Expanding the Concept of Diagnostic Reference Levels to Noise and Dose Reference Levels in CT

Myelography Using Energy-Integrating Detector CT Versus Photon-Counting Detector CT for Detection of CSF-Venous Fistulas in Patients With Spontaneous Intracranial Hypotension

BACKGROUND. CSF-venous fistulas (CVFs), which are an increasingly recognized cause of spontaneous intracranial hypotension (SIH), are often diminutive in size and exceedingly difficult to detect by conventional imaging. OBJECTIVE. This purpose of this study was to compare energy-integrating detector (EID) CT myelography and photon-counting detector (PCD) CT myelography in terms of image quality and diagnostic performance for detecting CVFs in patients with SIH. METHODS. This retrospective study included 38 patients (15 men and 23 women; mean age, 55 ± 10 [SD] years) with SIH who underwent both clinically indicated EID CT myelography (slice thickness, 0.625 mm) and PCD CT myelography (slice thickness, 0.2 mm; performed in ultrahigh-resolution mode) to assess for CSF leak. Three blinded radiologists reviewed examinations in random order, assessing image noise, discernibility of spinal nerve root sleeves, and overall image quality (each assessed using a scale of 0-100, with 100 denoting highest quality) and recording locations of the CVFs. Definite CVFs were defined as CVFs described in CT myelography reports using unequivocal language and having an attenuation value greater than 70 HU. RESULTS. For all readers, PCD CT myelography, in comparison with EID CT myelography, showed higher mean image noise (reader 1: 69.9 ± 18.5 [SD] vs 37.6 ± 15.2; reader 2: 59.5 ± 8.7 vs 49.3 ± 12.7; and reader 3: 57.6 ± 13.2 vs 42.1 ± 16.6), higher mean nerve root sleeve discernibility (reader 1: 81.6 ± 21.7 [SD] vs 30.4 ± 13.6; reader 2: 83.6 ± 10 vs 70.1 ± 18.9; and reader 3: 59.6 ± 13.5 vs 50.5 ± 14.4), and higher mean overall image quality (reader 1: 83.2 ± 20.0 [SD] vs 38.1 ± 13.5; reader 2: 80.1 ± 10.1 vs 72.4 ± 19.8; and reader 3: 57.8 ± 11.2 vs 51.9 ± 13.6) (all p < .05). Eleven patients had a definite CVF. Sensitivity and specificity of EID CT myelography and PCD CT myelography for the detection of definite CVF were 45% and 96% versus 64% and 85%, respectively, for reader 1; 36% and 100% versus 55% and 96%, respectively, for reader 2; and 57% and 100% versus 55% and 93%, respectively, for reader 3. The sensitivity was significantly higher for PCD CT myelography than for EID CT myelography for reader 1 and reader 2 (both p < .05) and was not significantly different between the two techniques for reader 3 (p = .45); for all three readers, specificity was not significantly different between the two modalities (all p > .05). CONCLUSION. In comparison with EID CT myelography, PCD CT myelography yielded significantly improved image quality with significantly higher sensitivity for CVFs (for two of three readers), without significant loss of specificity. CLINICAL IMPACT. The findings support a potential role for PCD CT myelography in facilitating earlier diagnosis and targeted treatment of SIH, avoiding high morbidity during potentially prolonged diagnostic workups.

Revising and exploring the variations in methodologies for establishing the diagnostic reference levels for paediatric PET/CT imaging

PET-computed tomography (PET/CT) is a hybrid imaging technique that combines anatomical and functional information; to investigate primary cancers, stage tumours, and track treatment response in paediatric oncology patients. However, there is debate in the literature about whether PET/CT could increase the risk of cancer in children, as the machine is utilizing two types of radiation, and paediatric patients have faster cell division and longer life expectancy. Therefore, it is essential to minimize radiation exposure by justifying and optimizing PET/CT examinations and ensure an acceptable image quality. Establishing diagnostic reference levels (DRLs) is a crucial quantitative indicator and effective tool to optimize paediatric imaging procedures. This review aimed to distinguish and acknowledge variations among published DRLs for paediatric patients in PET/CT procedures. A search of relevant articles was conducted using databases, that is, Embase, Scopus, Web of Science, and Medline, using the keywords: PET-computed tomography, computed tomography, PET, radiopharmaceutical, DRL, and their synonyms. Only English and full-text articles were included, with no limitations on the publication year. After the screening, four articles were selected, and the review reveals different DRL approaches for paediatric patients undergoing PET/CT, with primary variations observed in patient selection criteria, reporting of radiation dose values, and PET/CT equipment. The study suggests that future DRL methods for paediatric patients should prioritize data collection in accordance with international guidelines to better understand PET/CT dose discrepancies while also striving to optimize radiation doses without compromising the quality of PET/CT images.

Size based dependence of patient dose metrics, and image quality metrics for clinical indicator-based imaging protocols in abdominal CT procedures

INTRODUCTION

Diagnostic reference level (DRL) values for computed tomography (CT) based on clinical indication are warranted since imaging protocols are indication-dependent. This study proposes clinical DRL values using the CT dose metrics and five patient size-related parameters while considering image quality.

METHODS

The volumetric CT dose index (CTDIvol), dose-length product (DLP) and five size-related parameters of size-specific dose estimates (SSDE), namely the anterior-posterior (AP) dimension, lateral (LAT) dimension, sum dimension, effective diameter, and the body mass index (BMI), were used to calculate DRL values for CT chest-abdomen-pelvis (CAP) and abdomen-pelvis (AbP) protocols. DRL values of the clinical indications for cancer, urinary system stones and other pathologies were assessed based on the BMI classifications using the median and 75th percentile. An image subtraction algorithm was used to assess the image quality metrics (IQM) of the CT images.

RESULTS

The 75th percentile for SSDEAP dimension for CAP cancer was 19.7, 14.9 and 12.7 mGy at Hospitals A, C and E, respectively. The median DLP for other AbP pathologies was 556.3, 1452.0 and 1960.7 mGy.cm for normal weight, overweight and obese patients, respectively, at Hospital A. The image quality varied among BMI classifications for different clinically indicated examinations. Although the dose increased with BMI, the image quality index was consistent because automatic tube current modulation (ATCM) was used.

CONCLUSION

DRL values are influenced by patient size-related parameters and the clinical indication protocols, while the image quality index is independent of the BMI.

IMPLICATIONS FOR PRACTICE

Size-related clinical DRL values and image quality index can be used to monitor and optimise dose and image quality. Acquisition parameters and image quality indexes should be investigated and adjusted when unusually high DRL values are noted.

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

OBJECTIVE

To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT.

METHODS

We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis.

RESULTS

Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths.

CONCLUSION

Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.

FACTORS INFLUENCING SIZE-SPECIFIC DOSE ESTIMATES OF SELECTED COMPUTED TOMOGRAPHY PROTOCOLS AT TWO CLINICAL PRACTICES IN SOUTH AFRICA

The study aimed to determine the factors that impact the size-specific dose estimate (SSDE) for computed tomography (CT) examinations of the chest-abdomen-pelvis and abdomen-pelvis protocols in two clinical radiology practices and evaluate the image quality of these protocols. Imaging parameters, protocols, dose metrics from the CT units and size-related parameters to calculate the SSDE were documented. The image quality of the CT images was assessed using an image subtraction algorithm. The SSDE increased as the volumetric CT dose index (CTDIvol), and the patient's body mass index increased, respectively. Significant differences (p < 0.001) occurred between the two hospitals regarding image quality. However, these differences were not indicative of differences in the diagnostic performances for task-based imaging protocols. Different clinical protocols should be reviewed to optimise dose. The inclusion of the pre-monitoring sequence, age of the machine and the scan requisition parameters impacted the SSDEs. Image quality should be assessed to evaluate the consistency of image quality between protocols applied by different CT units when assessing SSDEs.

Making CT Dose Monitoring Meaningful: Augmenting Dose with Imaging Quality

Due to the concerns about radiation dose associated with medical imaging, radiation dose monitoring systems (RDMSs) are now utilized by many radiology providers to collect, process, analyze, and manage radiation dose-related information. Currently, most commercially available RDMSs focus only on radiation dose information and do not track any metrics related to image quality. However, to enable comprehensive patient-based imaging optimization, it is equally important to monitor image quality as well. This article describes how RDMS design can be extended beyond radiation dose to simultaneously monitor image quality. A newly designed interface was evaluated by different groups of radiology professionals (radiologists, technologists, and physicists) on a Likert scale. The results show that the new design is effective in assessing both image quality and safety in clinical practices, with an overall average score of 7.8 out of 10.0 and scores ranging from 5.5 to 10.0. Radiologists rated the interface highest at 8.4 out of 10.0, followed by technologists at 7.6 out of 10.0, and medical physicists at 7.5 out of 10.0. This work demonstrates how the assessment of the radiation dose can be performed in conjunction with the image quality using customizable user interfaces based on the clinical needs associated with different radiology professions.

Effectiveness of body size stratification for patient exposure optimization in Computed Tomography

PURPOSE

To establish size-dependent DRL and to estimate the effectiveness of the size-dependent DRLs over size-independent DRLs for a CT exposure optimization process.

METHODS

The study included 16,933 adult CT body examinations of the most common CT protocols. Acquisitions were included following an image quality assessment. Patients were grouped into five different classes by means of the water equivalent diameter (D_w): 21 ≤ D_w < 25, 25 ≤ D_w < 29, 29 ≤ D_w < 33,33 ≤ D_w < 37 (in cm). CTDI_vol, DLP, DLP_tot. and SSDE median values were provided both for the sample as a whole (size-independent approach) and for each D_w class (size-dependent approach). The performance of the two approaches in classifying sub-optimal examinations was evaluated through the confusion matrix and Matthews Correlation Coefficient (MCC) metric. The 75th percentile of the CTDI_vol distribution was arbitrarily chosen as a threshold level above which the acquisitions are considered sub-optimal.

RESULTS

CTDI_vol, DLP, DLP_tot and SSDE typical values (median values) are statistically different across D_w groups. The confusion matrix analysis suggests that size-independent DRL could not mark potential suboptimal protocols for small and large patients. The agreement between the size-dependent and size-independent methods is strong only for the most populous classes (MCC > 0.7). For small and large patients size-independent approach fails to identify as sub-optimal around 20 % of the acquisition (MCC≪0.2).

CONCLUSIONS

It was proven by means of the confusion matrix and MCC metric that stratifying DRLs by patient size, size-dependent DRL can be a powerful strategy in order to improve the dose optimization process shown that a size-independent DRL fails to identify sub-optimal examinations for small and large patients.

Establishment of diagnostic reference levels in low-dose renal computed tomography

BACKGROUND

Increased radiation doses from computed tomography (CT) examinations is well known with proven risks of inducing cancers for effective dose >100 mSv (according to some studies >50 mSvs).

PURPOSE

To establish the diagnostic reference level (DRL) for low-dose renal CT examinations in the evaluation of renal stones.

MATERIAL AND METHODS

Patient demographics, CT parameters, and dosimetric indices (CTDI_vol and dose length product [DLP]) were collected from 12 tertiary hospitals that routinely perform renal CT in the detection and evaluation of renal stones over a period of 12 weeks. Data obtained from 1418 average-sized patients in each category were recorded. The median values of dosimetric indices for each site were calculated. The DRL values were defined as the 75th percentile of the distribution of the median values of CTDI_vol and DLP.

RESULTS

There were no significant differences between patient demographics. Mean kVp and mAs for protocols were 121.67 ± 11.56 and 226.91 ± 78.44, respectively. The CTDI_vol values were in the range of 2-36.2 mGy, while the DLP values were in the range of 43-1942 mGy.cm. The DRL for the CTDI_vol was 16.15 mGy and for the DLP 851.77 mGy.cm. The local median values of CDTI_vol and DLP are higher than DRL in two hospitals.

CONCLUSION

Comparison of local median values of CDTI_vol and DLP with DRL suggests the needs of an optimization strategy in some hospitals.

Optimized Camera-Based Patient Positioning in CT: Impact on Radiation Exposure

OBJECTIVE

The aim of this study was to evaluate whether a 3-dimensional (3D) camera can outperform highly trained technicians in precision of patient positioning and whether this transforms into a reduction in patient exposure.

MATERIALS AND METHODS

In a single-center study, 3118 patients underwent computer tomography (CT) scans of the chest and/or abdomen on a latest generation single-source CT scanner supported with an automated patient positioning system by 3D camera. One thousand five hundred fifty-seven patients were positioned laser-guided by a highly trained radiographer (camera off) and 1561 patients with 3D camera (camera on) guidance. Radiation parameters such as effective dose, organ doses, CT dose index, and dose length product were analyzed and compared. Isocenter accuracy and table height were evaluated between the 2 groups.

RESULTS

Isocenter positioning was significantly improved with the 3D camera ( P < 0.001) as compared with visual laser-guided positioning. Absolute table height differed significantly ( P < 0.001), being higher with camera positioning (165.6 ± 16.2 mm) as compared with laser-guided positioning (170.0 ± 20.4 mm). Radiation exposure decreased using the 3D camera as indicated by dose length product (321.1 ± 266.6 mGy·cm; camera off: 342.0 ± 280.7 mGy·cm; P = 0.033), effective dose (3.3 ± 2.7 mSv; camera off: 3.5 ± 2.9; P = 0.053), and CT dose index (6.4 ± 4.3 mGy; camera off: 6.8 ± 4.6 mGy; P = 0.011). Exposure of radiation-sensitive organs such as colon ( P = 0.015) and red bone marrow ( P = 0.049) were also lower using the camera.

CONCLUSIONS

The introduction of a 3D camera improves patient positioning in the isocenter of the scanner, which results in a lower and also better balanced dose reduction for the patients.

Oncology-specific radiation dose and image noise reference levels in adult abdominal-pelvic CT

OBJECTIVES

To provide our oncology-specific adult abdominal-pelvic CT reference levels for image noise and radiation dose from a high-volume, oncologic, tertiary referral center.

METHODS

The portal venous phase abdomen-pelvis acquisition was assessed for image noise and radiation dose in 13,320 contrast-enhanced CT examinations. Patient size (effective diameter) and radiation dose (CTDIvol) were recorded using a commercial software system, and image noise (Global Noise metric) was quantified using a custom processing system. The reference level and range for dose and noise were calculated for the full dataset, and for examinations grouped by CT scanner model. Dose and noise reference levels were also calculated for exams grouped by five different patient size categories.

RESULTS

The noise reference level was 11.25 HU with a reference range of 10.25-12.25 HU. The dose reference level at a median effective diameter of 30.7 cm was 26.7 mGy with a reference range of 19.6-37.0 mGy. Dose increased with patient size; however, image noise remained approximately constant within the noise reference range. The doses were 2.1-2.5 times than the doses in the ACR DIR registry for corresponding patient sizes. The image noise was 0.63-0.75 times the previously published reference level in abdominal-pelvic CT examinations.

CONCLUSIONS

Our oncology-specific abdominal-pelvic CT dose reference levels are higher than in the ACR dose index registry and our oncology-specific image noise reference levels are lower than previously proposed image noise reference levels.

ADVANCES IN KNOWLEDGE

This study reports reference image noise and radiation dose levels appropriate for the indication of abdomen-pelvis CT examination for cancer diagnosis and staging. The difference in these reference levels from non-oncology-specific CT examinations highlight a need for indication-specific, dose index and image quality reference registries.

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.

The cumulative radiation dose paradigm in pediatric imaging

Medical imaging professionals have an accountability for both quality and safety in the care of patients that have unexpected or anticipated repeated imaging examinations that use ionizing radiation. One measure in the safety realm for repeated imaging is cumulative effective dose (CED). CED has been increasingly scrutinized in patient populations, including adults and children. Recognizing the challenges with effective dose, including the cumulative nature, effective dose is still the most prevalent exposure currency for recurrent imaging examinations. While the responsibility for dose monitoring incorporates an element of tracking an individual patient cumulative radiation record, a more complex aspect is what should be done with this information. This challenge also differs between the pediatric and adult population, including the fact that high cumulative doses (e.g.,>100 mSv) are reported to occur much less frequently in children than in the adult population. It is worthwhile, then, to review the general construct of CED, including the comparison between the relative percentage occurrence in adult and pediatric populations, the relevant pediatric medical settings in which high CED occurs, the advances in medical care that may affect CED determinations in the future, and offer proposals for the application of the CED paradigm, considering the unique aspects of pediatric care.

Comparison of radiation dose and image quality between contrast-enhanced single- and dual-energy abdominopelvic computed tomography in children as a function of patient size

BACKGROUND

Widespread adoption of dual-energy computed tomography (DECT) requires evidence it does not cause higher radiation dose than conventional single-energy CT (SECT). While a few publications involving pediatric patients exist, most have focused on small cohorts. Hence, there is still a need for studies that ascertain what radiation doses are expected in larger populations that include representative ranges of patient sizes and ages.

OBJECTIVE

To compare radiation dose and image quality of DECT and SECT abdominopelvic examinations in children as a function of patient size.

MATERIALS AND METHODS

This retrospective study included 860 children (age range: 12.3±5.3 years) who underwent contrast-enhanced abdominopelvic exams on second-generation dual-source CT in a five-year period. Two groups, SECT and DECT, consisting of 430 children each, were matched by 5 effective diameters. Volume CT dose index (CTDI_vol) and size-specific dose estimate (SSDE) were analyzed as a function of effective diameter. Objective image quality was compared between the groups.

RESULTS

DECT SSDEs were lower across all effective patient diameters compared with SECT (mean: 8.5±1.8 mGv vs. 9.3±2.0 mGv, respectively, P≤0.001). DECT CTDI_vol was lower compared to SECT (mean: 5.6±2.4 mGv vs. 6.1±2.7 mGv, respectively, P≤0.001) except in the smallest diameter group (<15 cm) where it was comparable to SECT (P=0.065). Objective image quality versus effective diameter between the two CT groups was comparable (P>0.05).

CONCLUSION

In children, regardless of effective diameter, contrast-enhanced abdominopelvic DECT can be performed with a similar or lower dose and similar image quality compared with SECT examinations.

Assessment of the global noise algorithm for automatic noise measurement in head CT examinations

PURPOSE

The global noise (GN) algorithm has been previously introduced as a method for automatic noise measurement in clinical CT images. The accuracy of the GN algorithm has been assessed in abdomen CT examinations, but not in any other body part until now. This work assesses the GN algorithm accuracy in automatic noise measurement in head CT examinations.

METHODS

A publicly available image dataset of 99 head CT examinations was used to evaluate the accuracy of the GN algorithm in comparison to reference noise values. Reference noise values were acquired using a manual noise measurement procedure. The procedure used a consistent instruction protocol and multiple observers to mitigate the influence of intra- and interobserver variation, resulting in precise reference values. Optimal GN algorithm parameter values were determined. The GN algorithm accuracy and the corresponding statistical confidence interval were determined. The GN measurements were compared across the six different scan protocols used in this dataset. The correlation of GN to patient head size was also assessed using a linear regression model, and the CT scanner's X-ray beam quality was inferred from the model fit parameters.

RESULTS

Across all head CT examinations in the dataset, the range of reference noise was 2.9-10.2 HU. A precision of ±0.33 HU was achieved in the reference noise measurements. After optimization, the GN algorithm had a RMS error 0.34 HU corresponding to a percent RMS error of 6.6%. The GN algorithm had a bias of +3.9%. Statistically significant differences in GN were detected in 11 out of the 15 different pairs of scan protocols. The GN measurements were correlated with head size with a statistically significant regression slope parameter (p < 10-7 ). The CT scanner X-ray beam quality estimated from the slope parameter was 3.5 cm water HVL (2.8-4.8 cm 95% CI).

CONCLUSION

The GN algorithm was validated for application in head CT examinations. The GN algorithm was accurate in comparison to reference manual measurement, with errors comparable to interobserver variation in manual measurement. The GN algorithm can detect noise differences in examinations performed on different scanner models or using different scan protocols. The trend in GN across patients of different head sizes closely follows that predicted by a physical model of X-ray attenuation.

Comparison of 12 surrogates to characterize CT radiation risk across a clinical population

OBJECTIVES

Quantifying radiation burden is essential for justification, optimization, and personalization of CT procedures and can be characterized by a variety of risk surrogates inducing different radiological risk reflections. This study compared how twelve such metrics can characterize risk across patient populations.

METHODS

This study included 1394 CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogates were considered: volume computed tomography dose index (CTDIvol), dose-length product (DLP), size-specific dose estimate (SSDE), DLP-based effective dose (EDk ), dose to a defining organ (ODD), effective dose and risk index based on organ doses (EDOD, RI), and risk index for a 20-year-old patient (RIrp). The last three metrics were also calculated for a reference ICRP-110 model (ODD,0, ED0, and RI0). Lastly, motivated by the ICRP, an adjusted-effective dose was calculated as [Formula: see text]. A linear regression was applied to assess each metric's dependency on RI. The results were characterized in terms of risk sensitivity index (RSI) and risk differentiability index (RDI).

RESULTS

The analysis reported significant differences between the metrics with EDr showing the best concordance with RI in terms of RSI and RDI. Across all metrics and protocols, RSI ranged between 0.37 (SSDE) and 1.29 (RI0); RDI ranged between 0.39 (EDk) and 0.01 (EDr) cancers × 103patients × 100 mGy.

CONCLUSION

Different risk surrogates lead to different population risk characterizations. EDr exhibited a close characterization of population risk, also showing the best differentiability. Care should be exercised in drawing risk predictions from unrepresentative risk metrics applied to a population.

KEY POINTS

• Radiation risk characterization in CT populations is strongly affected by the surrogate used to describe it. • Different risk surrogates can lead to different characterization of population risk. • Healthcare professionals should exercise care in ascribing an implicit risk to factors that do not closely reflect risk.

Variability in image quality and radiation dose within and across 97 medical facilities

Purpose: To characterize variability in image quality and radiation dose across a large cohort of computed tomography (CT) examinations and identify the scan factors with the highest influence on the observed variabilities. Approach: This retrospective institutional-review-board-exempt investigation was performed on 87,629 chest and abdomen-pelvis CT scans acquired for 97 facilities from 2018 to 2019. Images were assessed in terms of noise, resolution, and dose metrics (global noise, frequency in which modulation transfer function is at 0.50, and volumetric CT dose index, respectively). The results were fit to linear mixed-effects models to quantify the variabilities as affected by scan parameters and settings and patient characteristics. A list of factors, ranked by t -value with p < 0.05 , was ascertained for each of the six mixed effects models. A type III p -value test was used to assess the influence of facility. Results: Across different facilities, image quality and dose were significantly different ( p < 0.05 ), with little correlation between their mean magnitudes and consistency (Pearson's correlation coefficient < 0.34 ). Scanner model, slice thickness, recon field-of-view and kernel, mAs, kVp, patient size, and centering were the most influential factors. The two body regions exhibited similar rankings of these factors for noise (Spearman's correlation coefficient = 0.76 ) and dose (Spearman's correlation coefficient = 0.86 ) but not for resolution (Spearman's correlation coefficient = 0.52 ). Conclusions: Clinical CT scans can vary in image quality and dose with broad implications for diagnostic utility and radiation burden. Average scan quality was not correlated with interpatient scan-quality consistency. For a given facility, this variability can be quite large, with magnitude differences across facilities. The knowledge of the most influential factors per body region may be used to better manage these variabilities within and across facilities.

A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials

Virtual imaging trials (VITs), defined as the process of conducting clinical imaging trials using computer simulations, offer a time- and cost-effective alternative to traditional imaging trials for CT. The clinical potential of VITs hinges on the realism of simulations modeling the image acquisition process, where the accurate scanner-specific simulation of scatter in a time-feasible manner poses a particular challenge. To meet this need, this study proposes, develops, and validates a rapid scatter estimation framework, based on GPU-accelerated Monte Carlo (MC) simulations and denoising methods, for estimating scatter in single source, dual-source, and photon-counting CT. A CT simulator was developed to incorporate parametric models for an anti-scatter grid and a curved energy integrating detector with an energy-dependent response. The scatter estimates from the simulator were validated using physical measurements acquired on a clinical CT system using the standard single-blocker method. The MC simulator was further extended to incorporate a pre-validated model for a PCD and an additional source-detector pair to model cross scatter in dual-source configurations. To estimate scatter with desirable levels of statistical noise using a manageable computational load, two denoising methods using a (1) convolutional neural network and an (2) optimized Gaussian filter were further deployed. The viability of this framework for clinical VITs was assessed by integrating it with a scanner-specific ray-tracer program to simulate images for an image quality (Mercury) and an anthropomorphic phantom (XCAT). The simulated scatter-to-primary ratios agreed with physical measurements within 4.4% ± 10.8% across all projection angles and kVs. The differences of ∼121 HU between images with and without scatter, signifying the importance of scatter for simulating clinical images. The denoising methods preserved the magnitudes and trends observed in the reference scatter distributions, with an averaged rRMSE value of 0.91 and 0.97 for the two methods, respectively. The execution time of ∼30 s for simulating scatter in a single projection with a desirable level of statistical noise indicates a major improvement in performance, making our tool an eligible candidate for conducting extensive VITs spanning multiple patients and scan protocols.

Virtual Imaging Trials for Coronavirus Disease (COVID-19)

OBJECTIVE. The virtual imaging trial is a unique framework that can greatly facilitate the assessment and optimization of imaging methods by emulating the imaging experiment using representative computational models of patients and validated imaging simulators. The purpose of this study was to show how virtual imaging trials can be adapted for imaging studies of coronavirus disease (COVID-19), enabling effective assessment and optimization of CT and radiography acquisitions and analysis tools for reliable imaging and management of COVID-19. MATERIALS AND METHODS. We developed the first computational models of patients with COVID-19 and as a proof of principle showed how they can be combined with imaging simulators for COVID-19 imaging studies. For the body habitus of the models, we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University. The morphologic features of COVID-19 abnormalities were segmented from 20 CT images of patients who had been confirmed to have COVID-19 and incorporated into XCAT models. Within a given disease area, the texture and material of the lung parenchyma in the XCAT were modified to match the properties observed in the clinical images. To show the utility, three developed COVID-19 computational phantoms were virtually imaged using a scanner-specific CT and radiography simulator. RESULTS. Subjectively, the simulated abnormalities were realistic in terms of shape and texture. Results showed that the contrast-to-noise ratios in the abnormal regions were 1.6, 3.0, and 3.6 for 5-, 25-, and 50-mAs images, respectively. CONCLUSION. The developed toolsets in this study provide the foundation for use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the COVID-19 pandemic.

A Benchmark for automatic noise measurement in clinical computed tomography

PURPOSE

Assessment of image quality directly in clinical image data is an important quality control objective as phantom-based testing does not fully represent image quality across patient variation. Computer algorithms for automatically measuring noise in clinical computed tomography (CT) images have been introduced, but the accuracy of these algorithms is unclear. This work benchmarks the accuracy of the global noise (GN) algorithm for automatic noise measurement in contrast-enhanced abdomen CT exams in comparison to precise reference noise measurements. The GN algorithm was further optimized compared to the previous report in the literature.

METHODS

Reference values of noise were established in a public image dataset of 82 contrast-enhanced abdomen CT exams. The reference noise values were obtained by manual regions-of-interest measurements of pixel standard deviation in the liver parenchyma according to an instruction protocol. Noise measurements taken by six observers were averaged together to improve reference noise statistical precision. The GN algorithm was used to automatically measure noise in each image set. The accuracy of the GN algorithm was determined in terms of RMS error compared to reference noise. The GN algorithm was optimized by conducting 1000 trials with random algorithm parameter values. The trial with the lowest RMS error was used to select optimum algorithm parameters.

RESULTS

The range of noise across CT image sets was 8.8-28.8 HU. Reference noise measurements were made with a precision of ±0.78 HU (95% confidence interval). The RMS error of automatic noise measurement was 0.93 HU (0.77-1.19 HU 95% confidence interval). The automatic noise measurements were equally accurate across image sets of varying noise magnitude. Optimum GN algorithm parameter values were: a kernel size of 7 pixels, and soft tissue lower and upper thresholds of 0 and 170 HU, respectively.

CONCLUSIONS

The performance of automatic noise measurement was benchmarked in a large clinical CT dataset. The study provides a framework for thorough validation of automatic clinical image quality measurement methods. The GN algorithm was optimized and validated for automatic measurement of soft-tissue noise in abdomen CT exams.

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.

Establishment of CTPA Local Diagnostic Reference Levels with Noise Magnitude as a Quality Indicator in a Tertiary Care Hospital

This study aimed to establish the local diagnostic reference levels (LDRLs) of computed tomography pulmonary angiography (CTPA) examinations based on body size with regard to noise magnitude as a quality indicator. The records of 127 patients (55 males and 72 females) who had undergone CTPAs using a 128-slice CT scanner were retrieved. The dose information, scanning acquisition parameters, and patient demographics were recorded in standardized forms. The body size of patients was categorized into three groups based on their anteroposterior body length: P1 (14–19 cm), P2 (19–24 cm), and P3 (24–31 cm), and the radiation dose exposure was statistically compared. The image noise was determined quantitatively by measuring the standard deviation of the region of interest (ROI) at five different arteries—the ascending and descending aorta, pulmonary trunk, and the left and right main pulmonary arteries. We observed that the LDRL values were significantly different between body sizes (p < 0.05), and the median values of the CT dose index volume (CTDI_vol) for P1, P2, and P3 were 6.13, 8.3, and 21.40 mGy, respectively. It was noted that the noise reference values were 23.78, 24.26, and 23.97 HU for P1, P2, and P3, respectively, which were not significantly different from each other (p > 0.05). The CTDI_vol of 9 mGy and dose length product (DLP) of 329 mGy∙cm in this study were lower than those reported by other studies conducted elsewhere. This study successfully established the LDRLs of a local healthcare institution with the inclusion of the noise magnitude, which is comparable with other established references.

A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images

Although there are many methods in the literature to eliminate noise from images, finding new methods remains a challenge in the field and, despite the complexity of existing methods, many of the methods do not reach a sufficient level of applicability, most often due to the relatively high calculation time. In addition, most existing methods perform well when the processed image is adapted to the algorithm, but otherwise fail or results in significant artifacts. The context of eliminating noise from images is similar to that of improving images and for this reason some notions necessary to understand the proposed method will be repeated. An adaptive spatial filter in the wavelet domain is proposed by soft truncation of the wavelet coefficients with threshold value adapted to the local statistics of the image and correction based on the hierarchical correlation map. The filter exploits, in a new way, both the inter-band and the bandwidth dependence of the wavelet coefficients, considering the minimization of computational resources.