Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study

Background CT deep learning image reconstruction (DLIR) improves image quality by reducing noise compared with adaptive statistical iterative reconstruction-V (ASIR-V). However, objective assessment of low-contrast lesion detectability is lacking. Purpose To investigate low-contrast detectability of hypoattenuating liver lesions on CT scans reconstructed with DLIR compared with CT scans reconstructed with ASIR-V in a patient and a phantom study. Materials and Methods This single-center retrospective study included patients undergoing portal venous phase abdominal CT between February and May 2021 and a low-contrast-resolution phantom scanned with the same protocol. Four reconstructions (ASIR-V at 40% strength [ASIR-V 40] and DLIR at three strengths) were generated. Five radiologists qualitatively assessed the images using the five-point Likert scale for image quality, lesion diagnostic confidence, conspicuity, and small lesion (≤1 cm) visibility. Up to two key lesions per patient, confirmed at histopathologic testing or at prior or follow-up imaging studies, were included. Lesion-to-background contrast-to-noise ratio was calculated. Interreader variability was analyzed. Intergroup qualitative and quantitative metrics were compared between DLIR and ASIR-V 40 using proportional odds logistic regression models. Results Eighty-six liver lesions (mean size, 15 mm ± 9.5 [SD]) in 50 patients (median age, 62 years [IQR, 57-73 years]; 27 [54%] female patients) were included. Differences were not detected for various qualitative low-contrast detectability metrics between ASIR-V 40 and DLIR (P > .05). Quantitatively, medium-strength DLIR and high-strength DLIR yielded higher lesion-to-background contrast-to-noise ratios than ASIR-V 40 (medium-strength DLIR vs ASIR-V 40: odds ratio [OR], 1.96 [95% CI: 1.65, 2.33]; high-strength DLIR vs ASIR-V 40: OR, 5.36 [95% CI: 3.68, 7.82]; P < .001). Low-contrast lesion attenuation was reduced by 2.8-3.6 HU with DLIR. Interreader agreement was moderate to very good for the qualitative metrics. Subgroup analysis based on lesion size of larger than 1 cm and 1 cm or smaller yielded similar results (P > .05). Qualitatively, phantom study results were similar to those in patients (P > .05). Conclusion The detectability of low-contrast liver lesions was similar on CT scans reconstructed with low-, medium-, and high-strength DLIR and ASIR-V 40 in both patient and phantom studies. Lesion-to-background contrast-to-noise ratios were higher for DLIR medium- and high-strength reconstructions compared with ASIR-V 40. © RSNA, 2024 Supplemental material is available for this article.

Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study

RATIONALE AND OBJECTIVES

This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image quality in computed tomography (CT) images across various field of view (FOV) sizes, radiation doses, and noise reduction strengths.

MATERIALS AND METHODS

A Catphan phantom equipped with an external body ring was used. CT images were reconstructed using filtered back-projection (FBP), HIR, NR-DLR, and SR-DLR across three noise reduction strengths: mild, standard, and strong. The noise power spectrum (NPS) was obtained from the FBP, HIR, NR-DLR, and SR-DLR images at various FOVs, radiation doses, and noise reduction strengths. The noise magnitude ratio (NMR) and central frequency ratio (CFR) were calculated from the HIR, NR-DLR, and SR-DLR images relative to the FBP images using NPS. The high-contrast value was obtained from the amplitude values of the peaks and valleys of profile curve and the task-based transfer function were also analyzed.

RESULTS

SR-DLR consistently demonstrated superior noise reduction capabilities, with NMR of 0.29-0.36 at reduced dose and 0.35-0.45 at standard dose, outperforming HIR and showing comparable efficiency to NR-DLR. The high-contrast values for SR-DLR were highest at mild and standard levels for both low and standard doses (0.610 and 0.726 at mild and 0.725 and 0.603 at standard levels). At the standard dose, the spatial resolution of SR-DLR was significantly improved, regardless of the noise reduction strength and FOV.

CONCLUSION

SR-DLR images achieved more substantial noise reduction than HIR and similar noise reduction as NR-DLR reconstructions while also improving spatial resolution.

New liver window width in detecting hepatocellular carcinoma on dynamic contrast-enhanced computed tomography with deep learning reconstruction

Changing a window width (WW) alters appearance of noise and contrast of CT images. The aim of this study was to investigate the impact of adjusted WW for deep learning reconstruction (DLR) in detecting hepatocellular carcinomas (HCCs) on CT with DLR. This retrospective study included thirty-five patients who underwent abdominal dynamic contrast-enhanced CT. DLR was used to reconstruct arterial, portal, and delayed phase images. The investigation of the optimal WW involved two blinded readers. Then, five other blinded readers independently read the image sets for detection of HCCs and evaluation of image quality with optimal or conventional liver WW. The optimal WW for detection of HCC was 119 (rounded to 120 in the subsequent analyses) Hounsfield unit (HU), which was the average of adjusted WW in the arterial, portal, and delayed phases. The average figures of merit for the readers for the jackknife alternative free-response receiver operating characteristic analysis to detect HCC were 0.809 (reader 1/2/3/4/5, 0.765/0.798/0.892/0.764/0.827) in the optimal WW (120 HU) and 0.765 (reader 1/2/3/4/5, 0.707/0.769/0.838/0.720/0.791) in the conventional WW (150 HU), and statistically significant difference was observed between them (p < 0.001). Image quality in the optimal WW was superior to those in the conventional WW, and significant difference was seen for some readers (p < 0.041). The optimal WW for detection of HCC was narrower than conventional WW on dynamic contrast-enhanced CT with DLR. Compared with the conventional liver WW, optimal liver WW significantly improved detection performance of HCC.

Noise power spectrum properties of deep learning-based reconstruction and iterative reconstruction algorithms: Phantom and clinical study

PURPOSE

To compare the noise power spectrum (NPS) properties and perform a qualitative analysis of hybrid iterative reconstruction (IR), model-based IR (MBIR), and deep learning-based reconstruction (DLR) at a similar noise level in clinical study and compare these outcomes with those in phantom study.

METHODS

A Catphan phantom with an external body ring was used in the phantom study. In the clinical study, computed tomography (CT) examination data of 34 patients were reviewed. NPS was calculated from DLR, hybrid IR, and MBIR images. The noise magnitude ratio (NMR) and the central frequency ratio (CFR) were calculated from DLR, hybrid IR, and MBIR images relative to filtered back-projection images using NPS. Clinical images were independently reviewed by two radiologists.

RESULTS

In the phantom study, DLR with a mild level had a similar noise level as hybrid IR and MBIR with strong levels. In the clinical study, DLR with a mild level had a similar noise level as hybrid IR with standard and MBIR with strong levels. The NMR and CFR were 0.40 and 0.76 for DLR, 0.42 and 0.55 for hybrid IR, and 0.48 and 0.62 for MBIR. The visual inspection of the clinical DLR image was superior to that of the hybrid IR and MBIR images.

CONCLUSION

Deep learning-based reconstruction improves overall image quality with substantial noise reduction while maintaining image noise texture compared with the CT reconstruction techniques.

Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction

PURPOSE

This study aimed to compare the hepatocellular carcinoma (HCC) detection performance, interobserver agreement for Liver Imaging Reporting and Data System (LI-RADS) categories, and image quality between deep learning reconstruction (DLR) and conventional hybrid iterative reconstruction (Hybrid IR) in CT.

METHODS

This retrospective study included patients who underwent abdominal dynamic contrast-enhanced CT between October 2021 and March 2022. Arterial, portal, and delayed phase images were reconstructed using DLR and Hybrid IR. Two blinded readers independently read the image sets with detecting HCCs, scoring LI-RADS, and evaluating image quality.

RESULTS

A total of 26 patients with HCC (mean age, 73 years ± 12.3) and 23 patients without HCC (mean age, 66 years ± 14.7) were included. The figures of merit (FOM) for the jackknife alternative free-response receiver operating characteristic analysis in detecting HCC averaged for the readers were 0.925 (reader 1, 0.937; reader 2, 0.913) in DLR and 0.878 (reader 1, 0.904; reader 2, 0.851) in Hybrid IR, and the FOM in DLR were significantly higher than that in Hybrid IR (p = 0.038). The interobserver agreement (Cohen's weighted kappa statistics) for LI-RADS categories was moderate for DLR (0.595; 95% CI, 0.585-0.605) and significantly superior to Hybrid IR (0.568; 95% CI, 0.553-0.582). According to both readers, DLR was significantly superior to Hybrid IR in terms of image quality (p ≤ 0.021).

CONCLUSION

DLR improved HCC detection, interobserver agreement for LI-RADS categories, and image quality in evaluations of HCC compared to Hybrid IR in abdominal dynamic contrast-enhanced CT.

Radiation dose optimization potential of deep learning-based reconstruction for multiphase hepatic CT: A clinical and phantom study

PURPOSE

This clinical and phantom study aimed to evaluate the impact of deep learning-based reconstruction (DLR) on image quality and its radiation dose optimization capability for multiphase hepatic CT relative to hybrid iterative reconstruction (HIR).

METHODS

Task-based image quality was assessed with a physical evaluation phantom; the high- and low-contrast detectability of HIR and DLR images were computed from the noise power spectrum and task-based transfer function at five different size-specific dose estimate (SSDE) values in the range 5.3 to 18.0-mGy. For the clinical study, images of 73 patients who had undergone multiphase hepatic CT under both standard-dose (STD) and lower-dose (LD) examination protocols within a time interval of about four-months on average, were retrospectively examined. STD images were reconstructed with HIR, while LD with HIR (LD-HIR) and DLR (LD-DLR). SSDE, quantitative image noise, and contrast-to-noise ratio (CNR) were compared between protocols. The noise magnitude, noise texture, streak artifact, image sharpness, interface smoothness, and overall image quality were subjectively rated by two independent radiologists.

RESULTS

In phantom study, the high- and low-contrast detectability of DLR images obtained at 5.3-mGy and 7.3-mGy, respectively, were slightly higher than those obtained with HIR at the STD protocol dose (18.0-mGy). In clinical study, LD-DLR yielded lower image noise, higher CNR, and higher subjective scores for all evaluation criteria than STD (all, p ≤ 0.05), despite having 52.8% lower SSDE (8.0 ± 2.5 vs. 16.8 ± 3.4-mGy).

CONCLUSIONS

DLR improved the subjective and objective image quality of multiphase hepatic CT compared with HIR techniques, even at approximately half the radiation dose.

Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction—A 20-reader study on a semi-anthropomorphic liver phantom

Background

A novel deep learning image reconstruction (DLIR) algorithm for CT has recently been clinically approved.

Purpose

To assess low-contrast detectability and dose reduction potential for CT images reconstructed with the DLIR algorithm and compare with filtered back projection (FBP) and hybrid iterative reconstruction (IR).

Material and methods

A customized upper-abdomen phantom containing four cylindrical liver inserts with low-contrast lesions was scanned at CT dose indexes of 5, 10, 15, 20 and 25 mGy. Images were reconstructed with FBP, 50% hybrid IR (IR50), and DLIR of low strength (DLL), medium strength (DLM) and high strength (DLH). Detectability was assessed by 20 independent readers using a two-alternative forced choice approach. Dose reduction potential was estimated separately for each strength of DLIR using a fitted model, with the detectability performance of FBP and IR50 as reference.

Results

For the investigated dose levels of 5 and 10 mGy, DLM improved detectability compared to FBP by 5.8 and 6.9 percentage points (p.p.), and DLH improved detectability by 9.6 and 12.3 p.p., respectively (all p < .007). With IR50 as reference, DLH improved detectability by 5.2 and 9.8 p.p. for the 5 and 10 mGy dose level, respectively (p < .03). With respect to this low-contrast detectability task, average dose reduction potential relative to FBP was estimated to 39% for DLM and 55% for DLH. Relative to IR50, average dose reduction potential was estimated to 21% for DLM and 42% for DLH.

Conclusions:

Low-contrast detectability performance is improved when applying a DLIR algorithm, with potential for radiation dose reduction.

•

Deep learning image reconstruction improves low-contrast detectability in CT.

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Performance improved with increasing strength of deep learning image reconstruction.

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Results suggest potential for CT radiation dose reduction.

Variability of quantitative measurements of metastatic liver lesions: a multi-radiation-dose-level and multi-reader comparison

PURPOSE

To evaluate the variability of quantitative measurements of metastatic liver lesions by using a multi-radiation-dose-level and multi-reader comparison.

METHODS

Twenty-three study subjects (mean age, 60 years) with 39 liver lesions who underwent a single-energy dual-source contrast-enhanced staging CT between June 2015 and December 2015 were included. CT data were reconstructed with seven different radiation dose levels (ranging from 25 to 100%) on the basis of a single CT acquisition. Four radiologists independently performed manual tumor measurements and two radiologists performed semi-automated tumor measurements. Interobserver, intraobserver, and interdose sources of variability for longest diameter and volumetric measurements were estimated and compared using Wilcoxon rank-sum tests and intraclass correlation coefficients.

RESULTS

Inter- and intraobserver variabilities for manual measurements of the longest diameter were higher compared to semi-automated measurements (p < 0.001 for overall). Inter- and intraobserver variabilities of volume measurements were higher compared to the longest diameter measurement (p < 0.001 for overall). Quantitative measurements were statistically different at < 50% radiation dose levels for semi-automated measurements of the longest diameter, and at 25% radiation dose level for volumetric measurements. The variability related to radiation dose was not significantly different from the inter- and intraobserver variability for the measurements of the longest diameter.

CONCLUSION

The variability related to radiation dose is comparable to the inter- and intraobserver variability for measurements of the longest diameter. Caution should be warranted in reducing radiation dose level below 50% of a conventional CT protocol due to the potentially detrimental impact on the assessment of lesion response in the liver.

Task-based assessment of neck CT protocols using patient-mimicking phantoms-effects of protocol parameters on dose and diagnostic performance

Objectives

To assess how modifying multiple protocol parameters affects the dose and diagnostic performance of a neck CT protocol using patient-mimicking phantoms and task-based methods.

Methods

Six patient-mimicking neck phantoms containing hypodense lesions of 1 cm diameter and 30 HU contrast and one non-lesion phantom were examined with 36 CT protocols. All possible combinations of the following parameters were investigated: 100- and 120-kVp tube voltage; tube current modulation (TCM) noise levels of SD 7.5, 10, and 14; pitches of 0.637, 0.813, and 1.388; filtered back projection (FBP); and iterative reconstruction (AIDR 3D). Dose-length products (DLPs) and lesion detectability (assessed by 14 radiologists) were compared with the clinical standard protocol (120 kVp, TCM SD 7.5, 0.813 pitch, AIDR 3D).

Results

The DLP of the standard protocol was 25 mGy•cm; the area under the curve (AUC) was 0.839 (95%CI: 0.790–0.888). Combined effects of tube voltage reduction to 100 kVp and TCM noise level increase to SD 10 optimized protocol performance by improving dose (7.3 mGy•cm) and detectability (AUC 0.884, 95%CI: 0.844–0.924). Diagnostic performance was significantly affected by the TCM noise level at 120 kVp (AUC 0.821 at TCM SD 7.5 vs. 0.776 at TCM SD 14, p = 0.003), but not at 100-kVp tube voltage (AUC 0.839 at TCM SD 7.5 vs. 0.819 at TCM SD 14, p = 0.354), the reconstruction method at 100 kVp (AUC 0.854 for AIDR 3D vs. 0.806 for FBP, p < 0.001), but not at 120-kVp tube voltage (AUC 0.795 for AIDR 3D vs. 0.793 for FBP, p = 0.822), and the tube voltage for AIDR 3D reconstruction (p < 0.001), but not for FBP (p = 0.226).

Conclusions

Combined effects of 100-kVp tube voltage, TCM noise level of SD 10, a pitch of 0.813, and AIDR 3D resulted in an optimal neck protocol in terms of dose and diagnostic performance. Protocol parameters were subject to complex interactions, which created opportunities for protocol improvement.

Key Points

• A task-based approach using patient-mimicking phantoms was employed to optimize a CT system for neck imaging through systematic testing of protocol parameters.

• Combined effects of 100-kVp tube voltage, TCM noise level of SD 10, a pitch of 0.813, and AIDR 3D reconstruction resulted in an optimal protocol in terms of dose and diagnostic performance.

• Interactions of protocol parameters affect diagnostic performance and should be considered when optimizing CT techniques.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07374-8) contains supplementary material, which is available to authorized users.

Deep Learning-based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases

Purpose

To evaluate the effect of a deep learning-based reconstruction (DLR) method on the conspicuity of hypovascular hepatic metastases on abdominal CT images.

Materials and Methods

This retrospective study with institutional review board approval included 58 patients with hypovascular hepatic metastases. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and the contrast-to-noise ratio (CNR). CNR was calculated as region of interest ([ROI]_L - ROI_T)/N, where ROI_L is the mean liver parenchyma attenuation, ROI_T, the mean tumor attenuation, and N, the noise. Two other radiologists graded the conspicuity of the liver lesion on a five-point scale where 1 is unidentifiable and 5 is detected without diagnostic compromise. Only the smallest liver lesion in each patient, classified as smaller or larger than 10 mm, was evaluated. The difference between hybrid iterative reconstruction (IR) and DLR images was determined by using a two-sided Wilcoxon signed-rank test.

Results

The image noise was significantly lower, and the CNR was significantly higher on DLR images than hybrid IR images (median image noise: 19.2 vs 12.8 HU, P < .001; median CNR: tumors < 10 mm: 1.9 vs 2.5; tumors > 10 mm: 1.7 vs 2.2, both P < .001). The scores for liver lesions were significantly higher for DLR images than hybrid IR images (P < .01 for both in tumors smaller or larger than 10 mm).

Conclusion

DLR improved the quality of abdominal CT images for the evaluation of hypovascular hepatic metastases.© RSNA, 2019Supplemental material is available for this article.

Determination of optimum exposure parameters for dentoalveolar structures of the jaws using the CB MercuRay system with cluster signal-to-noise analysis

OBJECTIVE

To determine the optimum cone beam computed tomography exposure parameters for specific diagnostic tasks.

METHODS

A Teflon phantom attached to a half-mandible in a large container was scanned in dental (D), implant (I), and panoramic (P) modes. An identical phantom in a small container was scanned in D mode. Both were scanned at 60, 80, 100, and 120 kV. We evaluated the image quality of five anatomical structures [dentinoenamel junction (1), lamina dura and periodontal ligament space (2), trabecular pattern (3), cortex-spongy bone junction (4), and pulp chamber and root canal (5)] and analyzed the diagnostic image quality with cluster signal-to-noise analysis. We then evaluated correlations between the two image qualities and calculated the threshold of acceptable diagnostic image quality. Optimum exposure parameters were determined from images with acceptable diagnostic image quality.

RESULTS

For the small container, the optimum exposure parameters were D mode, 80 kV for (1), (3), and (4) and D mode, 100 kV for (5). For the large container, they were D mode, 120 kV for (1), (3), and (5) and D mode, 100 kV for (4). I mode, 120 kV reached the acceptable level for (4). No images reached the acceptable level for (2).

CONCLUSIONS

No optimum exposure parameters were identified for the evaluation of the lamina dura and periodontal ligament space. D mode was sufficient for the other structures; however, the tube voltage required for each structure differed. Smaller patients required lower tube voltage. I mode, 120 kV may be used for larger lesions.

Dual-layer dual-energy computed tomography for the assessment of hypovascular hepatic metastases: impact of closing k-edge on image quality and lesion detectability

OBJECTIVES

To evaluate the image quality of virtual-monoenergetic-imaging (VMI) from dual-layer dual-energy CT (DLCT) for the assessment of hypovascular liver metastases and its effect on lesion detectability.

METHODS

Eighty-one patients with hypovascular-liver-metastases undergoing portal-venous-phase abdominal DLCT were included. Polyenergetic-images (PEI) and VMI at 40-200 keV (VMI_40-200, 10-keV interval) were reconstructed. Image noise, tumor-to-liver contrast, and contrast-to-noise ratio (CNR) of hepatic parenchyma and metastatic nodules (n = 288) were measured to determine the optimal monoenergetic levels. Two radiologists independently and subjectively assessed the image quality (image contrast, image noise, and diagnostic confidence) of PEI and optimal VMI on 5-point scales to determine the best energy. For 38 patients having up to 10 metastases each with diameters < 25 mm (153 lesions), we compared blindly assessed lesion detectability and conspicuity between PEI and VMI at the best energy.

RESULTS

Image noise of VMI_40-200 was consistently lower than that of PEI (p < 0.01). Tumor-to-liver contrast and CNR increased as the energy decreased with CNR at VMI_40-70 being higher than that observed on PEI (p < 0.01). The highest subjective score for diagnostic confidence was assigned at VMI₄₀ followed by VMI_50-70, all of which were significantly better than that of PEI (p < 0.01, kappa = 0.75). Lesion detectability at VMI₄₀ was significantly superior to PEI, especially for lesions with diameters of < 10 mm (p < 0.01, kappa ≥ 0.6).

CONCLUSIONS

VMI_40-70 provided a better subjective and objective image quality for the evaluation of hypovascular liver metastases, and the lesion detectability was improved with use of VMI₄₀ compared with conventional PEI.

KEY POINTS

• DLCT-VMI at 40-70 keV provides a superior subjective and objective image quality compared with conventional PEI for the assessment of hypovascular hepatic metastases during portal venous phase. • Tumor-to-liver contrast and CNR of hypovascular hepatic metastases was maximized at 40 keV without a relevant increase in the image noise. • VMI at 40 keV yields a superior lesion detectability, especially for small (< 1 cm) metastatic nodules compared with conventional PEI.

CT Detectability of Small Low-Contrast Hypoattenuating Focal Lesions: Iterative Reconstructions versus Filtered Back Projection

Purpose To investigate performance in detectability of small (≤1 cm) low-contrast hypoattenuating focal lesions by using filtered back projection (FBP) and iterative reconstruction (IR) algorithms from two major CT vendors across a range of 11 radiation exposures. Materials and Methods A low-contrast detectability phantom consisting of 21 low-contrast hypoattenuating focal objects (seven sizes between 2.4 and 10.0 mm, three contrast levels) embedded into a liver-equivalent background was scanned at 11 radiation exposures (volume CT dose index range, 0.5-18.0 mGy; size-specific dose estimate [SSDE] range, 0.8-30.6 mGy) with four high-end CT platforms. Data sets were reconstructed by using FBP and varied strengths of image-based, model-based, and hybrid IRs. Sixteen observers evaluated all data sets for lesion detectability by using a two-alternative-forced-choice (2AFC) paradigm. Diagnostic performances were evaluated by calculating area under the receiver operating characteristic curve (AUC) and by performing noninferiority analyses. Results At benchmark exposure, FBP yielded a mean AUC of 0.79 ± 0.09 (standard deviation) across all platforms which, on average, was approximately 2% lower than that observed with the different IR algorithms, which showed an average AUC of 0.81 ± 0.09 (P = .12). Radiation decreases of 30%, 50%, and 80% resulted in similar declines of observer detectability with FBP (mean AUC decrease, -0.02 ± 0.05, -0.03 ± 0.05, and -0.05 ± 0.05, respectively) and all IR methods investigated (mean AUC decrease, -0.00 ± 0.05, -0.04 ± 0.05, and -0.04 ± 0.05, respectively). For each radiation level and CT platform, variance in performance across observers was greater than that across reconstruction algorithms (P = .03). Conclusion Iterative reconstruction algorithms have limited radiation optimization potential in detectability of small low-contrast hypoattenuating focal lesions. This task may be further complicated by a high degree of variation in radiologists' performances, seemingly exceeding real performance differences among reconstruction algorithms. © RSNA, 2018 Online supplemental material is available for this article.

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.

Dual-layer DECT for multiphasic hepatic CT with 50 percent iodine load: a matched-pair comparison with a 120 kVp protocol

OBJECTIVES

To evaluate the image quality and lesion conspicuity of virtual-monochromatic-imaging (VMI) with dual-layer DECT (DL-DECT) for reduced-iodine-load multiphasic-hepatic CT.

METHODS

Forty-five adults with renal dysfunction who had undergone hepatic DL-DECT with 300-mgI/kg were included. VMI (40-70-keV, DL-DECT-VMI) was generated at each enhancement phase. As controls, 45 matched patients undergoing standard 120-kVp protocol (120-kVp, 600-mgI/kg, and iterative reconstruction) were included. We compared the size-specific dose estimate (SSDE), image noise, CT attenuation, and contrast-to-noise ratio (CNR) between protocols. Two radiologists scored the image quality and lesion conspicuity.

RESULTS

SSDE was significantly lower in DL-DECT group (p < 0.01). Image noise of DL-DECT-VMI was almost constant at each keV (differences of ≤15%) and equivalent to or lower than of 120-kVp. As the energy decreased, CT attenuation and CNR gradually increased; the values of 55-60 keV images were almost equivalent to those of standard 120-kVp. The highest scores for overall quality and lesion conspicuity were assigned at 40-keV followed by 45 to 55-keV, all of which were similar to or better than of 120-kVp.

CONCLUSIONS

For multiphasic-hepatic CT with 50% iodine-load, DL-DECT-VMI at 40- to 55-keV provides equivalent or better image quality and lesion conspicuity without increasing radiation dose compared with standard 120-kVp protocol.

KEY POINTS

• 40-55-keV yields optimal image quality for half-iodine-load multiphasic-hepatic CT with DL-DECT. • DL-DECT protocol decreases radiation exposure compared with 120-kVp scans with iterative reconstruction. • 40-keV images maximise conspicuity of hepatocellular carcinoma especially at hepatic-arterial phase.

Cluster signal-to-noise analysis for evaluation of the information content in an image

OBJECTIVES

(1) To develop an observer-free method of analysing image quality related to the observer performance in the detection task and (2) to analyse observer behaviour patterns in the detection of small mass changes in cone-beam CT images.

METHODS

13 observers detected holes in a Teflon phantom in cone-beam CT images. Using the same images, we developed a new method, cluster signal-to-noise analysis, to detect the holes by applying various cut-off values using ImageJ and reconstructing cluster signal-to-noise curves. We then evaluated the correlation between cluster signal-to-noise analysis and the observer performance test. We measured the background noise in each image to evaluate the relationship with false positive rates (FPRs) of the observers. Correlations between mean FPRs and intra- and interobserver variations were also evaluated. Moreover, we calculated true positive rates (TPRs) and accuracies from background noise and evaluated their correlations with TPRs from observers.

RESULTS

Cluster signal-to-noise curves were derived in cluster signal-to-noise analysis. They yield the detection of signals (true holes) related to noise (false holes). This method correlated highly with the observer performance test (R² = 0.9296). In noisy images, increasing background noise resulted in higher FPRs and larger intra- and interobserver variations. TPRs and accuracies calculated from background noise had high correlation with actual TPRs from observers; R² was 0.9244 and 0.9338, respectively.

CONCLUSIONS

Cluster signal-to-noise analysis can simulate the detection performance of observers and thus replace the observer performance test in the evaluation of image quality. Erroneous decision-making increased with increasing background noise.

Liver detection algorithm: its efficacy for CT noise reduction in the liver

OBJECTIVES

This study was performed to evaluate the efficacy of a novel computed tomography (CT) liver detection algorithm (LDA), which allows for targeted increase of radiation dose to the upper abdomen, on image quality of the liver.

METHODS

We retrospectively evaluated the LDA by comparing 40 consecutive patients who had portal venous CT abdomen performed without use of the algorithm, to 40 patients in whom the algorithm was used. Image quality was assessed objectively by comparing the standard deviation (SD) of attenuation values in Hounsfield units (HU) of the abdominal organs. Qualitative analysis was performed by two blinded radiologists who independently graded the image quality of abdominal organs

RESULTS

There was significant noise reduction in the liver (P < 0.001) and spleen (P < 0.001) in the LDA group compared to the conventional group. There was also a significant improvement in image quality of the liver (P < 0.001), kidney (P < 0.001), spleen (P < 0.001), pancreas (P < 0.001), and psoas (P = 0.005) in the LDA group compared to the conventional group. Overall dose between the two groups was similar.

CONCLUSIONS

This liver detection algorithm improves the subjective image quality of upper abdominal organs, in particular the liver, without increasing overall radiation dose.

Emerging techniques for dose optimization in abdominal CT

Recent advances in computed tomographic (CT) scanning technique such as automated tube current modulation (ATCM), optimized x-ray tube voltage, and better use of iterative image reconstruction have allowed maintenance of good CT image quality with reduced radiation dose. ATCM varies the tube current during scanning to account for differences in patient attenuation, ensuring a more homogeneous image quality, although selection of the appropriate image quality parameter is essential for achieving optimal dose reduction. Reducing the x-ray tube voltage is best suited for evaluating iodinated structures, since the effective energy of the x-ray beam will be closer to the k-edge of iodine, resulting in a higher attenuation for the iodine. The optimal kilovoltage for a CT study should be chosen on the basis of imaging task and patient habitus. The aim of iterative image reconstruction is to identify factors that contribute to noise on CT images with use of statistical models of noise (statistical iterative reconstruction) and selective removal of noise to improve image quality. The degree of noise suppression achieved with statistical iterative reconstruction can be customized to minimize the effect of altered image quality on CT images. Unlike with statistical iterative reconstruction, model-based iterative reconstruction algorithms model both the statistical noise and the physical acquisition process, allowing CT to be performed with further reduction in radiation dose without an increase in image noise or loss of spatial resolution. Understanding these recently developed scanning techniques is essential for optimization of imaging protocols designed to achieve the desired image quality with a reduced dose.

Impact of incremental increase in CT image noise on detection of low-contrast hypodense liver lesions

RATIONALE AND OBJECTIVES

To determine the impact of incremental increases in computed tomography (CT) image noise on detection of low-contrast hypodense liver lesions.

MATERIAL AND METHODS

We studied 50 CT examinations acquired at image noise index (NI) of 15 and hypodense liver lesions and 50 examinations with no lesions. Validation of a noise addition tool to be used in the evaluation of the CT examinations was performed with a liver phantom. Using this tool, three 100-image sets were assembled: an NI of 17.4 (simulating 75% of the original patient radiation dose), 21.2 (simulating 50% dose), and 29.7 (simulating 25%). Three readers scored certainty of lesion presence using a five-point Likert scale.

RESULTS

For original images (NI 15) plus images with NI of 17.4 and 21.2, sensitivity was >90% threshold (range, 95%-98%). For images with NI of 29.7, sensitivity was just below the threshold (89%). Reader Az values for receiver operating characteristic curves were good for original, NI 17.4, and NI 21.2 images (0.976, 0.973, and 0.96, respectively). For NI of 29.7, the Az decreased to 0.913. Detection sensitivity was <90% for both lesion size < 10 mm (85%) and lesion-to-liver contrast <60 Hounsfield units (85%) only at NI 29.7.

CONCLUSIONS

For low-contrast lesion detection in liver CT, image noise can be increased up to NI 21.2 (a 50% patient radiation dose reduction) without substantial reduction in sensitivity.

Exposure (mAs) optimisation of a multi-detector CT protocol for hepatic lesion detection: Are thinner slices better?

INTRODUCTION

The purpose of this work was to determine the exposure-optimised slice thickness for hepatic lesion detection with CT.

METHODS

A phantom containing spheres (diameter 9.5, 4.8 and 2.4 mm) with CT density 10 HU below the background (50 HU) was scanned at 125, 100, 75 and 50 mAs. Data were reconstructed at 5-, 3- and 1-mm slice thicknesses. Noise, contrast-to-noise ratio (CNR), area under the curve (AUC) as calculated using receiver operating characteristic analysis and sensitivity representing lesion detection were calculated and compared.

RESULTS

Compared with the 125 mAs/5 mm slice thickness setting, significant reductions in AUC were found for 75 mAs (P < 0.01) and 50 mAs (P < 0.05) at 1- and 3-mm thicknesses, respectively; sensitivity for the 9.5-mm sphere was significantly reduced for 75 (P < 0.05) and 50 mAs (P < 0.01) at 1-mm thickness; sensitivity for the 4.8-mm sphere was significantly lower for 100, 75 and 50 mAs at all three slice thicknesses (P < 0.05). The 2.4-mm sphere was rarely detected. At each slice thickness, noise at 100, 75 and 50 mAs exposures was approximately 10, 30 and 50% higher, respectively, than that at 125 mAs exposure. CNRs decreased in an irregular manner with reductions in exposure and slice thickness.

CONCLUSION

This study demonstrated no advantage to using slices below 5 mm thickness, and consequently thinner slices are not necessarily better.

Effect of reduced radiation exposure and iterative reconstruction on detection of low-contrast low-attenuation lesions in an anthropomorphic liver phantom: an 18-reader study

PURPOSE

To measure the effect of reduced radiation exposure on low-contrast low-attenuation liver lesion detection in an anthropomorphic abdominal phantom by using filtered back projection (FBP) and sinogram-affirmed iterative reconstruction.

MATERIALS AND METHODS

Eighteen radiologists blinded to phantom and study design interpreted randomized image data sets that contained 36 spherical simulated liver lesions of three sizes and three attenuation differences (5-mm diameter: 12, 18, and 24 HU less than the 90-HU background attenuation of the simulated liver insert; 10- and 15-mm diameter: 6, 12, and 18 HU less than the 90-HU background attenuation) scanned with four discrete exposure settings and reconstructed by using FBP and sinogram-affirmed iterative reconstruction. Response assessment included region-level lesion presence or absence on a five-point diagnostic confidence scale. Statistical evaluation included multireader multicase receiver operating characteristic curve analysis, with nonparametric methods and noninferiority analysis at a margin of -0.10.

RESULTS

Pooled accuracy at 75% exposure for both FBP and sinogram-affirmed iterative reconstruction was noninferior to 100% exposure (P = .002 and P < .001, respectively). Subsequent exposure reductions resulted in a significant decrease in accuracy. When the smallest (5-mm-diameter) lesions were excluded from analysis, sinogram-affirmed iterative reconstruction was superior to FBP at 100% exposure (P = .011), and sinogram-affirmed iterative reconstruction at 25% and 50% exposure reduction was noninferior to FBP at 100% exposure (P ≤ .013). Reader confidence was greater with sinogram-affirmed iterative reconstruction than with FBP for 10- and 15-mm lesions (2.94 vs 2.76 and 3.62 vs 3.52, respectively).

CONCLUSION

In this low-contrast low-attenuation liver lesion model, a 25% exposure reduction maintained noninferior diagnostic accuracy. However, detection was inferior with each subsequent exposure reduction, regardless of reconstruction method. Sinogram-affirmed iterative reconstruction and FBP performed equally well at modest exposure reduction (25%-50%). Readers had higher confidence levels with sinogram-affirmed iterative reconstruction for the 10- and 15-mm lesions.

Contrast-to-noise ratio and low-contrast object resolution on full- and low-dose MDCT: SAFIRE versus filtered back projection in a low-contrast object phantom and in the liver

OBJECTIVE

The purpose of this article is to evaluate the effect of sinogram-affirmed iterative reconstruction (SAFIRE) on contrast-to-noise ratio (CNR) compared with filtered back projection (FBP) and to determine whether SAFIRE improves low-contrast object detection or conspicuity in a low-contrast object phantom and in the liver on full- and low-dose examinations.

SUBJECTS AND METHODS

A low-contrast object phantom was scanned at 100%, 70%, 50%, and 30% dose using a single-source made of a dual-source MDCT scanner, with the raw data reconstructed with SAFIRE and FBP. Unenhanced liver CT scans in 22 patients were performed using a dual-source MDCT. The raw data from both tubes (100% dose) were reconstructed using FBP, and data from one tube (50% dose) were reconstructed using both FBP and SAFIRE. CNR was measured in the phantom and in the liver. Noise, contrast, and CNR were compared using paired Student t tests. Six readers assessed sphere detection and conspicuity in the phantom and liver-inferior vena cava conspicuity in the patient data. The phantom and patient data were assessed using multiple-variable logistic regression.

RESULTS

The phantom at 70% and 50% doses with SAFIRE had decreased noise and increased CNR compared with the 100% dose with FBP. In the liver, the mean CNR improvement at 50% dose with SAFIRE compared with FBP was 31.4% and 88% at 100% and 50% doses, respectively (p < 0.001). Sphere object detection and conspicuity improved with SAFIRE (p < 0.001). However, smaller spheres were obscured on both FBP and SAFIRE images at lower doses. Liver-vessel conspicuity improved with SAFIRE over 50%-dose FBP in 67.4% of cases (p < 0.001), and versus 100%-dose FBP, improved in 38.6% of cases (p = 0.085). As a predictor for detection, CNR alone had a discriminatory ability (c-index, 0.970) similar to that of the model that analyzed dose, lesion size, attenuation difference, and reconstruction technique (c-index, 0.978).

CONCLUSION

Lower dose scans reconstructed with SAFIRE have a higher CNR. The ability of SAFIRE to improve low-contrast object detection and conspicuity depends on the radiation dose level. At low radiation doses, low-contrast objects are invisible, regardless of reconstruction technique.

CT enterography of Crohn's disease

CT enterography (CTE) is a technique using neutral oral contrast, intravenous contrast and thin cut, multiplanar CT acquisitions to optimize small bowel imaging. One of the primary indications for CTE is the detection and evaluation of Crohn's disease. This article summarizes the advantages/disadvantages, scanning technique, imaging findings, performance and pitfalls of CTE for the evaluation of Crohn's disease.