Patient dose optimization for computed radiography using physical and observer-based measurements as image quality metrics

Journal Pre-proof Patient dose optimization for computed radiography using physical and observerbased measurements as image quality metrics Marcelo B. Freitas, Ricardo B. Pimentel, Laura F. Braga, Francisco S.A. Salido, Rodrigo F.C.A. Neves, Regina B. Medeiros PII:

S0969-806X(19)30822-9

DOI:

https://doi.org/10.1016/j.radphyschem.2020.108768

Reference:

RPC 108768

To appear in:

Radiation Physics and Chemistry

Received Date: 31 July 2019 Revised Date:

29 January 2020

Accepted Date: 7 February 2020

Please cite this article as: Freitas, M.B., Pimentel, R.B., Braga, L.F., Salido, F.S.A., Neves, R.F.C.A., Medeiros, R.B., Patient dose optimization for computed radiography using physical and observerbased measurements as image quality metrics, Radiation Physics and Chemistry (2020), doi: https:// doi.org/10.1016/j.radphyschem.2020.108768. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Author Contribution Statement

Paper: Patient dose optimization for computed radiography using physical and observer-based measurements as image quality metrics Marcelo B. Freitas: Conceptualization, Methodology, Investigation, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration Ricardo B. Pimentel: Visualization, Software, Investigation, Data Curation Laura F. Braga: Visualization, Software, Investigation, Data Curation Francisco S. A. Salido: Investigation, Resources Rodrigo F. C. A. Neves: Investigation, Resources Regina B. Medeiros: Conceptualization, Methodology, Investigation, Supervision

Patient dose optimization for computed radiography using physical and observer-based measurements as image quality metrics Marcelo B. Freitas1, Ricardo B. Pimentel2, Laura F. Braga2, Francisco S. A. Salido3, Rodrigo F. C. A. Neves3, Regina B. Medeiros4 [email protected] 1

Department of Biophysics, Paulista School of Medicine – Federal University of São Paulo, São Paulo, Brazil

2

Medical Physics Residency Program – Federal University of São Paulo, São Paulo, Brazil

3

Diagnostic Imaging Center – Hospital do Rim, São Paulo, Brazil

4

Paulista School of Medicine – Federal University of São Paulo, São Paulo, Brazil

Radiation protection of patients undergoing diagnostic x-ray examinations requires practical evaluation of doses and image quality under clinical conditions. On this subject, optimization of the dose-image quality relationship plays key role in order to achieve this goal. In this study, patient dose optimization was implemented for a computed radiography (CR) system used in general x-ray examinations, considering physical and observer-based measurements as image quality metrics. An anthropomorphic phantom was used to simulate the patient under clinical conditions of chest and abdominal x-rays. Entrance surface doses (ESDs) were measured using a solid-state dose detector positioned at phantom entrance surface during simulated x-rays with different combinations of tube potential (kV) and tube current-time product (mAs), including the kV-mAs used clinically. Agfa's CR System with CR12-X digitizer and a set of 35x43cm cassettes and imaging plates (IP) were employed to capture digital images. Contrast-to-noise ratio (CNR) determined from different regions in the acquired images was used as physical measurement of image quality. Two experienced radiologists evaluated the images qualifying them in terms of acceptable noise. The relationship between calculated CNR and ESD measured for each exposure setting in association to the acceptable images by radiologists were employed as optimization strategies: maximize CNR for a constant dose, minimize dose for a constant CNR and, finally, maximize the figure of merit (FoM) that relates CNR and dose. Prior to the dose and image quality optimization, standardized exposure index (EI) from clinically accepted images and its associated deviation index (DI) were collected for one month. Large reductions in ESD (up to 65%) with clinical image quality assurance were achieved with optimization strategies. The results indicate that the physical parameters of digital image quality assessment validated by experts observation can be an efficient way in the practice of optimization. keywords: optimization; radiation dose; image quality; computed radiography; radiology. 1. Introduction Nowadays, medical imaging systems are based on digital radiography. Among the digital imaging modalities, computed radiography (CR) has usually been the first digital imaging system to be implemented in radiology departments, gradually replacing traditional film-based projection radiographs. Despite the many advantages of CR systems (Seibert et al., 1996; Lança and Silva, 2013), its use at first can lead to an increase in radiation doses delivered to patients during medical exposures – dose creep (Seibert, 2004; Uffmann and Schaefer-Prokop, 2009), resulting among other reasons due to the wide exposure latitude (dynamic range) of the photostimulable or storage phosphors used as detectors such as barium fluorohalide which is coated on the plate referred to as an imaging plate (IP). In this context, the application of the optimization principle in radiological practice is required to balance the clinically acceptable image quality with patient dose, which should be as low as reasonable achievable or practicable 1

(Andria et al., 2017, 2016; Baptista et al., 2014; Samei et al., 2011; Sun et al., 2012). Selection of the appropriate exposure technique factors such as tube potential (kV) and tube current– exposure time product (mAs) used in a digital radiography examination is an important aspect of the optimization for every clinical imaging task in conventional radiology since it affects both image quality and patient dose. The literature review on the use these optimization approaches in CR found conflicting results (Seeram et al., 2013), indicating that further studies are needed. Although patient dose can be measured in terms of well-established quantities such as the air kerma incident on the patient or the entrance surface dose (ESD), which is the dose to the skin and includes backscattered radiation, the image quality assessment of medical images is still a challenge. The combination of physical measures and observer performance methods can be an efficient strategy to evaluate the imaging performance in CR systems (Alves et al., 2016; Mansson, 2000; Moore et al., 2013; Van Peteghem et al., 2016; Verdun et al., 2015). Contrastto-noise ratio (CNR) is a traditional objective metric and is related to the contrast or signal difference between larger objects and the image background. On the other hand, the image visual assessment by human observers such as radiologists is a subjective way to evaluate the image quality and can be described, for example, by observer´s ability to visualize details against the background noise within the image at first and then interpreting them in a complex cognitive process that results in diagnosis. Optimization approaches can also include analysis of figures of merit (FoM) which have the main definition given as the contrast-to-noise ratio (CNR) squared per unit dose (Borg et al., 2012; Samei et al., 2005). Considering these aspects, this study investigates different strategies for patient dose optimization in chest and abdomen computed radiography based on maximizing CNR for a constant dose (diagnostic reference level), minimizing dose for a constant CNR (acceptable images for radiologists) and, finally, maximizing figure of merit (FoM) that relates CNR and dose. This study intends to contribute for reduction of doses delivery to patients in computed radiography making use of practical optimization methods that can be accomplished considering the clinical practice of diagnostic radiology departments.

2. Material and methods All irradiations were performed in a general purpose x-ray room of a larger specialized hospital. Chest PA (posterior-anterior projection) and abdomen AP (anterior-posterior projection) x-ray examinations were simulated using an anthropomorphic phantom (Rando Man Phantom) as patient (Figure 1). The phantom is representative of a standard adult of 1.73 m height and 73.5 kg weight and it is constituted of different materials, which simulate the various tissue densities of the human body.

(a)

(b)

2

Figure 1. Experimental setup to simulate (a) chest PA and (b) abdomen AP x-ray examinations. It is possible to observe the solid state sensor positioned on the phantom surface to measure entrance surface dose (ESD). The X-ray equipment used in this study was a Philips Compact Plus 600 for general radiography with a high frequency generator and a RTM 90 HS x-ray tube. The total filtration of this system is 2.5 mm Al equivalent. An Agfa's CR System with CR12-X digitizer and a set of 35x43 cm cassettes and imaging plates (IP) with a 10 pixels/mm spatial resolution and a grey scale of 16 bits/pixel resolution were employed to capture digital images. The same IP was used throughout all irradiation sequences that simulated each x-ray examination to achieve maximum reproducibility. Both x-ray equipment and CR system demonstrated compliance with performance standards when subjected to comprehensive quality control tests (BRASIL, 1998; Vaño Carruana and Alonso Díaz, 2002). For each x-ray examination, different settings of kV and mAs, above and below those values used clinically, were selected to produce different exposures on IP and therefore different image qualities. A total of 32 and 29 images were collected to chest and abdomen x-ray examinations, respectively. IP exposure was monitored on the CR reader by deviation index (DI) which is defined as relative measurement of the quantity of radiation that was incident on regions of the detector for each exposure made – clinical exposure index (EI) – in relation to target value set by manufacturer for same x-ray examination – target exposure index (EIT) (AAPM, 2009): DI

10 ∗ log

EI EI

Thus, a DI equal to zero corresponds to a IP exposure (EI) equal to the target value (EIT) set by the manufacturer. Typical x-ray source-detector distances (SDD) of 1.8 m and 1.0 m to chest and abdomen, respectively, were used on the x-ray examination simulations. Air kerma quantity was measured in each irradiation using solid state sensor (AGMS-D – Radcal) with digitizer module (Accu-Gold – Radcal) and ESDs (doses) were calculated from measured air kerma multiplied by backscattering factor in this energy range (~1.4). No clinical post-processing was applied to the acquired images. All pixel values from images acquired on the CR system were linearized to prior calculating CNR using signal transfer properties (STP) relationship. The STP 3

was found by plotting the pixel value measured at the image center against the air kerma at the x-ray detector (image plate – IP) (Figure 2).

35,000

Mean Pixel Value (MPV)

30,000

MPV=2734*kerma (0.51)+186 R2=0.999

25,000

20,000

15,000

10,000

5,000

0 0

20

40

60

80

100

120

140

kerma (µGy)

Figure 2. Relationship between pixel value and air kerma (signal transfer property – STP) of CR system used to linearize acquired images. CNRs were calculated from three (lung, heart and spine) and two (sacrum and tissue) regions of interest (ROIs) for each chest and abdomen x-ray image, respectively, as shown in Figure 3. CNR as physical image quality measure was computed following: CNR

S

σ

S

2

σ

where S1 and S2, are the mean pixel values from ROI1 and ROI2, respectively, and σ1 and σ2 are associated standard deviations. These parameters were measured using ImageJ public domain software. CNR were calculated from lung-heart, lung-spine and heart-spine regions for each chest x-ray image and from sacrum-tissue region for abdomen x-ray image.

(a)

(b)

Figure 3. Regions of interest (ROIs) used to calculate contrast-to-noise ratio (CNR) on the (a) chest and (b) abdomen x-ray images. 4

Also as physical image quality metric, figures of merit (FoM) using calculated CNRs and measured ESD were computed following: FoM

CNR ESD

All images were assessed by two experienced radiologists, with 18 and 19 years of experience in x-ray imaging, considering the acceptable noise level. The noise level was acceptable when the radiologists considered that the images from phantom had a grainy (or sandlike) appearance similar or better than the clinical images from actual patients. All image files were randomized and made anonymous so that the radiologists were unaware of the irradiation protocol (kV and mAs). All images were analyzed by radiologists on diagnostic display monitors in a radiology reading room under the same conditions as clinical images were evaluated in the daily clinical routine. Although the process of interpreting a medical image is complex, in this study we chose a simple observer-based image quality assessment metric that could be easily accomplished in clinical practice. To this end, the observer was asked to indicate whether the image displayed on a diagnostic monitor was acceptable or unacceptable in terms of noise (quantum mottle). This approach supported observer-based image quality assessment. Three strategies were undertaken to achieve optimization principle (Figure 4): 1) CNR was maximized up to the dose achieves ESD-based diagnostic reference level required by Brazilian regulation (BRASIL, 1998) in each x-ray examination (0.4 mGy and 10 mGy for chest and abdomen, respectively), 2) ESD (radiation dose) was minimized considering only images accepted by radiologists, i.e., the lowest radiation dose obtained that still produces an acceptable image quality based on human observer assessment and, finally, 3) FoM was maximized to evaluate the efficiency of the CR system in producing a high CNR image with a low ESD.

Figure 4. Optimization strategies undertaken by: 1) maximizing CNR; 2) minimizing dose; 3) maximizing figure of merit (FoM). Each of these optimization strategies gives priority to one aspect on the dose-image relation and their particular use depends on clinical needs of the individual. As illustrated in the Figure 4, the strategy 1 focuses on the maximizing image quality, raising the dose up to diagnostic reference 5

level adopted by national regulation or, locally, by hospital, and in this case a specific diagnostic task is the main concern. On the other hand, when the diagnostic task is general or the patient is more sensitive to radiation, such as children or pregnant women, the dose can be reduced until the image produced still has an acceptable noise level for the diagnostic task (strategy 2). The strategy 3 is preferred when the balance between dose and image quality must be achieved globally and maximizing FoM meets this goal. Before starting dose and image quality optimization study, exposure index (EI) and its associated deviation index (DI) were collected for a period of the one month from chest and abdomen radiographs performed in the x-ray room where this study was conducted. Only EI and DI from images accepted clinically were considered in this survey. Percentage distribution of DI values was analyzed considering the ranges suggested by Report 116 AAPM (AAPM, 2009). Target exposure index (EIT) has been set by the CR system manufacturer.

3. Results and discussion A total of 480 PA chest and 206 AP abdomen clinical x-ray images had their DI values collected. These values along with practiced EI values indicate that an EIT of 300 is set for PA chest and 450 for AP abdomen x-ray examinations. Distribution of these DI values (minimum, 1st quartile, median, mean, 3rd quartile and maximum) for each x-ray examination is shown in Figure 5. Percentage distribution of DI values in each range suggested by Report 116 AAPM can be observed in the Table 1.

15

Deviation Index (DI)

10

5

0

−5

−10 Chest PA

Abdomen AP X-ray

Figure 5. Distribution of deviation index (DI) values from chest and abdomen clinical images performed in the x-ray room where the current study was conducted. Table 1. Percentage (%) of x-ray examinations in each range of deviation index (DI) defined according to the Report 116 from AAPM (AAPM, 2009). DI >3 1 to 3 0.5 to 1

Percentage (%) Chest PA (N=480)

Abdomen AP (N=206)

2.5 15.2 5.8

28.5 31.4 13.0

6

-0.5 to 0.5 -0.5 to -1 -1 to -3 < -3

21.5 11.3 33.3 10.4

9.2 6.8 9.7 1.4

Large variations of DI values point out that training programs for radiographers should be conducted in the department, considering that X-ray equipment and imaging plates (IPs) have demonstrated compliance in quality control testing. Nevertheless, defining expected distributions for DI data that represent best practice requires careful study of the effect of values of interest (VOI) identification, patient positioning, and other factors on the reported DI and its distribution (Dave et al., 2018). Although there is no direct relationship between patient dose and detector exposure, it is important to take account the EI practiced on the x-ray examinations before starting a dose and image quality optimization study. Considering that all images have been accepted for medical diagnosis, the highest percentage of DI for chest x-ray examinations in the range -1 to -3 (33%) suggests that EIT set by manufacturer may not be adequate. Furthermore, exposure factors (kV and mAs) practiced by radiographers in this DI range suggest that they would already be optimized. In this current optimization study, technical parameters used in this DI range were considered as the clinical protocol used in the PA chest xray routine: 88 kV, 11 mAs and SSD of 1.8 m. On the other hand, percentage distribution of DI values practiced in abdomen x-ray examinations shows that high DI values are most frequent (31%), reinforcing that dose and image quality optimization should be implemented in these radiographs or, in the last analysis that EIT was not properly set by manufacturer. Although the setting that provides a DI close to zero was not the most often practiced in clinical routine, it was considered as clinical protocol for AP abdomen x-ray examinations in the optimization study: 75 kV, 32 mAs and SSD of 1.0 m. Tube potential (kV) and tube current-time product (mAs) settings used in PA chest and AP abdomen x-ray simulations using anthropomorphic phantom can be seen in Tables 2, respectively. Entrance surface dose (ESD) measured for each combination kV-mAs along with deviation index (DI) practiced in each image were also gathered. Contrast-to-noise ratio (CNR) determined from different regions of interest (ROIs) and mean value of figure of merit (FoM) calculated for each kV set in both x-ray examinations can also be seen in Table 2.

Table 2. Settings of tube potential (kV) and tube current-time product (mAs) along with entrance surface dose (ESD) measured and deviation index (DI) obtained in each chest and abdomen x-ray images simulated using anthropomorphic phantom. Contrast-to-noise ratio (CNR) determined for each image analyzed considering different regions of interest (ROIs) is also shown. Mean value of figure of merit (FoM) was calculated for each kV used. Chest kVp

80

mAs

ESD (µGy)

DI

6.3 9 12.5 18 25

237 332 471 655 922

-6,3 -4,6 -3,0 -2,0 -0,5

CNR

FoM

Lung-Heart

Lung-Spine

Heart-Spine

19.0 21.2 21.7 24.2 25.9

25.5 29.7 31.5 32.7 37.5

2.66 2.80 2.89 2.75 2.77

Lung-Heart

Lung-Spine

Heart-Spine

0.9±0.4

1.8±0.7

0.015±0.009

7

84

88

92

96

32 40 6.3 9 12.5 18 25 32 40 6.3 9 11 20 25 32 40 7.1 10 14 18 25 32 5.6 6.3 9 11 14

1141 1417 258 361 513 721 1016 1258 1555 286 399 514 885 1112 1374 1702 337 490 684 847 1192 1479 294 359 454 584 724

0,4 1,7 -5,4 -3,9 -2,4 -1,3 0,4 1,3 2,1 -4,9 -3,4 -2,2 0,0 1,1 2,0 3,1 -3,8 -1,9 -0,6 0,3 1,8 2,7 -3,9 -2,9 -1,7 -0,9 0,1

23.7 24.3 19.4 20.2 22.3 23.4 25.9 24.4 23.6 21.1 25.7 24.6 27.1 26.0 26.5 26.7 18.1 20.6 20.3 19.7 17.5 17.9 22.9 23.3 25.2 26.2 22.0

34.2 34.7 25.5 26.6 30.4 32.4 36.7 33.4 33.9 28.4 35.2 33.7 39.2 37.0 39.1 38.5 20.9 23.8 23.6 23.2 20.7 21.2 31.7 32.6 37.0 39.1 32.2

3.00 2.87 2.55 2.77 2.68 2.80 2.69 2.83 2.81 2.65 2.48 2.66 2.69 2.75 2.71 2.77 2.26 2.41 2.50 2.50 2.73 2.55 2.58 2.77 2.66 2.66 3.08

0.8±0.4

1.5±0.6

0.013±0.008

1.0±0.5

1.9±0.9

0.011±0.007

0.6±0.3

0.8±0.4

0.009±0.004

1.3±0.4

2.7±0.8

0.017±0.005

Abdomen kVp

71

75

79

83

87

mAs

ESD (mGy)

DI

22 32 40 56 18 25 32 40 50 63 14 20 28 40 50 63 9 12.5 18 25 32 40 12.5 16 20 22 28 36

2.9 4.0 4.9 7.0 2.5 3.5 4.3 5.3 6.8 8.5 2.2 3.0 4.3 5.8 7.5 9.3 1.5 2.2 3.0 4.2 5.2 6.4 2.4 3.0 3.7 4.1 5.2 6.3

-4.1 -1.3 -0.4 1.2 -4.3 -2.3 -0.7 0.0 1.4 2.3 -3.7 -2.2 -0.7 1.8 2.2 3.0 -5.3 -3.2 -1.4 0.4 2.7 3.7 -2.2 -1.0 0.4 1.1 2.3 3.3

CNR

FoM

Tissue-Sacrum

Tissue-Sacrum

7.9 9.3 9.7 9.7 10.6 10.6 11.3 11.5 11.6 12.0 10.0 10.8 11.0 11.3 11.7 11.8 7.9 8.2 10.3 11.0 11.0 11.3 8.0 8.2 8.7 8.9 8.8 8.8

18±4

28±10

28±12

30±7

19±5

As expected, ESD increases linearly with increasing mAs in both x-rays examinations. Overall, CNR determined from different ROIs in the phantom images increases at all kV similarly as mAs increases, but its values have an abrupt reduction at 92kV and 87kV for chest and abdomen x-ray images, respectively. This abrupt change of the image quality can be related to variation in the IP phosphor sensitivity with x-ray spectra (Martin, 2007). As FoM is independent of the mAs (number of photons), and relates solely to differences in the radiation quality, its mean value was calculated for each kV (Table 2). The high uncertainties (standard deviation) found demonstrate that FoM calculated using CNR squared per ESD is still related to mAs, i.e., CNR curves for each kV plotted against mAs have a power coefficient lower than the 8

ideal value of 0.5 (quantum noise - Poisson statistics). The presence of radiation scatter in the phantom images can cause this deviation from expected behavior. Radiologists considered all acquired images were acceptable with regard to noise level and then the three optimization strategies were performed considering this. Settings of kV-mAs combination along with practiced ESD according to optimization strategies and their impact on dose variation can be observed in Table 3.

Table 3. Settings of tube potential (kVp), tube current-time product (mAs), entrance surface dose (ESD) and its variation according to the optimization strategy. X-ray Chest PA

Abdomen AP

Optimization Strategy Practiced Clinically Maximized Image Quality Minimized Dose Maximized Figure-of-Merit Practiced Clinically Maximized Image Quality Minimized Dose Maximized Figure-of-Merit

kVp 88 88 80 96 75 75 83 83

mAs 11 9 6.3 5.6 32 63 9 9

ESD (mGy) 0.51 0.40 0.24 0.29 4.3 8.5 1.5 1.5

Dose Variation -22% -53% -43% +98% -65% -65%

Dose reduction, ensuring image had still quality for clinical diagnosis, was achieved by optimization strategies using minimized dose and maximized FoM. This reduction reached 53% and 65% for chest and abdomen x-ray examinations, respectively. Even with the optimization strategy using maximized image quality, there was an ESD reduction of 22%, indicating that both routine clinical protocol (kV and mAs) and EIT set by manufacturer for chest x-ray examinations were not adequate. For abdomen x-ray images, minimized dose and maximized FoM optimization strategies showed the same results on dose reduction, pointing out that the CR system is efficient in producing diagnostic quality images with low radiation dose.

4. Conclusions Optimization strategies were successful in reducing doses with clinical image quality assurance in chest and abdomen x-ray examinations performed using computed radiography. The choice of optimization strategy depends on diagnostic task, which can enhance image quality or dose. Image quality and dose optimization was achieved simultaneously using figures of merit which relate CNR and ESD, although assuming squared CNR is independent of mAs does not appear to be a valid behavior under clinical scatter conditions. The best kV-mAs combination was not evident in the optimization strategy using figure of merit, although the lowest mAs values in a given kV have been selected. Exposure index tracking of digital detectors, as imaging plate, and its deviation from target value were the starting point to the dose and image quality optimization. In addition, this dose optimization study provided information about exposure factors and ESD which allow to revise the target exposure index (EIT) set by manufacturer. Despite all images had been considered with acceptable noise by radiologists, observer-based performance methods are still a relevant point when optimization strategy based on image

9

quality is required. On the other hand, physical measurements of image quality demonstrated to be key point to achieve patient dose optimization in computed radiography. Overall, the results of this study reinforce the importance of performing dose optimization studies in digital imaging systems considering physical measurements and expert validation as image quality assessment metrics.

Acknowledgement The authors thank the Santa Marcelina Hospital for having provided the anthropomorphic phantom and the Ministries of Education and Health for resident scholarships.

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https://doi.org/10.1088/0031-9155/56/16/002 Seeram, E., Davidson, R., Bushong, S., Swan, H., 2013. Radiation dose optimization research: Exposure technique approaches in CR imaging - A literature review. Radiography. https://doi.org/10.1016/j.radi.2013.07.005 Seibert, J.A., 2004. Tradeoffs between image quality and dose, in: Pediatric Radiology. https://doi.org/10.1007/s00247-004-1268-7 Sun, Z., Lin, C., Tyan, Y.S., Ng, K.H., 2012. Optimization of chest radiographic imaging parameters: A comparison of image quality and entrance skin dose for digital chest radiography systems. Clin. Imaging. https://doi.org/10.1016/j.clinimag.2011.09.006 Uffmann, M., Schaefer-Prokop, C., 2009. Digital radiography: The balance between image quality and required radiation dose. Eur. J. Radiol. https://doi.org/10.1016/j.ejrad.2009.05.060 Van Peteghem, N., Bosmans, H., Marshall, N.W., 2016. NPWE model observer as a validated alternative for contrast detail analysis of digital detectors in general radiography. Phys. Med. Biol. https://doi.org/10.1088/0031-9155/61/21/N575 Vaño Carruana, E., Alonso Díaz, M., 2002. Protocolo español de control de calidad en Radiodiagnóstico. Revisión 1, Sefm-Sepr. https://doi.org/10.1007/s13398-014-0173-7.2 Verdun, F.R., Racine, D., Ott, J.G., Tapiovaara, M.J., Toroi, P., Bochud, F.O., Veldkamp, W.J.H., Schegerer, A., Bouwman, R.W., Hernandez-Giron, I., Marshall, N.W., Edyvean, S., 2015. Image quality in CT: From physical measurements to model observers. Phys. Medica. https://doi.org/10.1016/j.ejmp.2015.08.007

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Patient dose optimization for computed radiography using physical and observer-based measurements as image quality metrics Highlights: •

Optimization is required to balance clinically acceptable image quality-patient dose

•

Physical measurements and expert validation as image quality assessment metrics

•

Optimization strategies were useful in reducing dose with image quality assurance

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Image quality-dose optimization were achieved simultaneously using figure of merit

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Optimization studies allow to revise the target exposure index set by manufacturer

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: