Third version of vendor-specific model-based iterativereconstruction (Veo 3.0): evaluation of CT image quality in the abdomen using new noise reduction presets and varied slice optimization

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction

OBJECTIVE

To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images.

METHODS

In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases.

RESULTS

Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05].

CONCLUSIONS

DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction

Background

Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions.

Methods

We conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta.

Results

There were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants' body mass index and waist circumference.

Conclusions

The optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.

Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction

PURPOSE

To assess whether image quality differences between SECT (single-energy CT) and DECT (dual-energy CT 70 keV) with equivalent radiation doses result in altered detection and characterization accuracy of liver metastases when using deep learning image reconstruction (DLIR), and whether DECT spectral curve usage improves accuracy of indeterminate lesion characterization.

METHODS

In this prospective Health Insurance Portability and Accountability Act-compliant study (March through August 2022), adult men and non-pregnant adult women with biopsy-proven colorectal cancer and liver metastases underwent SECT (120 kVp) and a DECT (70 keV) portovenous abdominal CT scan using DLIR in the same breath-hold (Revolution CT ES; GE Healthcare). Participants were excluded if consent could not be obtained, if there were nonequivalent radiation doses between the two scans, or if the examination was cancelled/rescheduled. Three radiologists independently performed lesion detection and characterization during two separate sessions (SECT DLIRmedium and DECT DLIRhigh) as well as reported lesion confidence and overall image quality. Hounsfield units were measured. Spectral HU curves were provided for any lesions rated as indeterminate. McNemar's test was used to test the marginal homogeneity in terms of diagnostic sensitivity, accuracy and lesion detection. A generalized estimating equation method was used for categorical outcomes.

RESULTS

30 participants (mean age, 58 years ± 11, 21 men) were evaluated. Mean CTDIvol was 34 mGy for both scans. 141 lesions (124 metastases, 17 benign) with a mean size of 0.8 cm ± 0.3 cm were identified. High scores for image quality (scores of 4 or 5) were not significantly different between DECT (N = 71 out of 90 total scores from the three readers) and SECT (N = 62) (OR, 2.01; 95% CI:0.89, 4.57; P = 0.093). Equivalent image noise to SECT DLIRmed (HU SD 10 ± 2) was obtained with DECT DLIRhigh (HU SD 10 ± 3) (P = 1). There was no significant difference in lesion detection between DECT and SECT (140/141 lesions) (99.3%; 95% CI:96.1%, 100%).The mean lesion confidence scores by each reader were 4.2 ± 1.3, 3.9 ± 1.0, and 4.8 ± 0.8 for SECT and 4.1 ± 1.4, 4.0 ± 1.0, and 4.7 ± 0.8 for DECT (odds ratio [OR], 0.83; 95% CI: 0.62, 1.11; P = 0.21). Small lesion (≤5mm) characterization accuracy on SECT and DECT was 89.1% (95% CI:76.4%, 96.4%; 41/46) and 84.8% (71.1%, 93.7%; 39/46), respectively (P = 0.41). Use of spectral HU lesion curves resulted in 34 correct changes in characterizations and no mischaracterizations.

CONCLUSION

DECT required a higher strength of DLIR to obtain equivalent noise compared to SECT DLIR. At equivalent radiation doses and image noise, there was no significant difference in subjective image quality or observer lesion performance between DECT (70 keV) and SECT. However, DECT spectral HU curves of indeterminate lesions improved characterization.

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

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

Low pitch significantly reduces helical artifacts in abdominal CT

PURPOSE

High helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image quality. This study aims to determine the severity and incidence of helical artifacts in abdominal CT imaging and their relation to the helical pitch scan parameter.

METHODS

To obtain a dataset with varying pitch values, we used CT exam data both internal and external to our center. A cohort of 59 consecutive adult patients receiving an abdomen CT examination at our center with an accompanying prior examination from an external center was selected for retrospective review. Two expert observers performed a blinded rating of helical artifact in each examination using a five-point Likert scale. The incidence of artifacts with respect to the helical pitch was assessed. A generalized linear mixed-effects regression (GLMER) model, with study arm (Internal or External to our center) and helical pitch as the fixed-effect predictor variables, was fit to the artifact ratings, and significance of the predictor variables was tested.

RESULTS

For a pitch of <0.75, the proportion of exams with mild or worse helical artifacts (Likert scores of 1-3) was <1%. The proportion increased to 16% for exams with pitch between 0.75 and 1.2, and further increased to 78% for exams with a pitch greater than 1.2. Pitch was significantly associated with helical artifact in the GLMER model (p = 2.8 × 10-9), while study arm was not a significant factor (p = 0.76).

CONCLUSION

The incidence and severity of helical artifact increased with helical pitch. This difference persisted even after accounting for the potential confounding factor of the center where the study was performed.

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.

Metal artifacts reduction in computed tomography: A phantom study to compare the effectiveness of metal artifact reduction algorithm, model-based iterative reconstruction, and virtual monochromatic imaging

The purpose of this study was to compare the effectiveness of a metal artifact reduction algorithm (MAR), model-based iterative reconstruction (MBIR), and virtual monochromatic imaging (VMI) for reducing metal artifacts in CT imaging.A phantom study was performed for quantitatively evaluating the dark bands and fine streak artifacts generated by unilateral hip prostheses. Images were obtained by conventional scanning at 120 kilovolt peak, and reconstructed using filtered back projection, MAR, and MBIR. Furthermore, virtual monochromatic images (VMIs) at 70 kilo-electron volts (keV) and 140 keV with/without use of MAR were obtained by dual-energy CT. The extents and mean CT values of the dark bands and the differences in the standard deviations and location parameters of the fine streak artifacts evaluated by the Gumbel method in the images obtained by each of the methods were statistically compared by analyses of variance.Significant reduction of the extent of the dark bands was observed in the images reconstructed using MAR than in those not reconstructed using MAR (all, P < .01). Images obtained by VMI at 70 keV and 140 keV with use of MAR showed significantly increased mean CT values of the dark bands as compared to those obtained by reconstructions without use of MAR (all, <.01). Significant reduction of the difference in the standard deviations used to evaluate fine streak artifacts was observed in each of the image sets obtained with VMI at 140 keV with/without MAR and conventional CT with MBIR as compared to the images obtained using other methods (all, P < .05), except between VMI at 140 keV without MAR and conventional CT with MAR. The location parameter to evaluate fine streak artifacts was significantly reduced in CT images obtained using MBIR and in images obtained by VMI at 140 keV with/without MAR as compared to those obtained using other reconstruction methods (all, P < .01).In our present study, MAR appeared to be the most effective reconstruction method for reducing dark bands in CT images, and MBIR and VMI at 140 keV appeared to the most effective for reducing streak artifacts.

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.

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience

OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April-May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92-94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.

Comparison of Abdominal Computed Tomographic Enhancement and Organ Lesion Depiction Between Weight-Based Scanner Software Contrast Dosing and a Fixed-Dose Protocol in a Tertiary Care Oncologic Center

OBJECTIVE

This study aimed to evaluate the quality of enhancement and solid-organ lesion depiction using weight-based intravenous (IV) contrast dosing calculated by injector software versus fixed IV contrast dose in oncologic abdominal computed tomographic (CT) examinations.

METHODS

This institutional review board-exempt retrospective cohort study included 134 patients who underwent single-phase abdominal CT before and after implementation of weight-based IV contrast injector software. Patient weight, height, body mass index, and body surface area were determined. Two radiologists qualitatively assessed examinations (4 indicating markedly superior to -4 indicating markedly inferior), and Hounsfield unit measurements were performed.

RESULTS

Enhancement (estimated mean, -0.05; 95% confidence interval [CI], -0.19 to 0.09; P = 0.46) and lesion depiction (estimated mean, -0.01; 95% CI, -0.10 to 0.07; P = 0.79) scores did not differ between CT examinations using weight-based IV contrast versus fixed IV contrast dosing when a minimum of 38.5 g of iodine was used. However, the scores using weight-based IV contrast dosing were lower when the injector software calculated and delivered less than 38.5 g of iodine (estimated mean, -0.81; 95% CI, -1.06 to -0.56; P < 0.0001). There were no significant differences in measured Hounsfield units between the CT examinations using weight-based IV contrast dosing versus fixed IV contrast dosing.

CONCLUSIONS

Oncologic CT image quality was maintained or improved with weight-based IV contrast dosing using injector software when using a minimum amount of 38.5 g of iodine.

Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT

Purpose To evaluate colorectal cancer hepatic metastasis detection and characterization between reduced radiation dose (RD) and standard dose (SD) contrast material-enhanced CT of the abdomen and to qualitatively compare between filtered back projection (FBP) and iterative reconstruction algorithms. Materials and Methods In this prospective study (from May 2017 through November 2017), 52 adults with biopsy-proven colorectal cancer and suspected hepatic metastases at baseline CT underwent two portal venous phase CT scans: SD and RD in the same breath hold. Three radiologists, blinded to examination details, performed detection and characterization of 2-15-mm lesions on the SD FBP and RD adaptive statistical iterative reconstruction (ASIR)-V 60% series images. Readers assessed overall image quality and lesions between SD FBP and seven different iterative reconstructions. Two nonblinded consensus reviewers established the reference standard using the picture archiving and communication system lesion marks of each reader, multiple comparison examinations, and clinical data. Results RD CT resulted in a mean dose reduction of 54% compared with SD. Of the 260 lesions (233 metastatic, 27 benign), 212 (82%; 95% confidence interval [CI]: 76%, 86%) were detected with RD CT, whereas 252 (97%; 95% CI: 94%, 99%) were detected with SD (P < .001); per-lesion sensitivity was 79% (95% CI: 74%, 84%) and 94% (95% CI: 90%, 96%) (P < .001), respectively. Mean qualitative scores ranked SD images as higher quality than RD series images, and ASIR-V ranked higher than ASIR and Veo 3.0. Conclusion CT evaluation of colorectal liver metastases is compromised with modest radiation dose reduction, and the use of iterative reconstructions could not maintain observer performance. © RSNA, 2018.