Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance

An inventory of patient-image based risk/dose, image quality and body habitus/size metrics for adult abdomino-pelvic CT protocol optimisation

PURPOSE

Patient-specific protocol optimisation in abdomino-pelvic Computed Tomography (CT) requires measurement of body habitus/size (BH), sensitivity-specificity (surrogates image quality (IQ) metrics) and risk (surrogates often dose quantities) (RD). This work provides an updated inventory of metrics available for each of these three categories of optimisation variables derivable directly from patient measurements or images. We consider objective IQ metrics mostly in the spatial domain (i.e., those related directly to sharpness, contrast, noise quantity/texture and perceived detectability as these are used by radiologists to assess the acceptability or otherwise of patient images in practice).

MATERIALS AND METHODS

The search engine used was PubMed with the search period being 2010-2024. The key words used were: 'comput* tomography', 'CT', 'abdom*', 'dose', 'risk', 'SSDE', 'image quality', 'water equivalent diameter', 'size', 'body composition', 'habit*', 'BMI', 'obes*', 'overweight'. Since BH is critical for patient specific optimisation, articles correlating RD vs BH, and IQ vs BH were reviewed.

RESULTS

The inventory includes 11 BH, 12 IQ and 6 RD metrics. 25 RD vs BH correlation studies and 9 IQ vs BH correlation studies were identified. 7 articles in the latter group correlated metrics from all three categories concurrently.

CONCLUSIONS

Protocol optimisation should be fine-tuned to the level of the individual patient and particular clinical query. This would require a judicious choice of metrics from each of the three categories. It is suggested that, for increased utility in clinical practice, more future optimisation studies be clinical task based and involve the three categories of metrics concurrently.

Computed tomography dose index determination in dose modulation prospectively involving the third-generation iterative reconstruction and noise index

PURPOSE

Optimizing CT protocols is challenging in the presence of automatic dose modulation because the CT dose index (CTDIvol) at different patient sizes is unknown to the operator. The task is more difficult when both the image quality index and iterative reconstruction prospectively affect the dose determination. It is of interest in practice to be informed of the CTDIvol during the protocol initialization and evaluation. It was our objective to obtain a predictive relationship between CTDIvol, the image quality index, and iterative reconstruction strength at various patient sizes.

METHODS

Dose modulation data were collected on a GE Revolution 256-slice scanner utilizing a Mercury phantom and selections of the noise index (NI) from 8 to 17, the third generation iterative reconstruction (ASIR-V) from 0% to 80%, and phantom diameters from 16 to 36 cm. The fixed parameters were 120 kVp, a pitch of .984, and a collimation of 40 mm with a primary slice width of 2.5 mm. The CTDIvol per diameter was based on the average tube current over three adjacent slices (same or similar diameter) multiplied by a conversion factor between the average mA of the series and the reported CTDIvol. The relationship between CTDIvol, NI, and ASIR-V for each diameter was fitted with a 2nd order polynomial of ASIR-V multiplied by a power law of NI.

RESULTS

The ASIR-V fit parameters versus diameter followed a Lorentz function while the NI exponent versus diameter followed an exponential growth function. The CTDIvol predictions were accurate within 15% compared to phantom results on a separate GE Revolution. For clinical relevance, the phantom diameter was converted to an abdomen or chest equivalent diameter and was well matched to patient data.

CONCLUSION

The fitted relationship for CTDIvol. for given values of NI and ASIR-V blending for a range of phantom sizes was a good match to phantom and patient data. The results can be of direct help for selecting adequate parameters in CT protocol development.

Development of acceptable quality radiation dose levels for common computed tomography examinations: A focused multicenter study in United Arab Emirates

Purpose

Diagnostic Reference Level (DRL) is a practical tool for radiation dose optimization, yet it does not indicate the patient size or image quality. The Acceptable Quality Dose (AQD) introduced to address the limitations of the DRLs and it is based on image quality, radiation dose, and patient weight. The aim of this study is to establish the AQD for adult patients' undergoing Computed Tomography (CT) examinations (Head, chest, abdomen).

Methods

This study is conducted in the four main hospitals at the Ministry of Health and Prevention. Patient information and exposure parameters were extracted. All the acceptable images are scored for their quality assessments. Data is classified as seven weight groups, <50, 50-59, 60-69, 70-79, 80-89, 90-99, and ≥100 kg. The mean ± SD, median, and 75th are calculated for the CTDIvol and DLP for each weight group per examination.

Results

Out of 392, 358 CT examinations are scored with acceptable quality. The median CTDIvol values for the weight groups are obtained as 24.6, 25.4, 25.4, 25.0, 26.0, 27.0, and 29.0 mGy. Moreover, median DLP values are obtained as 576.7, 601.0, 616.5, 636.1, 654.0, 650.0, 780.0, and 622.5 mGy.cm, respectively, for head CT without Contrast Media (CM). Similar calculation for head CT with (CM), chest without CM, abdomen without CM, and chest and abdomen (with and without CM) CTs are presented.

Conclusion

Images with bad, unacceptable and higher than necessary qualities contribute to increasing patient dose and increasing the DRLs. The AQD for the selected examinations were lower than the proposed DRLs in the United Arab Emirates. The integration of image quality and patients size in the assessment of the AQD values provide effective model to compare radiation dose indices within facility and compare with others. The obtained results may be useful in terms of improving dose and the diagnostic quality in the national and international levels.

Medical Physics 3.0 and Its Relevance to Radiology

Medical Physics 3.0 is a grassroots movement within the medical physics community to re-envision a new commitment and engagement of physics in the care process. In this model, a physicist, present in either the clinic or the laboratory, is to practice physics for the direct benefit of the patient, either immediately in the clinical setting or eventually through research. As a demonstration of this shift of focus, this article offers a framework by which medical physics can better serve the rigor and quality of radiological practice. This is accomplished through (1) patient-centered metrics of imaging dose and quality that are generalizable across and relatable to varying tasks; (2) prospectively prescribing protocols and dose based on needed quality; and (3) retrospectively monitoring and targeting dose and quality across practice. The article also suggests a model for this opportunity in which the current "consultant" physicist is vested with the responsibility to fully contribute her essential expertise in the care of individual patients.

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.

Correlation of Algorithmic and Visual Assessment of Lesion Detection in Clinical Images

RATIONALE AND OBJECTIVES

Clinically-relevant quantitative measures of task-based image quality play key roles in effective optimization of medical imaging systems. Conventional phantom-based measures do not adequately reflect the real-world image quality of clinical Computed Tomography (CT) series which is most relevant for diagnostic decision-making. The assessment of detectability index which incorporates measurements of essential image quality metrics on patient CT images can overcome this limitation. Our current investigation extends and validates the technique on standard-of-care clinical cases.

MATERIALS AND METHODS

We obtained a clinical CT image dataset from an Institutional Review Board-approved prospective study on colorectal adenocarcinoma patients for detecting hepatic metastasis. For this study, both perceptual image quality and lesion detection performance of same-patient CT image series with standard and low dose acquisitions in the same breath hold and four processing algorithms applied to each acquisition were assessed and ranked by expert radiologists. The clinical CT image dataset was processed using the previously validated method to estimate a detectability index for each known lesion size in the size distribution of hepatic lesions relevant for the imaging task and for each slice of a CT series. We then combined these lesion-size-specific and slice-specific detectability indexes with the size distribution of hepatic lesions relevant for the imaging task to compute an effective detectability index for a clinical CT imaging condition of a patient. The assessed effective detectability indexes were used to rank task-based image quality of different imaging conditions on the same patient for all patients. We compared the assessments to those by expert radiologists in the prospective study in terms of rank order agreement between the rankings of algorithmic and visual assessment of lesion detection and perceptual quality.

RESULTS

Our investigation indicated that algorithmic assessment of lesion detection and perceptual quality can predict observer assessment for detecting hepatic metastasis. The algorithmic and visual assessment of lesion detection and perceptual quality are strongly correlated using both the Kendall's Tau and Spearman's Rho methods (perfect agreement has value 1): for assessment of lesion detection, 95% of the patients have rank correlation coefficients values exceeding 0.87 and 0.94, respectively, and for assessment of perceptual quality, 0.85 and 0.94, respectively.

CONCLUSION

This study used algorithmic detectability index to assess task-based image equality for detecting hepatic lesions and validated it against observer rankings on standard-of-care clinical CT cases. Our study indicates that detectability index provides a robust reflection of overall image quality for detecting hepatic lesions under clinical CT imaging conditions. This demonstrates the concept of utilizing the measure to quantitatively assess the quality of the information content that different imaging conditions can provide for the same clinical imaging task, which enables targeted optimization of clinical CT systems to minimize clinical and patient risks.

Technical Note: Validation of TG 233 phantom methodology to characterize noise and dose in patient CT data

PURPOSE

Phantoms are useful tools in diagnostic CT, but practical limitations reduce phantoms to being only a limited patient surrogate. Furthermore, a phantom with a single cross sectional area cannot be used to evaluate scanner performance in modern CT scanners that use dose reduction techniques such as automated tube current modulation (ATCM) and iterative reconstruction (IR) algorithms to adapt x-ray flux to patient size, reduce radiation dose, and achieve uniform image noise. A new multisized phantom (Mercury Phantom, MP) has been introduced, representing multiple diameters. This work aimed to ascertain if measurements from MP can predict radiation dose and image noise in clinical CT images to prospectively inform protocol design.

METHODS

The adult MP design included four different physical diameters (18.5, 23.0, 30.0, and 37.0 cm) representing a range of patient sizes. The study included 1457 examinations performed on two scanner models from two vendors, and two clinical protocols (abdominopelvic with and chest without contrast). Attenuating diameter, radiation dose, and noise magnitude (average pixel standard deviation in uniform image) was automatically estimated in patients and in the MP using a previously validated algorithm. An exponential fit of CTDI_vol and noise as a function of size was applied to patients and MP data. Lastly, the fit equations from the phantom data were used to fit the patient data. In each patient distribution fit, the normalized root mean square error (nRMSE) values were calculated in the residuals' plots as a metric to indicate how well the phantom data can predict dose and noise in clinical operations as a function of size.

RESULTS

For dose across patient size distributions, the difference between nRMSE from patient fit and MP model data prediction ranged between 0.6% and 2.0% (mean 1.2%). For noise across patient size distributions, the nRMSE difference ranged between 0.1% and 4.7% (mean 1.4%).

CONCLUSIONS

The Mercury Phantom provided a close prediction of radiation dose and image noise in clinical patient images. By assessing dose and image quality in a phantom with multiple sizes, protocol parameters can be designed and optimized per patient size in a highly constrained setup to predict clinical scanner and ATCM system performance.

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.

Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging

Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.

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

OBJECTIVE. Diagnostic reference levels were developed as guidance for radiation dose in medical imaging and, by inference, diagnostic quality. The objective of this work was to expand the concept of diagnostic reference levels to explicitly include noise of CT examinations to simultaneously target both dose and quality through corresponding reference values. MATERIALS AND METHODS. The study consisted of 2851 adult CT examinations performed with scanners from two manufacturers and two clinical protocols: abdominopelvic CT with IV contrast administration and chest CT without IV contrast administration. An institutional informatics system was used to automatically extract protocol type, patient diameter, volume CT dose index, and noise magnitude from images. The data were divided into five reference patient size ranges. Noise reference level, noise reference range, dose reference level, and dose reference range were defined for each size range. RESULTS. The data exhibited strong dependence between dose and patient size, weak dependence between noise and patient size, and different trends for different manufacturers with differing strategies for tube current modulation. The results suggest size-based reference intervals and levels for noise and dose (e.g., noise reference level and noise reference range of 11.5-12.9 HU and 11.0-14.0 HU for chest CT and 10.1-12.1 HU and 9.4-13.7 HU for abdominopelvic CT examinations) that can be targeted to improve clinical performance consistency. CONCLUSION. New reference levels and ranges, which simultaneously consider image noise and radiation dose information across wide patient populations, were defined and determined for two clinical protocols. The methods of new quantitative constraints may provide unique and useful information about the goal of managing the variability of image quality and dose in clinical CT examinations.

Medical Physics 3.0: Ensuring Quality and Safety in Medical Imaging

Medical imaging often involves radiation and thus radiation protection. Radiation protection in medicine is only one component of the broader calling of health care professionals: fostering human health. As such, radiation risk needs to be put into the context of the larger mandate of improved outcomes in health care. Medical physicists, according to the new vision of Medical Physics 3.0, make a significant contribution to this mandate as they engage proactively and meaningfully in patient care. Facing the new realities of value-based, personalized, and evidence-based practice, Medical Physics 3.0 is an initiative to make physics inform every patient's care by fostering new skills and expanding horizons for the medical physics profession. It provides a framework by which medical physicists can maintain and improve their integral roles in, and contributions to, health care, its innovation, and its precision. One way that Medical Physics 3.0 will manifest itself in medical imaging practice is by engaging physicists to ensure the precise and optimized use of radiation. Optimization takes place through knowing the defining attributes of the technology in use, the specifics of the patient's situation, and the goals of the imaging and/or intervention. The safety as well as the quality of the procedure is ascertained quantitatively and optimized prospectively, ensuring a proper balance between quality and safety to offer maximum potential benefit to the patient. The results of procedures across the health care operation are then retrospectively analyzed to ensure that each procedure has, in actuality, delivered the targeted quality and safety objectives. Characterizing quality and safety in quantitative terms, objectively optimizing them in the practice of personalized care, and analyzing the results from clinical operations all require the unique combination of precision and innovation that physicists bring to the development and practice of medicine.

Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?

OBJECTIVE

The purpose of this study is to determine whether second-order texture analysis can be used to distinguish lipid-poor adenomas from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT (CECT), and chemical-shift MRI.

MATERIALS AND METHODS

In this retrospective study, 23 adrenal nodules (15 lipid-poor adenomas and eight adrenal malignancies) in 20 patients (nine female patients and 11 male patients; mean age, 59 years [range, 15-80 years]) were assessed. All patients underwent unenhanced CT, CECT, and chemical-shift MRI. Twenty-one second-order texture features from the gray-level cooccurrence matrix and gray-level run-length matrix were calculated in 3D. The mean values for 21 texture features and four imaging features (lesion size, unenhanced CT attenuation, CECT attenuation, and signal intensity index) were compared using a t test. The diagnostic performance of texture analysis versus imaging features was also compared using AUC values. Multivariate logistic regression models to predict malignancy were constructed for texture analysis and imaging features.

RESULTS

Lesion size, unenhanced CT attenuation, and the signal intensity index showed significant differences between benign and malignant adrenal nodules. No significant difference was seen for CECT attenuation. Eighteen of 21 CECT texture features and nine of 21 unenhanced CT texture features revealed significant differences between benign and malignant adrenal nodules. CECT texture features (mean AUC value, 0.80) performed better than CECT attenuation (mean AUC value, 0.60). Multivariate logistic regression models showed that CECT texture features, chemical-shift MRI texture features, and imaging features were predictive of malignancy.

CONCLUSION

Texture analysis has a potential role in distinguishing benign from malignant adrenal nodules on CECT and may decrease the need for additional imaging studies in the workup of incidentally discovered adrenal nodules.

The Need for Practical and Accurate Measures of Value for Radiology

Radiologists play a critical role in helping the health care system achieve greater value. Unfortunately, today radiology is often judged by simple "checkbox" metrics, which neither directly reflect the value radiologists provide nor the outcomes they help drive. To change this system, first, we must attempt to better define the elusive term value and, then, quantify the value of imaging through more relevant and meaningful metrics that can be more directly correlated with outcomes. This framework can further improve radiology's value by enhancing radiologists' integration into the care team and their engagement with patients. With these improvements, we can maximize the value of imaging in the overall care of patients.

Medical imaging dose optimisation from ground up: expert opinion of an international summit

As in any medical intervention, there is either a known or an anticipated benefit to the patient from undergoing a medical imaging procedure. This benefit is generally significant, as demonstrated by the manner in which medical imaging has transformed clinical medicine. At the same time, when it comes to imaging that deploys ionising radiation, there is a potential associated risk from radiation. Radiation risk has been recognised as a key liability in the practice of medical imaging, creating a motivation for radiation dose optimisation. The level of radiation dose and risk in imaging varies but is generally low. Thus, from the epidemiological perspective, this makes the estimation of the precise level of associated risk highly uncertain. However, in spite of the low magnitude and high uncertainty of this risk, its possibility cannot easily be refuted. Therefore, given the moral obligation of healthcare providers, 'first, do no harm,' there is an ethical obligation to mitigate this risk. Precisely how to achieve this goal scientifically and practically within a coherent system has been an open question. To address this need, in 2016, the International Atomic Energy Agency (IAEA) organised a summit to clarify the role of Diagnostic Reference Levels to optimise imaging dose, summarised into an initial report (Järvinen et al 2017 Journal of Medical Imaging 4 031214). Through a consensus building exercise, the summit further concluded that the imaging optimisation goal goes beyond dose alone, and should include image quality as a means to include both the benefit and the safety of the exam. The present, second report details the deliberation of the summit on imaging optimisation.