0.2 mm Cu or 0.2 mm Gd filtration. For iodine contrast SDNR, a significant decrease was observed with increasing phantom size, but a decrease in SDNR for increasing kV was only observed for 70 kV (50 and 60 kV were not significantly different). Thicker Gd filtration (0.3 mm Gd) resulted in a significant increase in iodine SDNR and decrease in soft tissue SDNR but requires significantly more tube current to deliver the same MGD.

CONCLUSIONS

The choice of 60 kV with 0.2 mm Gd filtration provides a good trade-off for maximizing both soft tissue and iodine contrast. This scanning technique takes advantage of the ~50 keV Gd k-edge to produce contrast and can be achieved within operating range of the x-ray generator used in this work. Imaging at 60 kV allows for a greater range in dose delivered to the large breast sizes when uniform image quality is desired across all breast sizes. While imaging performance metrics (i.e., detectability index and SDNR) were shown to be strongly correlated, the methodologies presented in this work for the estimation of NEQ (and subsequently d') provides a meaningful description of the spatial resolution and noise characteristics of this prototype bCT system across a range of beam quality, dose, and object sizes.

Image quality comparison between a phase-contrast synchrotron radiation breast CT and a clinical breast CT: a phantom based study

In this study we compared the image quality of a synchrotron radiation (SR) breast computed tomography (BCT) system with a clinical BCT in terms of contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), noise power spectrum (NPS), spatial resolution and detail visibility. A breast phantom consisting of several slabs of breast-adipose equivalent material with different embedded targets (i.e., masses, fibers and calcifications) was used. Phantom images were acquired using a dedicated BCT system installed at the Radboud University Medical Center (Nijmegen, The Netherlands) and the SR BCT system at the SYRMEP beamline of Elettra SR facility (Trieste, Italy) based on a photon-counting detector. Images with the SR setup were acquired mimicking the clinical BCT conditions (i.e., energy of 30 keV and radiation dose of 6.5 mGy). Images were reconstructed with an isotropic cubic voxel of 273 µm for the clinical BCT, while for the SR setup two phase-retrieval (PhR) kernels (referred to as “smooth” and “sharp”) were alternatively applied to each projection before tomographic reconstruction, with voxel size of 57 × 57 × 50 µm³. The CNR for the clinical BCT system can be up to 2-times higher than SR system, while the SNR can be 3-times lower than SR system, when the smooth PhR is used. The peak frequency of the NPS for the SR BCT is 2 to 4-times higher (0.9 mm⁻¹ and 1.4 mm⁻¹ with smooth and sharp PhR, respectively) than the clinical BCT (0.4 mm⁻¹). The spatial resolution (MTF_10%) was estimated to be 1.3 lp/mm for the clinical BCT, and 5.0 lp/mm and 6.7 lp/mm for the SR BCT with the smooth and sharp PhR, respectively. The smallest fiber visible in the SR BCT has a diameter of 0.15 mm, while for the clinical BCT is 0.41 mm. Calcification clusters with diameter of 0.13 mm are visible in the SR BCT, while the smallest diameter for the clinical BCT is 0.29 mm. As expected, the image quality of the SR BCT outperforms the clinical BCT system, providing images with higher spatial resolution and SNR, and with finer granularity. Nevertheless, this study assesses the image quality gap quantitatively, giving indications on the benefits associated with SR BCT and providing a benchmarking basis for its clinical implementation. In addition, SR-based studies can provide a gold-standard in terms of achievable image quality, constituting an upper-limit to the potential clinical development of a given technique.

Validation of algorithmic CT image quality metrics with preferences of radiologists

PURPOSE

Automated assessment of perceptual image quality on clinical Computed Tomography (CT) data by computer algorithms has the potential to greatly facilitate data-driven monitoring and optimization of CT image acquisition protocols. The application of these techniques in clinical operation requires the knowledge of how the output of the computer algorithms corresponds to clinical expectations. This study addressed the need to validate algorithmic image quality measurements on clinical CT images with preferences of radiologists and determine the clinically acceptable range of algorithmic measurements for abdominal CT examinations.

MATERIALS AND METHODS

Algorithmic measurements of image quality metrics (organ HU, noise magnitude, and clarity) were performed on a clinical CT image dataset with supplemental measures of noise power spectrum from phantom images using techniques developed previously. The algorithmic measurements were compared to clinical expectations of image quality in an observer study with seven radiologists. Sets of CT liver images were selected from the dataset where images in the same set varied in terms of one metric at a time. These sets of images were shown via a web interface to one observer at a time. First, the observer rank ordered the CT images in a set according to his/her preference for the varying metric. The observer then selected his/her preferred acceptable range of the metric within the ranked images. The agreement between algorithmic and observer rankings of image quality were investigated and the clinically acceptable image quality in terms of algorithmic measurements were determined.

RESULTS

The overall rank-order agreements between algorithmic and observer assessments were 0.90, 0.98, and 1.00 for noise magnitude, liver parenchyma HU, and clarity, respectively. The results indicate a strong agreement between the algorithmic and observer assessments of image quality. Clinically acceptable thresholds (median) of algorithmic metric values were (17.8, 32.6) HU for noise magnitude, (92.1, 131.9) for liver parenchyma HU, and (0.47, 0.52) for clarity.

CONCLUSIONS

The observer study results indicated that these algorithms can robustly assess the perceptual quality of clinical CT images in an automated fashion. Clinically acceptable ranges of algorithmic measurements were determined. The correspondence of these image quality assessment algorithms to clinical expectations paves the way toward establishing diagnostic reference levels in terms of clinically acceptable perceptual image quality and data-driven optimization of CT image acquisition protocols.

Task-based image quality assessment in radiation therapy: initial characterization and demonstration with computer-simulation study

In the majority of current radiation therapy (RT) applications, image quality is still assessed subjectively or by utilizing physical measures. A novel theory that applies objective task-based image quality assessment in radiation therapy (IQA-in-RT) was recently proposed, in which the area under the therapeutic operating characteristic curve (AUTOC) was employed as the figure-of-merit (FOM) for evaluating RT effectiveness. Although theoretically more appealing than conventional subjective or physical measures, a comprehensive implementation and evaluation of this novel task-based IQA-in-RT theory is required for its further application in improving clinical RT. In this work, a practical and modular IQA-in-RT framework is presented for implementing this theory for the assessment of imaging components on the basis of RT treatment outcomes. Computer-simulation studies are conducted to demonstrate the feasibility and utility of the proposed IQA-in-RT framework in optimizing x-ray computed tomography (CT) pre-treatment imaging, including the optimization of CT imaging dose and image reconstruction parameters. The potential advantages of optimizing imaging components in the RT workflow by use of the AUTOC as the FOM are also compared against those of other physical measures. The results demonstrate that optimization using the AUTOC leads to selecting different parameters from those indicated by physical measures, potentially improving RT performance. The sources of systemic randomness and bias that affect the determination of the AUTOC are also analyzed. The presented work provides a practical solution for the further investigation and analysis of the task-based IQA-in-RT theory and advances its applications in improving RT clinical practice and cancer patient care.

Evaluation of three-dimensional iterative image reconstruction in C-arm-based interventional cone-beam CT: A phantom study in comparison with customary reconstruction technique

We compared images obtained using a three-dimensional iterative image reconstruction (3D-IIR) algorithm for C-arm-based interventional cone-beam computed tomography (CBCT) with that using the customary reconstruction technique to quantify the effect of reconstruction techniques on image quality.We scanned 2 phantoms using an angiography unit with digital flat-panel system-an elliptical cylinder acrylic phantom to evaluate spatial resolution and a Catphan phantom to evaluate CT number linearity, image noise, and low-contrast resolution. Three-dimensional imaging was calculated using Feldkamp algorithms, and additional image sets were reconstructed using 3D-IIR at 5 settings (Sharp, Default, Soft+, Soft++, Soft+++). We evaluated quality of images obtained using the 6 reconstruction techniques and analyzed variance to test values of the 10% value of each MTF, mean CT number, and contrast-to-noise ratio (CNR), with P < .05 considered statistically significant.Modulation transfer function curves and CT number linearity among images obtained using the customary technique and the 5 3D-IIR techniques showed excellent agreement. Noise power spectrum curves demonstrated uniform noise reduction across the spatial frequency in the iterative reconstruction, and CNR obtained using all but the Sharp 3D-IIR technique was significantly better than that using the customary reconstruction technique (Sharp, P = .1957; Default, P = .0042; others, P < .0001). Use of 3D-IIR, especially the Soft++ and Soft+++ settings, improved visualization of low-contrast targets.Use of a 3D-IIR can significantly improve image noise and low-contrast resolution while maintaining spatial resolution in C-arm-based interventional CBCT, yielding higher quality images that may increase safety and efficacy in interventional radiology.

Ultra-low dose chest computed tomography: Effect of iterative reconstruction levels on image quality

PURPOSE

To optimize image quality and radiation dose of chest CT with respect to various iterative reconstruction levels, detector collimations and body sizes.

METHOD

A Kyoto Kagaku Lungman with and without extensions was scanned using fixed ultra-low doses of 0.25, 0.49 and 0.74 mGy CTDI_vol, and collimations of 40 and 80 mm. Images were reconstructed with the lung kernel, filtered back projection (FBP) and different ASIR-V levels (10-100%). Contrast-to-noise ratios (CNR) were calculated for 12 mm simulated lesions of different densities in the lung. Image noise, signal-to-noise ratios (SNR), variations in Hounsfield units (HU), noise power spectrum (NPS) and noise texture deviations (NTD) were evaluated for all reconstructions. NTD was calculated as percentage of pixels outside 3 standard deviations to evaluate IR-specific artefacts.

RESULTS

Compared to the FBP, image noise reduced (5-55%) with ASIR-V levels irrespective of dose or collimation. SNR correlated positively (r ≥ 0.925, p ≤ 0.001) with ASIR-V levels at all doses, collimations, and phantom sizes. ASIR-V enhanced the CNR of the lesion with the lowest contrast from 12.7-42.1 (0-100% ASIR-V) at 0.74 mGy with 40 mm collimation. As expected, higher SNR and CNR were measured in the smaller phantom than the bigger phantom. Uniform HU were observed between FBP and ASIR-V levels at all doses, collimations, and phantom sizes. NPS curves left-shifted towards lower frequencies at increasing levels of ASIR-V irrespective of collimation. A positive correlation (r ≥ 0.946, p ≥ 0.001) was observed between NTD and ASIR-V levels. NTD of the FBP was not significantly (p ≤ 0.087) different from NTD of ASIR-V ≤ 20%. The data from the NPS and NTD indicates a blotchier and coarser noise texture at higher levels of ASIR-V, especially at 100% ASIR-V.

CONCLUSION

In comparison with the FBP technique, ASIR-V enhanced quantitative image quality parameters at all ultra-low doses tested. Moreover, the use of ASIR-V showed consistency with body size and collimation. Hence, ASIR-V may be useful for improving image quality of chest CT at ultra-low doses.

Application of fractal dimension for quantifying noise texture in computed tomography images

PURPOSE

Evaluation of noise texture information in CT images is important for assessing image quality. Noise texture is often quantified by the noise power spectrum (NPS), which requires numerous image realizations to estimate. This study evaluated fractal dimension for quantifying noise texture as a scalar metric that can potentially be estimated using one image realization.

METHODS

The American College of Radiology CT accreditation phantom (ACR) was scanned on a clinical scanner (Discovery CT750, GE Healthcare) at 120 kV and 25 and 90 mAs. Images were reconstructed using filtered back projection (FBP/ASIR 0%) with varying reconstruction kernels: Soft, Standard, Detail, Chest, Lung, Bone, and Edge. For each kernel, images were also reconstructed using ASIR 50% and ASIR 100% iterative reconstruction (IR) methods. Fractal dimension was estimated using the differential box-counting algorithm applied to images of the uniform section of ACR phantom. The two-dimensional Noise Power Spectrum (NPS) and one-dimensional-radially averaged NPS were estimated using established techniques. By changing the radiation dose, the effect of noise magnitude on fractal dimension was evaluated. The Spearman correlation between the fractal dimension and the frequency of the NPS peak was calculated. The number of images required to reliably estimate fractal dimension was determined and compared to the number of images required to estimate the NPS-peak frequency. The effect of Region of Interest (ROI) size on fractal dimension estimation was evaluated. Feasibility of estimating fractal dimension in an anthropomorphic phantom and clinical image was also investigated, with the resulting fractal dimension compared to that estimated within the uniform section of the ACR phantom.

RESULTS

Fractal dimension was strongly correlated with the frequency of the peak of the radially averaged NPS curve, having a Spearman rank-order coefficient of 0.98 (P-value < 0.01) for ASIR 0%. The mean fractal dimension at ASIR 0% was 2.49 (Soft), 2.51 (Standard), 2.52 (Detail), 2.57 (Chest), 2.61 (Lung), 2.66 (Bone), and 2.7 (Edge). A reduction in fractal dimension was observed with increasing ASIR levels for all investigated reconstruction kernels. Fractal dimension was found to be independent of noise magnitude. Fractal dimension was successfully estimated from four ROIs of size 64 × 64 pixels or one ROI of 128 × 128 pixels. Fractal dimension was found to be sensitive to non-noise structures in the image, such as ring artifacts and anatomical structure. Fractal dimension estimated within a uniform region of an anthropomorphic phantom and clinical head image matched that estimated within the ACR phantom for filtered back projection reconstruction.

CONCLUSIONS

Fractal dimension correlated with the NPS-peak frequency and was independent of noise magnitude, suggesting that the scalar metric of fractal dimension can be used to quantify the change in noise texture across reconstruction approaches. Results demonstrated that fractal dimension can be estimated from four, 64 × 64-pixel ROIs or one 128 × 128 ROI within a head CT image, which may make it amenable for quantifying noise texture within clinical images.

An effective noise reduction method for multi-energy CT images that exploit spatio-spectral features

PURPOSE

To develop and evaluate an image-domain noise reduction method for multi-energy CT (MECT) data.

METHODS

Multi-Energy Non-Local Means (MENLM) is a technique that uses the redundant information in MECT images to achieve noise reduction. In this method, spatio-spectral features are used to determine the similarity between pixels, making the similarity evaluation more robust to image noise. The performance of this MENLM filter was tested on images acquired on a whole-body research photon counting CT system. The impact of filtering on image quality was quantitatively evaluated in phantom studies in terms of image noise level (standard deviation of pixel values), noise power spectrum (NPS), in-plane and cross-plane spatial resolution, CT number accuracy, material decomposition performance, and subjective low-contrast spatial resolution using the American College of Radiology (ACR) CT accreditation phantom. Clinical feasibility was assessed by performing MENLM on contrast-enhanced swine images and unenhanced cadaver head images using clinically relevant doses and dose rates.

RESULTS

The phantom studies demonstrated that the MENLM filter reduced noise substantially and still preserved the shape and peak frequency of the NPS. With 80% noise reduction, MENLM filtering caused no degradation of high-contrast spatial resolution, as illustrated by the modulation transfer function (MTF) and slice sensitivity profile (SSP). CT number accuracy was also maintained for all energy channels, demonstrating that energy resolution was not affected by filtering. Material decomposition performance was improved with MENLM filtering. The subjective evaluation using the ACR phantom demonstrated an improvement in low-contrast performance. MENLM achieved effective noise reduction in both contrast-enhanced swine images and unenhanced cadaver head images, resulting in improved detection of subtle vascular structures and the differentiation of white/gray matter.

CONCLUSION

In MECT, MENLM achieved around 80% noise reduction and greatly improved material decomposition performance and the detection of subtle anatomical/low-contrast features while maintaining spatial and energy resolution. MENLM filtering may improve diagnostic or functional analysis accuracy and facilitate radiation dose and contrast media reduction for MECT.

Quantitation of clinical feedback on image quality differences between two CT scanner models

The aim of this work was to quantitate differences in image quality between two GE CT scanner models - the LightSpeed VCT ("VCT") and Discovery HD750 ("HD") - based upon feedback from radiologists at our institution. First, 3 yrs of daily QC images of the manufacturer-provided QC phantom from 10 scanners - five of each model - were analyzed for both noise magnitude, measured as CT-number standard deviation, and noise power spectrum within the uniform water section. The same phantom was then scanned on four of each model and analyzed for low contrast detectability (LCD) using a built-in LCD tool at the scanner console. An anthropomorphic phantom was scanned using the same eight scanners. A slice within the abdomen section was chosen and three ROIs were placed in regions representing liver, stomach, and spleen. Both standard deviation of CT-number and LCD value was calculated for each image. Noise magnitude was 8.5% higher in HD scanners compared to VCT scanners. An associated increase in the magnitude of the noise power spectra were also found, but both peak and mean NPS frequency were not different between the two models. VCT scanners outperformed HD scanners with respect to LCD by an average of 13.1% across all scanners and phantoms. Our results agree with radiologist feedback, and necessitate a closer look at our body CT protocols among different scanner models at our institution.