What is the preferred strength setting of the sinogram-affirmed iterative reconstruction algorithm in abdominal CT imaging?

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans

In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.

Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction

Background

We assessed and compared the image quality of normal and pathologic structures as well as the image noise in chest computed tomography images using "adaptive statistical iterative reconstruction-V" (ASiR-V) or deep learning reconstruction "TrueFidelity".

Methods

Forty consecutive patients with suspected lung disease were evaluated. The 1.25-mm axial images and 2.0-mm coronal multiplanar images were reconstructed under the following three conditions: (i) ASiR-V, lung kernel with 60% of ASiR-V; (ii) TF-M, standard kernel, image filter (Lung) with TrueFidelity at medium strength; and (iii) TF-H, standard kernel, image filter (Lung) with TrueFidelity at high strength. Two radiologists (readers) independently evaluated the image quality of anatomic structures using a scale ranging from 1 (best) to 5 (worst). In addition, readers ranked their image preference. Objective image noise was measured using a circular region of interest in the lung parenchyma. Subjective image quality scores, total scores for normal and abnormal structures, and lesion detection were compared using Wilcoxon's signed-rank test. Objective image quality was compared using Student's paired t-test and Wilcoxon's signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of P < 0.016.

Results

Both readers rated TF-M and TF-H images significantly better than ASiR-V images in terms of visualization of the centrilobular region in axial images. The preference score of TF-M and TF-H images for reader 1 were better than that of ASiR-V images, and the preference score of TF-H images for reader 2 were significantly better than that of ASiR-V and TF-M images. TF-M images showed significantly lower objective image noise than ASiR-V or TF-H images.

Conclusion

TrueFidelity showed better image quality, especially in the centrilobular region, than ASiR-V in subjective and objective evaluations. In addition, the image texture preference for TrueFidelity was better than that for ASiR-V.

Image quality improvement in low-dose chest CT with deep learning image reconstruction

OBJECTIVES

To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.

METHODS

This retrospective study included 86 patients who underwent low-dose CT for lung cancer screening. Images were reconstructed with ASiR-V40% and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures.

RESULTS

The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR-V40%, the DLIR-L, DLIR-M, and DLIR-H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR-V40%, DLIR-L, and DLIR-M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR-H had slightly lower score.

CONCLUSIONS

Compared with ASiR-V40%, DLIR significantly reduces image noise in low-dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.

A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

Purpose of Review

Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical perspective.

Recent Findings

DLR has been shown to allow for noise magnitude reductions relative to filtered back-projection without suffering from “plastic” or “blotchy” noise texture that was found objectionable with most iterative and model-based solutions. Clinically, early reader studies have reported increases in subjective quality scores and studies have successfully implemented DLR-enabled dose reductions.

Summary

The future of CT image reconstruction is bright; deep learning methods have only started to tackle problems in this space via addressing noise reduction. Artifact mitigation and spectral applications likely be future candidates for DLR applications.

Finding the optimal tube current and iterative reconstruction strength in liver imaging; two needles in one haystack

Objectives

The aim of the study was to find the lowest possible tube current and the optimal iterative reconstruction (IR) strength in abdominal imaging.

Material and methods

Reconstruction software was used to insert noise, simulating the use of a lower tube current. A semi-anthropomorphic abdominal phantom (Quality Assurance in Radiology and Medicine, QSA-543, Moehrendorf, Germany) was used to validate the performance of the ReconCT software (S1 Appendix). Thirty abdominal CT scans performed with a standard protocol (120 kVref, 150 mAsref) scanned at 90 kV, with dedicated contrast media (CM) injection software were selected. There were no other in- or exclusion criteria. The software was used to insert noise as if the scans were performed with 90, 80, 70 and 60% of the full dose. Consequently, the different scans were reconstructed with filtered back projection (FBP) and IR strength 2, 3 and 4. Both objective (e.g. Hounsfield units [HU], signal to noise ratio [SNR] and contrast to noise ratio [CNR]) and subjective image quality were evaluated. In addition, lesion detection was graded by two radiologists in consensus in another 30 scans (identical scan protocol) with various liver lesions, reconstructed with IR 3, 4 and 5.

Results

A tube current of 60% still led to diagnostic objective image quality (e.g. SNR and CNR) when IR strength 3 or 4 were used. IR strength 4 was preferred for lesion detection. The subjective image quality was rated highest for the scans performed at 90% with IR 4.

Conclusion

A tube current reduction of 10–40% is possible in case IR 4 is used, leading to the highest image quality (10%) or still diagnostic image quality (40%), shown by a pairwise comparison in the same patients.

Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography

OBJECTIVES

Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.

METHODS

This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20-50%, 51-70%, 71-90%, 91-99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed.

RESULTS

Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland-Altman analysis revealed a mean difference of - 0.8 mm3 (± 2.5 mm3) and limits of agreement between - 5.8 and + 4.1 mm3.

CONCLUSION

DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume.

KEY POINTS

•Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography

OBJECTIVE

To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V).

METHODS

We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score≥9 was acceptable.

RESULTS

CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score≥9 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively.

CONCLUSIONS

DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V.

Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction

OBJECTIVE. Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying characterization results of a deep learning approach to decisions about clinical CT protocols. MATERIALS AND METHODS. A proprietary deep learning image reconstruction (DLIR) method was characterized against an existing advanced adaptive statistical iterative reconstruction method (ASIR-V) and FBP from the same vendor. The metrics used were contrast-to-noise ratio, spatial resolution as a function of contrast level, noise texture (i.e., noise power spectra [NPS]), noise scaling as a function of slice thickness, and CT number consistency. The American College of Radiology accreditation phantom and a uniform water phantom were used at a range of doses and slice thicknesses for both axial and helical acquisition modes. RESULTS. ASIR-V and DLIR were associated with improved contrast-to-noise ratio over FBP for all doses and slice thicknesses. No dose or contrast dependencies of spatial resolution were observed for ASIR-V or DLIR. NPS results showed DLIR maintained an FBP-like noise texture whereas ASIR-V shifted the NPS to lower frequencies. Noise changed with dose and slice thickness in the same manner for ASIR-V and FBP. DLIR slice thickness noise scaling differed from FBP, exhibiting less noise penalty with decreasing slice thickness. No clinically significant changes were observed in CT numbers for any measurement condition. CONCLUSION. In a phantom model, DLIR does not suffer from the concerns over reduction in spatial resolution and introduction of poor noise texture associated with previous methods.

Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm

OBJECTIVE

To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose.

METHODS

The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared.

RESULTS

For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P < 0.05). CNR was not statistically different between AV-70, DL-M, and DL-H (all P > 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%).

CONCLUSION

Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.

[Volume Measurements of Post-transplanted Liver of Pediatric Recipients Using Workstations and Deep Learning]

PURPOSE

The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant recipients on digital imaging and communications in medicine (DICOM) -format images using U-Net and three-dimensional (3-D) workstations (3DWS) .

METHOD

For segmentation accuracy assessments, Dice coefficients were calculated for the graft liver and spleen. After verifying that the created DICOM-format images could be imported using the existing 3DWS, accuracy rates between the ground truth and segmentation images were calculated via mask processing.

RESULT

As per the verification results, Dice coefficients for the test data were as follows: graft liver, 0.758 and spleen, 0.577. All created DICOM-format images were importable using the 3DWS, with accuracy rates of 87.10±4.70% and 80.27±11.29% for the graft liver and spleen, respectively.

CONCLUSION

The U-Net could be used for graft liver and spleen segmentations, and volume measurement using 3DWS was simplified by this method.

Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)

PURPOSE

To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).

METHODS

Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.

RESULTS

There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.

CONCLUSION

On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.

Increasing iterative reconstruction strength at low tube voltage in coronary CT angiography protocols using 3D-printed and Catphan® 500 phantoms

Purpose

The purpose of this study was to investigate the effect of increasing iterative reconstruction (IR) algorithm strength at different tube voltages in coronary computed tomography angiography (CCTA) protocols using a three‐dimensional (3D)‐printed and Catphan^® 500 phantoms.

Methods

A 3D‐printed cardiac insert and Catphan 500 phantoms were scanned using CCTA protocols at 120 and 100 kVp tube voltages. All CT acquisitions were reconstructed using filtered back projection (FBP) and Adaptive Statistical Iterative Reconstruction (ASIR) algorithm at 40% and 60% strengths. Image quality characteristics such as image noise, signal–noise ratio (SNR), contrast–noise ratio (CNR), high spatial resolution, and low contrast resolution were analyzed.

Results

There was no significant difference (P > 0.05) between 120 and 100 kVp measures for image noise for FBP vs ASIR 60% (16.6 ± 3.8 vs 16.7 ± 4.8), SNR of ASIR 40% vs ASIR 60% (27.3 ± 5.4 vs 26.4 ± 4.8), and CNR of FBP vs ASIR 40% (31.3 ± 3.9 vs 30.1 ± 4.3), respectively. Based on the Modulation Transfer Function (MTF) analysis, there was a minimal change of image quality for each tube voltage but increases when higher strengths of ASIR were used. The best measure of low contrast detectability was observed at ASIR 60% at 120 kVp.

Conclusions

Changing the IR strength has yielded different image quality noise characteristics. In this study, the use of 100 kVp and ASIR 60% yielded comparable image quality noise characteristics to the standard CCTA protocols using 120 kVp of ASIR 40%. A combination of 3D‐printed and Catphan^® 500 phantoms could be used to perform CT dose optimization protocols.

CARE Dose 4D combined with sinogram-affirmed iterative reconstruction improved the image quality and reduced the radiation dose in low dose CT of the small intestine

OBJECTIVE

Multislice computed tomography (MSCT) has been used for diagnosis of small intestinal diseases. However, the radiation dose is a big problem. This study was to investigate whether CARE Dose 4D combined with sinogram-affirmed iterative reconstruction (SAFIRE) can provide better image quality at a lower dose for imaging small intestinal diseases compared to MSCT.

METHODS

The noise reduction ability of SAFIRE was assessed by scanning the plain water mold using SOMATOM Definition Flash double-source spiral CT. CT images at each stage of radiography for 239 patients were obtained. The patients were divided into groups A and B were based on different tube voltage and current or the image recombination methods. The images were restructured using with filtered back projection (FBP) and SAFIRE (S1-S5). The contrast noise ratio (CNR), CT Dose index (CTDI), subjective scoring, and objective scoring were compared to obtain the best image and reformation parameters at different stages of CT.

RESULTS

Twenty-six restructuring patterns of tube voltage and current were obtained by FBP and SAFIRE. The average radiation dose using CARE Dose 4D combined with SAFIRE (S4-S5) reduced approximately 74.85% compared to conditions where the tube voltage of 100 kV and tube current of 131 mAs for patients with MSCT small intestinal CT enterography at plain CT scan, arterial stage, small intestine, and portal venous phase. The objective and subjective scoring were all significantly different among groups A and B at each stage.

CONCLUSIONS

Combination of CARE Dose 4D and SAFIRE is shown to decrease the radiation dose while maintaining image quality.

Ultra-low dose CT colonography with automatic tube current modulation and sinogram-affirmed iterative reconstruction: Effects on radiation exposure and image quality

Objective

To assess the radiation dose and image quality of ultra‐low dose (ULD)‐CT colonography (CTC) obtained with the combined use of automatic tube current (mAs) modulation with a quality reference mAs of 25 and sinogram‐affirmed iterative reconstruction (SAFIRE), compared to low‐dose (LD) CTC acquired with a quality reference mAs of 55 and reconstructed with filtered back projection (FBP).

Methods

Eighty‐two patients underwent ULD‐CTC acquisition in prone position and LD‐CTC acquisition in supine position. Both ULD‐CTC and LD‐CTC protocols were compared in terms of radiation dose [weighted volume computed tomography dose index (CTDI_vol) and effective dose], image noise, image quality, and polyp detection.

Results

The mean effective dose of ULD‐CTC was significantly lower than that of LD‐CTC (0.98 and 2.69 mSv respectively, P < 0.0001) with an overall dose reduction of 63.2%. Image noise was comparable between ULD‐CTC and LD‐CTC (28.6 and 29.8 respectively, P = 0.09). There was no relevant difference when comparing image quality scores and polyp detection for both 2D and 3D images.

Conclusion

ULD‐CTC allows to significantly reduce the radiation dose without meaningful image quality degradation compared to LD‐CTC.

Application of 80-kVp scan and raw data-based iterative reconstruction for reduced iodine load abdominal-pelvic CT in patients at risk of contrast-induced nephropathy referred for oncological assessment: effects on radiation dose, image quality and renal function

OBJECTIVE

To evaluate the image quality, radiation dose, and renal safety of contrast medium (CM)-reduced abdominal-pelvic CT combining 80-kVp and sinogram-affirmed iterative reconstruction (SAFIRE) in patients with renal dysfunction for oncological assessment.

METHODS

We included 45 patients with renal dysfunction (estimated glomerular filtration rate  <45 ml per min per 1.73 m²) who underwent reduced-CM abdominal-pelvic CT (360 mgI kg^-1, 80-kVp, SAFIRE) for oncological assessment. Another 45 patients without renal dysfunction (estimated glomerular filtration rate >60 ml per lmin per 1.73 m²) who underwent standard oncological abdominal-pelvic CT (600 mgI kg^-1, 120-kVp, filtered-back projection) were included as controls. CT attenuation, image noise, and contrast-to-noise ratio (CNR) were compared. Two observers performed subjective image analysis on a 4-point scale. Size-specific dose estimate and renal function 1-3 months after CT were measured.

RESULTS

The size-specific dose estimate and iodine load of 80-kVp protocol were 32 and 41%,, respectively, lower than of 120-kVp protocol (p < 0.01). CT attenuation and contrast-to-noise ratio of parenchymal organs and vessels in 80-kVp images were significantly better than those of 120-kVp images (p < 0.05). There were no significant differences in quantitative or qualitative image noise or subjective overall quality (p > 0.05). No significant kidney injury associated with CM administration was observed.

CONCLUSION

80-kVp abdominal-pelvic CT with SAFIRE yields diagnostic image quality in oncology patients with renal dysfunction under substantially reduced iodine and radiation dose without renal safety concerns. Advances in knowledge: Using 80-kVp and SAFIRE allows for 40% iodine load and 32% radiation dose reduction for abdominal-pelvic CT without compromising image quality and renal function in oncology patients at risk of contrast-induced nephropathy.

The Acceptability of Iterative Reconstruction Algorithms in Head CT: An Assessment of Sinogram Affirmed Iterative Reconstruction (SAFIRE) vs. Filtered Back Projection (FBP) Using Phantoms

BACKGROUND

Computed tomography (CT) is the primary imaging investigation for many neurologic conditions with a proportion of patients incurring cumulative doses. Iterative reconstruction (IR) allows dose optimization, but head CT presents unique image quality complexities and may lead to strong reader preferences.

OBJECTIVES

This study evaluates the relationships between image quality metrics, image texture, and applied radiation dose within the context of IR head CT protocol optimization in the simulated patient setting. A secondary objective was to determine the influence of optimized protocols on diagnostic confidence using a custom phantom.

METHODS AND SETTING

A three-phase phantom study was performed to characterize reconstruction methods at the local reference standard and a range of exposures. CT numbers and pixel noise were quantified supplemented by noise uniformity, noise power spectrum, contrast-to-noise ratio (CNR), high- and low-contrast resolution. Reviewers scored optimized protocol images based on established reporting criteria.

RESULTS

Increasing strengths of IR resulted in lower pixel noise, lower noise variance, and increased CNR. At the reference standard, the image noise was reduced by 1.5 standard deviation and CNR increased by 2.0. Image quality was maintained at ≤24% relative dose reduction. With the exception of image sharpness, there were no significant differences between grading for IR and filtered back projection reconstructions.

CONCLUSIONS

IR has the potential to influence pixel noise, CNR, and noise variance (image texture); however, systematically optimized IR protocols can maintain the image quality of filtered back projection. This work has guided local application and acceptance of lower dose head CT protocols.

CLINICAL AUDIT OF IMAGE QUALITY IN RADIOLOGY USING VISUAL GRADING CHARACTERISTICS ANALYSIS

The aim of this work was to assess whether an audit of clinical image quality could be efficiently implemented within a limited time frame using visual grading characteristics (VGC) analysis. Lumbar spine radiography, bedside chest radiography and abdominal CT were selected. For each examination, images were acquired or reconstructed in two ways. Twenty images per examination were assessed by 40 radiology residents using visual grading of image criteria. The results were analysed using VGC. Inter-observer reliability was assessed. The results of the visual grading analysis were consistent with expected outcomes. The inter-observer reliability was moderate to good and correlated with perceived image quality (r(2) = 0.47). The median observation time per image or image series was within 2 min. These results suggest that the use of visual grading of image criteria to assess the quality of radiographs provides a rapid method for performing an image quality audit in a clinical environment.