Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol. 2013;48:598-606. [PMID: 23511193 DOI: 10.1097/rli.0b013e3182899104]

Investigating the effectiveness of MAR algorithm on magnitude of artifacts in CBCT images: a systematic review

BACKGROUND

There has been an increasing interest in the use of implants to treat edentulous patients. In this regard, the use of cone beam computed tomography (CBCT) offers a variety of advantages compared with other imaging methods. However, the creation of beam-hardening artifacts adversely affects the quality of images. To our knowledge, little is known about the actual effectiveness of the Metal Artifact Reduction (MAR) algorithm on image quality improvement.

OBJECTIVES

The objective of this study is to conduct a systematic review to investigate the effectiveness of the MAR algorithm on the magnitude of artifacts generated in CBCT images.

MATERIALS AND METHODS

An electronic search was performed in electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. For each database, the search strategy was defined specifically. Studies that had the inclusion criteria for this review were imported into Endnote version 20. The risk of bias in the studies included in this systematic review was assessed by two independent reviewers based on the Joanna Briggs Institute (JBI)'s Critical Appraisal checklist. The selected final articles were scored based on the specified checklist. After reviewing selected articles, it was not possible to perform a meta-analysis due to the heterogeneity and multiplicity of the variables, and the studies were included in the systematic review.

RESULTS

A total of 4738 studies were identified. After eliminating duplicate and unrelated articles, 10 articles met the inclusion criteria. Results showed that the use of the MAR algorithm in the preparation of CBCT scans reduces the standard deviation (SD) of gray values. However, no definite result was achieved in relation to the contrast-to-noise ratio (CNR). In fact, it cannot be definitively concluded whether the use of the MAR algorithm will increase the CNR.

CONCLUSION

The results of this systematic review demonstrated that we cannot provide a definite answer regarding the effect of the MAR algorithm on reducing the artifacts around dental implants. The explanation is that this factor is affected by many variables, whose change can have a significant effect on the magnitude of artifacts generated in the image.

Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan

OBJECTIVE

The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based iterative reconstruction (MBIR) from a single CT using dual-split scan in patients with suspected liver metastasis via a noninferiority design.

MATERIALS AND METHODS

This prospective study enrolled participants who met the eligibility criteria at 2 tertiary hospitals in South Korea from June 2022 to January 2023. The criteria included (a) being aged between 20 and 85 years and (b) having suspected or known liver metastases. Dual-source CT scans were conducted, with the standard radiation dose divided in a 2:1 ratio between tubes A and B (67% and 33%, respectively). The voltage settings of 100/120 kVp were selected based on the participant's body mass index (<30 vs ≥30 kg/m2). For image reconstruction, MBIR was utilized for standard-dose (100%) images, whereas DLR was employed for both low-dose (67%) and ultra-low-dose (33%) images. Three radiologists independently evaluated FLL conspicuity, the probability of metastasis, and subjective image quality using a 5-point Likert scale, in addition to quantitative signal-to-noise and contrast-to-noise ratios. The noninferiority margins were set at -0.5 for conspicuity and -0.1 for detection.

RESULTS

One hundred thirty-three participants (male = 58, mean body mass index = 23.0 ± 3.4 kg/m2) were included in the analysis. The low- and ultra-low- dose had a lower radiation dose than the standard-dose (median CT dose index volume: 3.75, 1.87 vs 5.62 mGy, respectively, in the arterial phase; 3.89, 1.95 vs 5.84 in the portal venous phase, P < 0.001 for all). Median FLL conspicuity was lower in the low- and ultra-low-dose scans compared with the standard-dose (3.0 [interquartile range, IQR: 2.0, 4.0], 3.0 [IQR: 1.0, 4.0] vs 3.0 [IQR: 2.0, 4.0] in the arterial phase; 4.0 [IQR: 1.0, 5.0], 3.0 [IQR: 1.0, 4.0] vs 4.0 [IQR: 2.0, 5.0] in the portal venous phases), yet within the noninferiority margin (P < 0.001 for all). FLL detection was also lower but remained within the margin (lesion detection rate: 0.772 [95% confidence interval, CI: 0.727, 0.812], 0.754 [0.708, 0.795], respectively) compared with the standard-dose (0.810 [95% CI: 0.770, 0.844]). Sensitivity for liver metastasis differed between the standard- (80.6% [95% CI: 76.0, 84.5]), low-, and ultra-low-doses (75.7% [95% CI: 70.2, 80.5], 73.7 [95% CI: 68.3, 78.5], respectively, P < 0.001 for both), whereas specificity was similar (P > 0.05).

CONCLUSIONS

Low- and ultra-low-dose CT with DLR showed noninferior FLL conspicuity and detection compared with standard-dose CT with MBIR. Caution is needed due to a potential decrease in sensitivity for metastasis (clinicaltrials.gov/ NCT05324046).

Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Objective.Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse problem. Diffusion models can generate data/images by reversing a data-noising process through learned data distributions; and can be incorporated as a denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a diffusion model-based framework, prior frequency-guided diffusion model (PFGDM), for robust and structure-preserving LA-CBCT reconstruction.Approach.PFGDM uses a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and the condition is based on high-frequency information extracted from patient-specific prior CT scans which provides a strong anatomical prior for LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM (PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the high-frequency CT information condition until a pre-optimized iteration step, and drops it afterwards to enable both similar and differing CT/CBCT anatomies to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans. The two variants of PFGDM were tested and compared with current available LA-CBCT reconstruction solutions, via metrics including peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).Main results.PFGDM outperformed all traditional and diffusion model-based methods. The mean(s.d.) PSNR/SSIM were 27.97(3.10)/0.949(0.027), 26.63(2.79)/0.937(0.029), and 23.81(2.25)/0.896(0.036) for PFGDM-A, and 28.20(1.28)/0.954(0.011), 26.68(1.04)/0.941(0.014), and 23.72(1.19)/0.894(0.034) for PFGDM-B, based on 120°, 90°, and 30° orthogonal-view scan angles respectively. In contrast, the PSNR/SSIM was 19.61(2.47)/0.807(0.048) for 30° for DiffusionMBIR, a diffusion-based method without prior CT conditioning.Significance. PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles, allowing faster and more flexible CBCT scans with dose reductions.

Advanced Imaging of Total Knee Arthroplasty

The prevalence of total knee arthroplasty (TKA) is increasing with the aging population. Although long-term results are satisfactory, suspected postoperative complications often require imaging with the implant in place. Advancements in computed tomography (CT), such as tin prefiltration, metal artifact reduction algorithms, dual-energy CT with virtual monoenergetic imaging postprocessing, and the application of cone-beam CT and photon-counting detector CT, allow a better depiction of the tissues adjacent to the metal. For magnetic resonance imaging (MRI), high bandwidth (BW) optimization, the combination of view angle tilting and high BW, as well as multispectral imaging techniques with multiacquisition variable-resonance image combination or slice encoding metal artifact correction, have significantly improved imaging around metal implants, turning MRI into a useful clinical tool for patients with suspected TKA complications.

Feasibility of Low-Dose and Low-Contrast Media Volume Approach in Computed Tomography Cardiovascular Imaging Reconstructed with Model-Based Algorithm

Aim: To evaluate the dose reduction and image quality of low-dose, low-contrast media volume in computed tomography (CT) examinations reconstructed with the model-based iterative reconstruction (MBIR) algorithm in comparison with the hybrid iterative (HIR) one. Methods: We prospectively enrolled a total of 401 patients referred for cardiovascular CT, evaluated with a 256-MDCT scan with a low kVp (80 kVp) reconstructed with an MBIR (study group) or a standard HIR protocol (100 kVp-control group) after injection of a fixed dose of contrast medium volume. Vessel contrast enhancement and image noise were measured by placing the region of interest (ROI) in the left ventricle, ascending aorta; left, right and circumflex coronary arteries; main, right and left pulmonary arteries; aortic arch; and abdominal aorta. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed. Subjective image quality obtained by consensus was assessed by using a 4-point Likert scale. Radiation dose exposure was recorded. Results: HU values of the proximal tract of all coronary arteries; main, right and left pulmonary arteries; and of the aorta were significantly higher in the study group than in the control group (p < 0.05), while the noise was significantly lower (p < 0.05). SNR and CNR values in all anatomic districts were significantly higher in the study group (p < 0.05). MBIR subjective image quality was significantly higher than HIR in CCTA and CTPA protocols (p < 0.05). Radiation dose was significantly lower in the study group (p < 0.05). Conclusions: The MBIR algorithm combined with low-kVp can help reduce radiation dose exposure, reduce noise, and increase objective and subjective image quality.

Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction

BACKGROUND

Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial.

OBJECTIVES

To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V).

AIMS

This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality.

METHOD

In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared.

RESULTS

The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30.

CONCLUSION

DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.

Utility of follow-up ultra-high-resolution CT angiography with model-based iterative reconstruction after flow diverter treatment for cerebral aneurysms

PURPOSE

Follow-up examinations after flow diverter (FD) treatment for cerebral aneurysms typically involve magnetic resonance imaging (MRI) or digital subtraction angiography (DSA). However, MRI is prone to vascular defects due to metal artifacts from FD, and DSA carries a risk of ischemic complications. In the context of computed tomography angiography (CTA), this study compares the efficacy of ultra-high-resolution CT (UHRCT) and novel reconstruction techniques, such as model-based iterative reconstruction (MBIR), against conventional methods such as filtered back projection (FBP) and hybrid iterative reconstruction (IR), to determine if they are a viable alternative to DSA in clinical settings.

MATERIALS AND METHODS

A phantom study was conducted with the full-width half-maximum considered as the FD thickness. This study compared three reconstruction methods: MBIR, FBP, and hybrid IR. A clinical study was also conducted with 21 patients who underwent follow-up CTA after FD treatment. The FD's visibility was assessed using a 4-point scale in FBP, hybrid IR, and MBIR compared to cone-beam CT (CBCT) with angiographic systems.

RESULTS

In the phantom study, FBP, hybrid IR, and MBIR visualized thinner FD thicknesses and improved detail rendering in that order. MBIR proved to be significantly superior in both the phantom and clinical study.

CONCLUSION

UHRCT with MBIR is highly effective for follow-up evaluations after FD treatment and may become the first-choice modality in the future.

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.

Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies

BACKGROUND

In coronary computed tomography angiography (CCTA), the main issue of image quality is noise in obese patients, blooming artifacts due to calcium and stents, high-risk coronary plaques, and radiation exposure to patients.

OBJECTIVE

To compare the CCTA image quality of deep learning-based reconstruction (DLR) with that of filtered back projection (FBP) and iterative reconstruction (IR).

METHODS

This was a phantom study of 90 patients who underwent CCTA. CCTA images were acquired using FBP, IR, and DLR. In the phantom study, the aortic root and the left main coronary artery in the chest phantom were simulated using a needleless syringe. The patients were classified into three groups according to their body mass index. Noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured for image quantification. A subjective analysis was also performed for FBP, IR, and DLR.

RESULTS

According to the phantom study, DLR reduced noise by 59.8% compared to FBP and increased SNR and CNR by 121.4% and 123.6%, respectively. In a patient study, DLR reduced noise compared to FBP and IR. Furthermore, DLR increased the SNR and CNR more than FBP and IR. In terms of subjective scores, DLR was higher than FBP and IR.

CONCLUSION

In both phantom and patient studies, DLR effectively reduced image noise and improved SNR and CNR. Therefore, the DLR may be useful for CCTA examinations.

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study

OBJECTIVE

To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC).

METHODS

Participants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis.

RESULTS

Sixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm, p = .003) and 27% lower contrast dose (106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL, p < .001) than the SD protocol. The comparative analysis demonstrated that CNR (p < .001) and portal vein conspicuity (p = .002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p = .140) and HCCs (75.7% vs. 70.4%, p = .644) between the SD protocol and DLD-DL.

CONCLUSIONS

DL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC.

KEY POINTS

• Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality.

Dual-layer spectral detector CT arthrography of the shoulder: assessment of image quality and value in differentiating calcium from iodine

BACKGROUND

Dual-layer spectral detector computed tomography (DLCT) may potentially improve CT arthrography through enhanced image quality and analysis of the chemical composition of tissue.

PURPOSE

To evaluate the image quality of monoenergetic reconstructions from DLCT arthrography of the shoulder and assess the additional diagnostic value in differentiating calcium from iodine.

MATERIAL AND METHODS

Images from consecutive shoulder DLCT arthrography examinations performed between December 2016 and February 2018 were retrospectively reviewed for hyperattenuating lesions within the labrum and tendons. The mean attenuation of the target lesion, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of the virtual monoenergetic images obtained at 40-200 keV were compared with conventional 140-kVp images. Two evaluators independently classified each target lesion as contrast media or calcification, without and with DLCT spectral data. Receiver operating curve (ROC) analysis was performed to assess the diagnostic performance of shoulder DLCT arthrography, without and with the aid of spectral data.

RESULTS

The study included 20 target lesions (18 DLCT arthrography examinations of 17 patients). The SNRs of the monoenergetic images at 40-60 keV were significantly higher than those of conventional images (P < 0.05). The CNRs of the monoenergetic images at 40-70 keV were significantly higher than those of conventional images (P < 0.001). The ability to differentiate calcium from iodine, without and with DLCT spectral data, did not significantly differ (P = 0.441 and P = 0.257 for reviewers 1 and 2, respectively).

CONCLUSION

DLCT had no additive value in differentiating calcium from iodine in small, hyperattenuating lesions in the labrum and tendons.

Metal artifact reduction in ultra-high-resolution cone-beam CT imaging with a twin robotic X-ray system

Cone-beam computed tomography (CBCT) has been shown to be a powerful tool for 3D imaging of the appendicular skeleton, allowing for detailed visualization of bone microarchitecture. This study was designed to compare artifacts in the presence of osteosynthetic implants between CBCT and multidetector computed tomography (MDCT) in cadaveric wrist scans. A total of 32 scan protocols with varying tube potential and current were employed: both conventional CBCT and MDCT studies were included with tube voltage ranging from 60 to 140 kVp as well as additional MDCT protocols with dedicated spectral shaping via tin prefiltration. Irrespective of scanner type, all examinations were conducted in ultra-high-resolution (UHR) scan mode. For reconstruction of UHR-CBCT scans an additional iterative metal artifact reduction algorithm was employed, an image correction tool which cannot be used in combination with UHR-MDCT. To compare applied radiation doses between both scanners, the volume computed tomography dose index for a 16 cm phantom (CTDIvol) was evaluated. Images were assessed regarding subjective and objective image quality. Without automatic tube current modulation or tube potential control, radiation doses ranged between 1.3 mGy (with 70 kVp and 50.0 effective mAs) and 75.2 mGy (with 140 kVp and 383.0 effective mAs) in UHR-MDCT. Using the pulsed image acquisition method of the CBCT scanner, CTDIvol ranged between 2.3 mGy (with 60 kVp and 0.6 mean mAs per pulse) and 61.0 mGy (with 133 kVp and 2.5 mean mAs per pulse). In essence, all UHR-CBCT protocols employing a tube potential of 80 kVp or more were found to provide superior overall image quality and artifact reduction compared to UHR-MDCT (all p < .050). Interrater reliability of seven radiologists regarding image quality was substantial for tissue assessment and moderate for artifact assessment with Fleiss kappa of 0.652 (95% confidence interval 0.618-0.686; p < 0.001) and 0.570 (95% confidence interval 0.535-0.606; p < 0.001), respectively. Our results demonstrate that the UHR-CBCT scan mode of a twin robotic X-ray system facilitates excellent visualization of the appendicular skeleton in the presence of metal implants. Achievable image quality and artifact reduction are superior to dose-comparable UHR-MDCT and even MDCT protocols employing spectral shaping with tin prefiltration do not achieve the same level of artifact reduction in adjacent soft tissue.

Diagnostic accuracy of ultra-low-dose CT colonography for the detection of colorectal polyps: a feasibility study

PURPOSE

The aim of this feasibility study was to evaluate the diagnostic accuracy of ultra-low-dose CT colonography using iterative reconstruction algorithms with reference to standard colonoscopy.

MATERIALS AND METHODS

Prior to this study, a phantom study was performed to investigate the optimal protocol for ultra-low-dose CT colonography. A total of 206 patients with average/high risk of colorectal cancer were recruited. After undergoing full bowel preparation, the patients were scanned in the prone and supine positions with the CT conditions set to 120 kV, standard deviation 45 to 50, and an adaptive iterative reconstruction algorithm applied. Two expert readers read the images independently. The main outcome measures were the per-patient and per-polyp accuracies for the detection of polyps ≥ 10 mm, with colonoscopy results as the reference standard.

RESULTS

Two hundred patients (102 females, mean age 67.5 years) underwent both ultra-low-dose CT colonography and colonoscopy on the same day. The mean radiation exposure dose was 0.64 ± 0.34 mSv. On colonoscopy, 39 patients had 45 polyps ≥ 10 mm (non-polypoid morphology 7), including 4 cancers. Per-patient sensitivity, specificity, and accuracy of CT colonography for polyps ≥ 10 mm were 0.74, 0.96, and 0.92 for reader one, and 0.74, 0.99, and 0.94 for reader two, respectively. Per-polyp sensitivities for polyps ≥ 10 mm were 0.73 for reader one and 0.71 for reader two. On subgroup analysis by morphology, non-polypoid polyps ≥ 10 mm were not detected by both readers.

CONCLUSION

Extreme ultra-low-dose CT colonography had an insufficient diagnostic performance for the detection of polyps ≥ 10 mm, because it was unable to detect non-polypoid polyps. This study showed that the problem with ultra-low-dose CT colonography was the lack of detectability of small-size polyps, especially non-polypoid polyps. To use ultra-low-dose CT colonography clinically, it is necessary to resolve the problems identified by this study.

Spectral imaging with dual-layer spectral detector computed tomography for the detection of perfusion defects in acute coronary syndrome

To evaluate the feasibility of spectral imaging with dual-layer spectral detector computed tomography (CT) for the diagnosis of acute coronary syndrome. We identified 30 consecutive patients who underwent cardiac CT using dual-layer spectral detector CT and were diagnosed with acute ischemic syndrome by an invasive coronary angiography. We reconstructed 120 kVp images and generated virtual monochromatic images (VMIs; 40-200 keV in 10 keV increments), iodine concentration maps, and effective atomic number (Z) maps. We calculated the contrast and contrast-to-noise ratio (CNR) between myocardial normal and hypo-perfusion and chose the VMIs with the best CNR for quantitative analysis. We compared the image noise, contrast, and CNR of 120 kVp images and the best VMIs, CT value, iodine concentration, and effective Z between myocardial normal and hypo-perfusion with the paired t test. As the X-ray energy decreased, venous attenuation, contrast, and CNR gradually increased. The 40 keV image yielded the best CNR. The contrast and CNR between myocardial normal and hypo-perfusion were significantly higher in 40 keV images than those in 120 kVp images. The iodine concentration and the effective Z were significantly higher in normal myocardium than those in hypo-perfused myocardium. Spectral imaging with dual-layer spectral detector CT is a feasible technique to detect the hypo-perfused area of acute ischemic syndrome.

Clinical acceptance of deep learning reconstruction for abdominal CT imaging: objective and subjective image quality and low-contrast detectability assessment

OBJECTIVE

To evaluate the image quality and clinical acceptance of a deep learning reconstruction (DLR) algorithm compared to traditional iterative reconstruction (IR) algorithms.

METHODS

CT acquisitions were performed with two phantoms and a total of nine dose levels. Images were reconstructed with two types of IR algorithms, DLR and filtered-back projection. Spatial resolution, image texture, mean noise value, and objective and subjective low-contrast detectability were compared. Ten senior radiologists evaluated the clinical acceptance of these algorithms by scoring ten CT exams reconstructed with the DLR and IR algorithms evaluated.

RESULTS

Compared to MBIR, DLR yielded a lower noise and a higher low-contrast detectability index at low doses (CTDI_vol ≤ 2.2 and ≤ 4.5 mGy, respectively). Spatial resolution and detectability at higher doses were better with MBIR. Compared to HIR, DLR yielded a higher spatial resolution, a lower noise, and a higher detectability index. Despite these differences in algorithm performance, significant differences in subjective low-contrast performance were not found (p ≥ 0.005). DLR texture was finer than that of MBIR and closer to that of HIR. Radiologists preferred DLR images for all criteria assessed (p < 0.0001), whereas MBIR was rated worse than HIR (p < 0.0001) in all criteria evaluated, except for noise (p = 0.044). DLR reconstruction time was 12 times faster than that of MBIR.

CONCLUSION

DLR yielded a gain in objective detection and noise at lower dose levels with the best clinical acceptance among the evaluated reconstruction algorithms.

KEY POINTS

• DLR yielded improved objective low-contrast detection and noise at lower dose levels. • Despite the differences in objective detectability among the algorithms evaluated, there were no differences in subjective detectability. • DLR presented significantly higher clinical acceptability scores compared to MBIR and HIR.

Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions

OBJECTIVES

To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3% dose) using deep learning denoising (DLD) to standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction (MBIR).

METHODS

In this retrospective study, CT images of 80 patients with hepatic focal lesions were included. For noninferiority analysis of overall image quality, a margin of - 0.5 points (scored in a 5-point scale) for the difference between scan protocols was pre-defined. Other quantitative or qualitative image quality assessments were performed. Additionally, detectability of significant liver lesions was compared, with 64 pairs of CT, using the jackknife alternative free-response ROC analysis, with noninferior margin defined by the lower limit of 95% confidence interval (CI) of the difference of figure-of-merit less than - 0.1.

RESULTS

The mean overall image quality scores with LDCT and SDCT were 3.77 ± 0.38 and 3.94 ± 0.34, respectively, demonstrating a difference of - 0.17 (95% CI: - 0.21 to - 0.12), which did not cross the predefined noninferiority margin of - 0.5. Furthermore, LDCT showed significantly superior quantitative results of liver lesion contrast to noise ratio (p < 0.05). However, although LDCT scored higher than the average score in qualitative image quality assessments, they were significantly lower than those of SDCT (p < 0.05). Figure-of-merit for lesion detection was 0.859 for LDCT and 0.878 for SDCT, showing noninferiority (difference: - 0.019, 95% CI: - 0.058 to 0.021).

CONCLUSION

LDCT using DLD with 67% radiation dose reduction showed non-inferior overall image quality and lesion detectability, compared to SDCT.

KEY POINTS

• Low-dose liver CT using deep learning denoising (DLD), at 67% dose reduction, provided non-inferior overall image quality compared to standard-dose CT using model-based iterative reconstruction (MBIR). • Low-dose CT using DLD showed significantly less noise and higher CNR _{lesion to liver} than standard-dose CT using MBIR and demonstrated at least average image quality score among all readers, albeit with lower scores than standard-dose CT using MBIR. • Low-dose liver CT showed noninferior detectability for malignant and pre-malignant liver lesions, compared to standard-dose CT.

Comparison of Knowledge-based Iterative Model Reconstruction (IMR) with Hybrid Iterative Reconstruction (iDose4) Techniques for Evaluation of Hepatocellular Carcinomas Using Computed Tomography

RATIONALE AND OBJECTIVES

To compare tumor conspicuity of small hepatocellular carcinomas (HCCs) and image quality on knowledge-based iterative model reconstruction low-dose computed tomography (IMR-LDCT) with hybrid iterative reconstruction standard-dose CT (iDose⁴-SDCT).

METHODS

Thirty-two patients (mean age 61.9 ± 9.7 years; male:female 27:5; mean body mass index 25.6 ± 3.8 kg/m²) with cirrhosis and 40 HCCs in IMR-LDCT group and 33 patients (mean age 60.1 ± 7.4 years; male:female 28:5; body mass index 26.7 ± 3.2 kg/m²) with cirrhosis and 40 HCCs in iDose⁴-SDCT group were included in this retrospective study. Objective analysis of reconstructed iDose⁴ and IMR images was done for contrast-to-noise ratio of HCCs (CNR_HCC), image noise, signal-to-noise ratio of portal vein (SNR_PV)_, and inferior vena cava (SNR_IVC). Subjective analysis of tumor conspicuity and image quality was done by two independent reviewers in a blinded manner. Mean volume CT dose index, dose length product, and effective dose for both groups were compared.

RESULTS

The CNR_HCC was significantly higher in IMR-LDCT compared to iDose⁴-SDCT in both arterial phase (AP), p < 0.0001, and delayed phase (DP), p < 0.0001. Image noise was significantly lower in IMR-LDCT compared to iDose⁴-SDCT in AP, portal venous phase, and DP with p < 0.0001. IMR-LDCT showed significantly higher SNR_PV (p < 0.0001) and SNR_IVC (p < 0.0001) compared to iDose⁴-SDCT. On subjective analysis, IMR-LDCT images showed better image quality in AP, portal venous phase, and DP and better tumor conspicuity in AP and DP. IMR-LDCT (21.4 ± 4.6 mSv) achieved 36.9% reduction in the effective dose compared to iDose⁴-SDCT (33.9 ± 6.2 mSv).

CONCLUSION

IMR algorithm provides better image quality and tumor conspicuity with considerable decrease in image noise compared to iDose⁴ reconstruction technique even on LDCT.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

BACKGROUND AND PURPOSE

To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach.

MATERIALS AND METHODS

We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics.

RESULTS

A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher (P=0.003).

CONCLUSION

Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment

Background

Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment.

Methods

MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted.

Results

Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle.

Conclusions

Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.

Advanced CT techniques for assessing hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic dynamic CT studies are routinely performed for its evaluation. Ongoing studies are examining advanced imaging techniques that may yield better findings than are obtained with conventional hepatic dynamic CT scanning. Dual-energy CT-, perfusion CT-, and artificial intelligence-based methods can be used for the precise characterization of liver tumors, the quantification of treatment responses, and for predicting the overall survival rate of patients. In this review, the advantages and disadvantages of conventional hepatic dynamic CT imaging are reviewed and the general principles of dual-energy- and perfusion CT, and the clinical applications and limitations of these technologies are discussed with respect to HCC. Finally, we address the utility of artificial intelligence-based methods for diagnosing HCC.

Deep learning-based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study

OBJECTIVES

(1) To compare low-contrast detectability of a deep learning-based denoising algorithm (DLA) with ADMIRE and FBP, and (2) to compare image quality parameters of DLA with those of reconstruction methods from two different CT vendors (ADMIRE, IMR, and FBP).

MATERIALS AND METHODS

Using abdominal CT images of 100 patients reconstructed via ADMIRE and FBP, we trained DLA by feeding FBP images as input and ADMIRE images as the ground truth. To measure the low-contrast detectability, the randomized repeat scans of Catphan® phantom were performed under various conditions of radiation exposures. Twelve radiologists evaluated the presence/absence of a target on a five-point confidence scale. The multi-reader multi-case area under the receiver operating characteristic curve (AUC) was calculated, and non-inferiority tests were performed. Using American College of Radiology CT accreditation phantom, contrast-to-noise ratio, target transfer function, noise magnitude, and detectability index (d') of DLA, ADMIRE, IMR, and FBPs were computed.

RESULTS

The AUC of DLA in low-contrast detectability was non-inferior to that of ADMIRE (p < .001) and superior to that of FBP (p < .001). DLA improved the image quality in terms of all physical measurements compared to FBPs from both CT vendors and showed profiles of physical measurements similar to those of ADMIRE.

CONCLUSIONS

The low-contrast detectability of the proposed deep learning-based denoising algorithm was non-inferior to that of ADMIRE and superior to that of FBP. The DLA could successfully improve image quality compared with FBP while showing the similar physical profiles of ADMIRE.

KEY POINTS

• Low-contrast detectability in the images denoised using the deep learning algorithm was non-inferior to that in the images reconstructed using standard algorithms. • The proposed deep learning algorithm showed similar profiles of physical measurements to advanced iterative reconstruction algorithm (ADMIRE).

Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT

OBJECTIVES

We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest radiation dose reduction while preserving the diagnostic value.

METHODS

Evaluated were 72 patients who had undergone hepatic dynamic U-HRCT; 36 were scanned with the standard radiation dose (SD group) and 36 with 70% of the SD (lower dose [LD] group). Hepatic arterial and equilibrium phase (HAP, EP) images were reconstructed with hybrid-IR in the SD group, and with hybrid-IR, MBIR, and DLR in the LD group. One radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). Superiority and equivalence with prespecified margins were assessed.

RESULTS

With respect to the image noise, in the HAP and EP, LD DLR and LD MBIR images were superior to SD hybrid-IR images; LD hybrid-IR images were neither superior nor equivalent to SD hybrid-IR images. With respect to the quality scores, only LD DLR images were superior to SD hybrid-IR images.

CONCLUSIONS

DLR preserved the quality of abdominal U-HRCT images even when scanned with a reduced radiation dose.

KEY POINTS

• Lower dose DLR images were superior to the standard-dose hybrid-IR images quantitatively and qualitatively at abdominal U-HRCT. • Neither hybrid-IR nor MBIR may allow for a radiation dose reduction at abdominal U-HRCT without compromising the image quality. • Because DLR allows for a reduction in the radiation dose and maintains the image quality even at the thinnest slice section, DLR should be applied to abdominal U-HRCT scans.

Impact of preset and postset adaptive statistical iterative reconstruction-V on image quality in nonenhanced abdominal-pelvic CT on wide-detector revolution CT

Background

Adaptive statistical iterative reconstruction-V technique (ASIR-V) is usually set at different strengths according to the different clinical requirements and scenarios encountered when setting scanning protocols, such as setting a more aggressive tube current reduction (defined as preset ASIR-V). Reconstruction with ASIR-V is useful after scanning using image algorithms to improve image quality (defined as postset ASIR-V). The aim of this study was to investigate the quality of images reconstructed with preset and postset ASIR-V, using the same noncontrast abdominal-pelvic computed tomography (CT) protocols in the same individual on a wide detector CT.

Methods

We prospectively enrolled 141 patients. The scan protocols in Groups A-E were 0%, 20%, 40%, 60%, and 80% preset ASIR-V, respectively, in the 256 wide-detector row Revolution CT (GE Healthcare, Waukesha, WI, USA). Each group was further divided into 5 subgroups with 0%, 20%, 40%, 60%, and 80% postset ASIR-V, respectively. The 64-detector Discovery 750 HDCT (GE, USA) was used for Group F as a control group, using 0%, 20%, 40%, 60%, and 80% ASIR, respectively. Image noise was measured in the spleen, aorta, and muscle. The CT attenuation and image noise were analyzed using the paired t-test; analysis of variance and post hoc multiple comparisons were made using the Student-Newman-Keuls (SNK) method.

Results

The CT attenuation in Groups A-F exhibited no significant difference between subgroups in three organs (P>0.05). Only with increasing preset ASIR-V% (Groups A to E), did the image noise decrease, except in Group B in the aorta and muscle (Noise_B > Noise_A, P_muscleA&B=0.233, P_aortaA&B=0.796). Only with increasing postset ASIR-V or ASIR% (Groups A and F), did the image noise decrease in the three organs. After preset and postset ASIR-V were combined, with preset ASIR-V% being equal to postset ASIR-V%, the image become similar to the corresponding preset ASIR-V part with the line of postset ASIR-V 0% (baseline of each group). When preset ASIR-V% was greater than the postset ASIR-V%, the image noise was higher than the baseline of each group. When preset ASIR-V% was less than the postset ASIR-V%, the image noise was lower than the baseline of each group. The radiation dose from B to E decreased from 11.2% to 57.1%. The CT dose index volume (CTDI_vol) and dose length product (DLP) in Group F were significantly higher than those in Group A.

Conclusions

Using both preset and postset ASIR-V allows dose reduction, with a potential to improve image quality only when postset ASIR-V% is higher than or equal to preset ASIR-V%. The image quality depends on postset ASIR-V%, whereas the decrease of radiation dose depends on preset ASIR-V%.

Improved detectability of hypoattenuating focal pancreatic lesions by dual-layer computed tomography using virtual monoenergetic images

Background

Multidetector CT is the mainstay for radiologic evaluation of pancreatic pathology. Still, imaging of focal pancreatic lesions using MDCT is faced by a number of challenges that are related to the limited contrast between the lesion and surrounding parenchyma, such as detecting early-stage pancreatic cancer and subtle features of cystic lesions that point to malignancy. Dual-layer CT is the first dual-energy CT machine based on separation of high- and low-energy photons at the detector level. If improved contrast between the lesions and normal pancreatic parenchyma could be achieved on CT images, we may expect enhanced CT detection of pancreatic lesions. The purpose of this study was to evaluate whether virtual monoenergetic reconstructions generated using contrast-enhanced dual-layer CT could improve detectability of hypoattenuating focal pancreatic lesions compared to conventional polyenergetic reconstructions.

Results

Fifty-four lesions were identified and verified by histopathology or follow-up CT, MRCP, and/or EUS along with clinical data. Across the virtual monoenergetic spectrum, 40 KeV images had the highest contrast-to-noise and signal-to-noise ratios (p < 0.001, p < 0.001) and were significantly higher than conventional images (p < 0.001). Subjective scores for lesion visibility at low kiloelectron volt monoenergetic (40 and 50 KeV) images greatly exceeded conventional images (p < 0.001).

Conclusion

Low kiloelectron volt monoenergetic reconstructions of contrast-enhanced dual-layer CT significantly improve detectability of hypoattenuating focal pancreatic lesions compared to conventional polyenergetic reconstructions.

The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting

AIM

To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).

MATERIALS AND METHODS

Thirty-six patients were evaluated retrospectively. All patients underwent contrast-enhanced chest CT and thin-section images were reconstructed using filtered back projection (FBP); ASiR-V (60% and 100% blending setting); and DLIR (low, medium, and high settings). Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated objectively. Two independent radiologists evaluated ASiR-V 60% and DLIR subjectively, in comparison with FBP, on a five-point scale in terms of noise, streak artefact, lymph nodes, small vessels, and overall image quality on a mediastinal window setting (width 400 HU, level 60 HU). In addition, image texture of ASiR-Vs (60% and 100%) and DLIR-high was analysed subjectively.

RESULTS

Compared with ASiR-V 60%, DLIR-med and DLIR-high showed significantly less noise, higher SNR, and higher CNR (p<0.0001). DLIR-high and ASiR-V 100% were not significantly different regarding noise (p=0.2918) and CNR (p=0.0642). At a higher DLIR setting, noise was lower and SNR and CNR were higher (p<0.0001). DLIR-high showed the best subjective scores for noise, streak artefact, and overall image quality (p<0.0001). Compared with ASiR-V 60%, DLIR-med and DLIR-high scored worse in the assessment of small vessels (p<0.0001). The image texture of DLIR-high was significantly finer than that of ASIR-Vs (p<0.0001).

CONCLUSIONS

DLIR-high improved the objective parameters and subjective image quality by reducing noise and streak artefacts and providing finer image texture.

Diagnostic Value of Model-Based Iterative Reconstruction Combined with a Metal Artifact Reduction Algorithm during CT of the Oral Cavity

BACKGROUND AND PURPOSE

Metal artifacts reduce the quality of CT images and increase the difficulty of interpretation. This study compared the ability of model-based iterative reconstruction and hybrid iterative reconstruction to improve CT image quality in patients with metallic dental artifacts when both techniques were combined with a metal artifact reduction algorithm.

MATERIALS AND METHODS

This retrospective clinical study included 40 patients (men, 31; women, 9; mean age, 62.9 ± 12.3 years) with oral and oropharyngeal cancer who had metallic dental fillings or implants and underwent contrast-enhanced ultra-high-resolution CT of the neck. Axial CT images were reconstructed using hybrid iterative reconstruction and model-based iterative reconstruction, and the metal artifact reduction algorithm was applied to all images. Finally, hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithm data were obtained. In the quantitative analysis, SDs were measured in ROIs over the apex of the tongue (metal artifacts) and nuchal muscle (no metal artifacts) and were used to calculate the metal artifact indexes. In a qualitative analysis, 3 radiologists blinded to the patients' conditions assessed the image-quality scores of metal artifact reduction and structural depictions.

RESULTS

Hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithms yielded significantly different metal artifact indexes of 82.2 and 73.6, respectively (95% CI, 2.6-14.7; P < .01). The latter algorithms resulted in significant reduction in metal artifacts and significantly improved structural depictions(P < .01).

CONCLUSIONS

Model-based iterative reconstruction + metal artifact reduction algorithms significantly reduced the artifacts and improved the image quality of structural depictions on neck CT images.

Ultra-high-resolution CT urography: Importance of matrix size and reconstruction technique on image quality

PURPOSE

To evaluate the image quality of CT urography (CTU) obtained with ultra-high-resolution CT (U-HRCT) reconstructed with hybrid iterative reconstruction (IR) and model-based IR algorithms.

METHOD

Forty-eight patients who underwent CTU using the U-HRCT system were enrolled in this retrospective study. Excretory phase images were reconstructed with three protocols: Protocol A: 1024-matrix, 0.25 mm-thickness, and model-based IR; Protocol B: 1024-matrix, 0.25 mm-thickness, and hybrid IR; Protocol C: 512-matrix, 0.5 mm-thickness, and model-based IR. Objective image noise and contrast-to-noise ratio (CNR) of the renal pelvis were compared among the protocols. Three-dimensional maximum intensity projection CTU images were generated from each image data set, and image quality was evaluated by two radiologists.

RESULTS

Protocol C yielded the lowest objective image noise and highest CNR, whereas Protocol A had highest image noise and lowest CNR (P <  0.01). Regarding the detailed delineation of urinary tract structures on the images, the mean visual score was significantly higher for Protocol A than for Protocols B and C (P <  0.001), and the mean score for subjective image noise was significantly lower for Protocol A than for Protocols B and C (P <  0.001).

CONCLUSIONS

CTU with a 1024-matrix and model-based IR depicted the structures of the urinary system in the most detail.

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm

PURPOSE

To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm.

METHODS

Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDI_vol : 0.9, 1.2, 3.6, 7.0, and 22.3 mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high-contrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (f_av ) and frequency at which the TTF was reduced to 50% (f_50% ), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDI_vol : 1.0, 2.1, 3.0, 6.0, and 10.0 mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, η , following AAPM TG233 methods. All measurements were compared among the algorithms.

RESULTS

Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74 ± 6% and 68 ± 4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55 ± 4% lower NPS f_av compared to FBP while True Fidelity had only marginally different noise frequency content with 9 ± 5% lower NPS f_av compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0 mGy dose level η values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f_50% ). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f_50% of up to 42% and 36%, respectively.

CONCLUSIONS

The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

Diagnostic Performance of Dual-Layer Computed Tomography for Deep Vein Thrombosis in Indirect Computed Tomography Venography

BACKGROUND

The aim of this study was to evaluate the quality and diagnostic performance of virtual monochromatic images (VMI) obtained with dual-layer dual-energy computed tomography (DL-DECT) during indirect CT venography (CTV) for deep vein thrombosis (DVT).

METHODS AND RESULTS

This retrospective study was approved by the Institutional Review Board, which waived the requirement for informed consent. We retrospectively enrolled 45 patients who underwent CTV with DL-DECT, and VMI were retrospectively generated. We compared the venous attenuation, noise, contrast, and contrast-to-noise ratio (CNR) between VMI with the highest CNR and conventional CT on paired t-test. Furthermore, we compared the pooled area under the curve (AUC) of each technique with Delong's test in 34 patients who underwent color Doppler ultrasonography. The 40-keV VMI had the best CNR. The noise was significantly lower on 40-keV (9.7±2.5 HU) than on 120-kVp VMI (10.5±2.5 HU; P<0.01). The contrast (120 kVp, 38.2±15.3 HU vs. 40 keV, 131.6±43.6 HU) and CNR (120 kVp, 3.8±1.7 vs. 40 keV, 14.4±6.1) were significantly higher in 40-keV VMI than in 120-kVp VMI (P<0.01). Furthermore, the pooled AUC was significantly higher for 40-keV (0.84) than for 120-kVp VMI (0.78; P=0.03).

CONCLUSIONS

In indirect CTV, 40-keV VMI obtained with DL-DECT offers better image quality and diagnostic performance for DVT than conventional CT.

Pediatric head computed tomography with advanced modeled iterative reconstruction: focus on image quality and reduction of radiation dose

BACKGROUND

Iterative reconstruction has become the standard method for reconstructing computed tomography (CT) scans and needs to be verified for adaptation.

OBJECTIVE

To assess the image quality after adapting advanced modeled iterative reconstruction (ADMIRE) for pediatric head CT.

MATERIALS AND METHODS

We included image sets with filtered back projection reconstruction (the cFBP group, n=105) and both filtered back projection and ADMIRE reconstruction (the lower-dose group, n=109) after dose reduction. All five strength levels of ADMIRE and filtered back projection were adapted for the lower-dose group and compared with the cFBP group. Quantitative parameters including noise, signal-to-noise ratio and contrast-to-noise ratio and qualitative parameters including noise, white matter and gray matter differentiation of the supra- and infratentorial levels, sharpness, artifact, and diagnostic accuracy were also evaluated and compared with interobserver agreement.

RESULTS

There was a mean dose reduction of 30.6% in CT dose index volume, 32.1% in dose length product, and 32.1% in effective dose after tube current reduction. There was gradual reduction of noise in air, cerebrospinal fluid and white matter with strength levels of ADMIRE from 1 to 5 (P<0.001). Signal-to-noise ratio and contrast-to-noise ratio in all age groups increased among strength levels of ADMIRE, in sequence from 1 to 5, with statistical significance (P<0.001). Gradual reduction of qualitative parameters was noted among strength levels of ADMIRE in sequence from 1 to 5 (P<0.001).

CONCLUSION

Use of ADMIRE for pediatric head CT can reduce radiation dose without degrading image quality.

Usefulness of Virtual Monochromatic Dual-Layer Computed Tomographic Imaging for Breast Carcinoma

OBJECTIVE

This study aimed to evaluate virtual monochromatic images (VMIs) obtained using dual-layer dual-energy computed tomography (CT) for breast carcinoma.

METHODS

We retrospectively enrolled 28 patients with breast cancer who were pathologically diagnosed using dual-layer dual-energy CT. Virtual monochromatic images (40-200 keV) were generated. We compared CT number, image noise, contrast, and contrast-to-noise ratio (CNR) between VMIs with the highest CNR and conventional CT images. We performed qualitative image analysis between VMIs at optimized energy and conventional CT images.

RESULTS

Image noise of VMIs was not significantly different from that of the conventional CT images. As the x-ray energy decreased, CNR increased. The 40-keV VMIs were highest CNR and higher than that of the conventional CT images. In qualitative image analysis, the 40-keV images were significantly higher than conventional CT images.

CONCLUSION

Both qualitative and quantitative analyses showed that the image quality of VMIs at 40 keV was significantly higher than that of conventional CT images.

Coronary CT Angiography with Knowledge-Based Iterative Model Reconstruction for Assessing Coronary Arteries and Non-Calcified Predominant Plaques

Objective

To assess the effects of iterative model reconstruction (IMR) on image quality for demonstrating non-calcific high-risk plaque characteristics of coronary arteries.

Materials and Methods

This study included 66 patients (53 men and 13 women; aged 39–76 years; mean age, 55 ± 13 years) having single-vessel disease with predominantly non-calcified plaques evaluated using prospective electrocardiogram-gated 256-slice CT angiography. Paired image sets were created using two types of reconstruction: hybrid iterative reconstruction (HIR) and IMR. Plaque characteristics were compared using the two algorithms. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images and the CNR between the plaque and adjacent adipose tissue were also compared between the two reformatted methods.

Results

Seventy-seven predominantly non-calcified plaques were detected. Forty plaques showed napkin-ring sign with the IMR reformatted method, while nineteen plaques demonstrated napkin-ring sign with HIR. There was no statistically significant difference in the presentation of positive remodeling, low attenuation plaque, and spotty calcification between the HIR and IMR reconstructed methods (all p > 0.5); however, there was a statistically significant difference in the ability to discern the napkin-ring sign between the two algorithms (χ² = 12.12, p < 0.001). The image noise of IMR was lower than that of HIR (10 ± 2 HU versus 12 ± 2 HU; p < 0.01), and the SNR and CNR of the images and the CNR between plaques and surrounding adipose tissues on IMR were better than those on HIR (p < 0.01).

Conclusion

IMR can significantly improve image quality compared with HIR for the demonstration of coronary artery and atherosclerotic plaques using a 256-slice CT.

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.

Impact of CT reconstruction algorithm on auto-segmentation performance

Model‐based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity‐based tasks such as auto‐segmentation. This work evaluates the sensitivity of an auto‐contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP). Images were created from raw projection data for 11 prostate cancer cases using FBP and nine different MBIR reconstructions (3 protocols/3 noise reduction levels) yielding 10 reconstructions/patient. Five bony structures, bladder, rectum, prostate, and seminal vesicles (SVs) were segmented using an auto‐segmentation pipeline that renders 3D binary masks for analysis. Performance was evaluated for volume percent difference (VPD) and Dice similarity coefficient (DSC), using FBP as the gold standard. Nonparametric Friedman tests plus post hoc all pairwise comparisons were employed to test for significant differences (P < 0.05) for soft tissue organs and protocol/level combinations. A physician performed qualitative grading of 396 MBIR contours across the prostate, bladder, SVs, and rectum in comparison to FBP using a six‐point scale. MBIR contours agreed with FBP for bony anatomy (DSC ≥ 0.98), bladder (DSC ≥ 0.94, VPD < 8.5%), and prostate (DSC = 0.94 ± 0.03, VPD = 4.50 ± 4.77% (range: 0.07–26.39%). Increased variability was observed for rectum (VPD = 7.50 ± 7.56% and DSC = 0.90 ± 0.08) and SVs (VPD and DSC of 8.23 ± 9.86% range (0.00–35.80%) and 0.87 ± 0.11, respectively). Over the all protocol/level comparisons, a significant difference was observed for the prostate VPD between BSPL1 and BSTL2 (adjusted P‐value = 0.039). Nevertheless, 300 of 396 (75.8%) of the four soft tissue structures using MBIR were graded as equivalent or better than FBP, suggesting that MBIR offered potential improvements in auto‐segmentation performance when compared to FBP. Future work may involve tuning organ‐specific MBIR parameters to further improve auto‐segmentation performance. Running title: Impact of CT Reconstruction Algorithm on Auto‐segmentation Performance.

Thorax CT Dose Reduction Based on Patient Features: Effect of Patient Characteristics on Image Quality and Effective Dose

Computed tomography (CT) radiation dose reduction is vital without compromising image quality. The aim was to determine the effects of patient characteristics on the received radiation dose and image quality in chest CT examinations and to be able to predict dose and image quality prior to scanning. Consecutive 230 patients underwent routine chest CT examinations were included. CT examination and patients input parameters were recorded for each patient. The effect of patients' demographics/anthropometrics on received dose and image quality was investigated by linear regression analysis. All parameters were evaluated using an artificial neural network (ANN). Of all parameters, patient demographics/anthropometrics were found to be 98% effective in calculating dose reduction. Using ANN on 60 new patients was more than 90% accurate for output parameters and 91% for image quality. Patient characteristics have a significant impact on radiation dose and image quality. Dose and image quality can be determined before CT. This will allow setting the most appropriate scanning parameters before the CT scan.

Matching and Homogenizing Convolution Kernels for Quantitative Studies in Computed Tomography

The sharpness of the kernels used for image reconstruction in computed tomography affects the values of the quantitative image features. We sought to identify the kernels that produce similar feature values to enable a more effective comparison of images produced using scanners from different manufactures. We also investigated a new image filter designed to change the kernel-related component of the frequency spectrum of a postreconstruction image from that of the initial kernel to that of a preferred kernel. A radiomics texture phantom was imaged using scanners from GE, Philips, Siemens, and Toshiba. Images were reconstructed multiple times, varying the kernel from smooth to sharp. The phantom comprised 10 cartridges of various textures. A semiautomated method was used to produce 8 × 2 × 2 cm regions of interest for each cartridge and for all scans. For each region of interest, 38 radiomics features from the categories intensity direct (n = 12), gray-level co-occurrence matrix (n = 21), and neighborhood gray-tone difference matrix (n = 5) were extracted. We then calculated the fractional differences of the features from those of the baseline kernel (GE Standard). To gauge the importance of the differences, we scaled them by the coefficient of variation of the same feature from a cohort of patients with non-small cell lung cancer. The noise power spectra for each kernel were estimated from the phantom's solid acrylic cartridge, and kernel-homogenization filters were developed from these estimates. The Philips C, Siemens B30f, and Toshiba FC24 kernels produced feature values most similar to GE Standard. The kernel homogenization filters reduced the median differences from baseline to less than 1 coefficient of variation in the patient population for all of the GE, Philips, and Siemens kernels except for GE Edge and Toshiba kernels. For prospective computed tomographic radiomics studies, the scanning protocol should specify kernels that have been shown to produce similar feature values. For retrospective studies, kernel homogenization filters can be designed and applied to reduce the kernel-related differences in the feature values.

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

OBJECTIVES

Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).

METHODS

Our retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared.

RESULTS

The image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality.

CONCLUSIONS

DLR improved the quality of abdominal U-HRCT images.

KEY POINTS

• The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. • Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.

Myocardial Late Iodine Enhancement and Extracellular Volume Quantification with Dual-Layer Spectral Detector Dual-Energy Cardiac CT

PURPOSE

To explore the usefulness of myocardial late iodine enhancement (LIE) and extracellular volume (ECV) quantification by using dual-energy cardiac CT.

MATERIALS AND METHODS

In this single-center retrospective study, a total of 40 patients were evaluated with LIE CT by using a dual-layer spectral detector CT system. Among these, 21 also underwent cardiac MRI. Paired image sets were created by using standard imaging at 120 kVp, virtual monochromatic imaging (VMI) at 50 keV, and iodine density imaging. The contrast-to-noise ratio and image quality were then compared. Two observers assessed the presence of LIE and calculated the interobserver agreements. Agreement between CT and cardiac MRI when detecting late-enhancing lesions and calculating the ECV was also assessed.

RESULTS

The contrast-to-noise ratio was significantly higher by using VMI than by using standard 120-kVp imaging, and the mean visual image quality score was significantly higher by using VMI than by using either standard or iodine density imaging. For interobserver agreement of visual detection of LIE, the agreement for VMI was excellent and the κ value (κ, 0.87) was higher than that for the standard 120-kVp (κ, 0.70) and iodine density (κ, 0.83) imaging. For detecting late-enhancing lesions, agreement with cardiac MRI was excellent by using VMI (κ, 0.90) and iodine density imaging (κ, 0.87) but was only good by using standard 120-kVp imaging (κ, 0.66). Quantitative comparisons of the ECV calculations by using CT and cardiac MRI showed excellent correlation (r ² = 0.94).

CONCLUSION

Dual-energy cardiac CT can assess myocardial LIE and quantify ECV, with results comparable to those obtained by using cardiac MRI.© RSNA, 2019See also the commentary by Litt in this issue.

Evaluation of the Impact of Iterative Reconstruction Algorithms on Computed Tomography Texture Features of the Liver Parenchyma Using the Filtration-Histogram Method

OBJECTIVE

To evaluate whether computed tomography (CT) reconstruction algorithms affect the CT texture features of the liver parenchyma.

MATERIALS AND METHODS

This retrospective study comprised 58 patients (normal liver, n = 34; chronic liver disease [CLD], n = 24) who underwent liver CT scans using a single CT scanner. All CT images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR) (iDOSE⁴), and model-based IR (IMR). On arterial phase (AP) and portal venous phase (PVP) CT imaging, quantitative texture analysis of the liver parenchyma using a single-slice region of interest was performed at the level of the hepatic hilum using a filtration-histogram statistic-based method with different filter values. Texture features were compared among the three reconstruction methods and between normal livers and those from CLD patients. Additionally, we evaluated the inter- and intra-observer reliability of the CT texture analysis by calculating intraclass correlation coefficients (ICCs).

RESULTS

IR techniques affect various CT texture features of the liver parenchyma. In particular, model-based IR frequently showed significant differences compared to FBP or hybrid IR on both AP and PVP CT imaging. Significant variation in entropy was observed between the three reconstruction algorithms on PVP imaging (p < 0.05). Comparison between normal livers and those from CLD patients revealed that AP images depend more strongly on the reconstruction method used than PVP images. For both inter- and intra-observer reliability, ICCs were acceptable (> 0.75) for CT imaging without filtration.

CONCLUSION

CT texture features of the liver parenchyma evaluated using the filtration-histogram method were significantly affected by the CT reconstruction algorithm used.

Diagnostic accuracy of low-radiation coronary computed tomography angiography with low tube voltage and knowledge-based model reconstruction

We aimed to evaluate the accuracy of coronary computed tomography angiography (CCTA) with a low-radiation protocol and iterative model reconstruction (IMR), in comparison with invasive coronary angiography (ICA). Sixty-one patients (45 males; mean age, 61.9 ± 9.2 years) with suspected coronary artery disease who underwent CCTA and ICA were retrospectively enrolled. CCTA was performed with low tube voltage (80 or 100 kVp), low tube current (100–200 mAs), prospective ECG triggering, and IMR using a 64-slice computed tomography scanner. Coronary artery disease was defined as luminal narrowing of >50%, as assessed using CCTA and ICA. The sensitivity, specificity, positive (PPV) and negative (NPV) predictive value, and accuracy of CCTA were examined. The mean radiation dose of CCTA was 1.05 ± 0.36 mSv. No non-diagnostic segment was noted. The sensitivity, specificity, PPV, NPV, and accuracy of CCTA were 86.4%, 96.1%, 80.3%, 97.5%, and 94.6% on a per segment basis, 93.1%, 94.7%, 88.3%, 97.0%, and 94.2% on a per vessel basis, and 100%, 83.3%, 93.5%, 100%, and 95.1% on a per patient basis, respectively. In conclusion, a low-radiation CCTA protocol with IMR may be useful for diagnosing coronary artery disease, as it reduces the radiation dose while maintaining diagnostic accuracy.

Predicting image properties in penalized-likelihood reconstructions of flat-panel CBCT

PURPOSE

Model-based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods exhibit data-dependent and shift-variant properties. Image quality predictors have been derived to prospectively estimate local noise and spatial resolution, facilitating both system hardware design and tuning of reconstruction methods. However, current MBIR image quality predictors rely on idealized system models, ignoring physical blurring effects and noise correlations found in real systems. In this work, we develop and validate a new set of predictors using a physical system model specific to flat-panel cone-beam CT (FP-CBCT).

METHODS

Physical models appropriate for integration with MBIR analysis are developed and parameterized to represent nonidealities in FP projection data including focal spot blur, scintillator blur, detector aperture effect, and noise correlations. Flat-panel-specific predictors for local spatial resolution and local noise properties in PL reconstructions are developed based on these realistic physical models. Estimation accuracy of conventional (idealized) and FP-specific predictors is investigated and validated against experimental CBCT measurements using specialized phantoms.

RESULTS

Validation studies show that flat-panel-specific predictors can accurately estimate the local spatial resolution and noise properties, while conventional predictors show significant deviations in the magnitude and scale of the spatial resolution and local noise. The proposed predictors show accurate estimations over a range of imaging conditions including varying x-ray technique and regularization strength. The conventional spatial resolution prediction is sharper than ground truth. Using conventional spatial resolution predictor, the full width at half maximum (FWHM) of local point spread function (PSF) is underestimated by 0.2 mm. This mismatch is mostly eliminated in FP-specific prediction. The general shape and amplitude of local noise power spectrum (NPS) FP-specific predictions are consistent with measurement, while the conventional predictions underestimated the noise level by 70%.

CONCLUSION

The proposed image quality predictors permit accurate estimation of local spatial resolution and noise properties for PL reconstruction, accounting for dependencies on the system geometry, x-ray technique, and patient-specific anatomy in real FP-CBCT. Such tools enable prospective analysis of image quality for a range of goals including novel system and acquisition design, adaptive and task-driven imaging, and tuning of MBIR for robust and reliable behavior.

Low contrast material dose coronary computed tomographic angiography using a dual-layer spectral detector system in patients at risk for contrast-induced nephropathy

OBJECTIVE:

To evaluate the effects of virtual monochromatic imaging (VMI) using dual-layer spectral detector CT on the image quality of coronary CT angiography (CCTA) acquired by using a low contrast material (CM) dose.

METHODS:

We used a VMI 50keV protocol with a 50%  CM dose (140  mgI kg^-1) to scan 30 patients with renal insufficiency and a 120 kVp with the standard CM dose (280  mgI kg^-1) to scan 30 controls without renal insufficiency. Quantitative parameters, including CT attenuation, image noise, and contrast-to-noise ratio (CNR), were measured. The visual image quality factors of contrast enhancement, image noise, beam-hardening artefact, vessel sharpness, and overall image quality were scored on a 4-point scale.

RESULTS:

The mean CT attenuation of the ascending aorta was significantly higher for 50 keV VMI than for 120 kVp. Image noise was significantly lower under the 50 keV VMI. CNR and the mean visual score for contrast enhancement were significantly higher for 50 keV VMI. There were no significant differences in the other visual image quality parameters between the two protocols.

CONCLUSION:

Dual-layer spectral detector CT using 50 keV VMI enabled reducing the CM dose by 50 % without CCAT image quality degradation in patients with renal insufficiency.

ADVANCES IN KNOWLEDGE:

The VMI 50 keV protocol using dual-layer spectral detector CT and a CM dose reduced by 50 % (140 mgI kg^-1) can improve the diagnostic image quality of CCTA.

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.

Assessment of arterially hyper-enhancing liver lesions using virtual monoenergetic images from spectral detector CT: phantom and patient experience

PURPOSE

To investigate a benefit from virtual monoenergetic reconstructions (VMIs) for assessment of arterially hyper-enhancing liver lesions in phantom and patients and to compare hybrid-iterative and spectral image reconstructions of conventional images (CI-IR and CI-SR).

METHODS

All imaging was performed on a SDCT (Philips Healthcare, Best, The Netherlands). Images of a non-anthropomorphic phantom with a lesion-mimicking insert (containing iodine in water solution) and arterial-phase images from contrast-enhanced patient examinations were evaluated. VMIs (40-200 keV, 10 keV increment), CI-IR, and CI-SR were reconstructed using different strengths of image denoising. ROIs were placed in lesions, liver/matrix, muscle; signal-to-noise, contrast-to-noise, and lesion-to-liver ratios (SNR, CNR, and LLR) were calculated. Qualitatively, 40, 70, and 110 keV and CI images were assessed by two radiologists on five-point Likert scales regarding overall image quality, lesion assessment, and noise.

RESULTS

In phantoms, SNR was increased threefold by VMI_40keV compared with CI-IR/SR (5.8 ± 1.1 vs. 18.8 ± 2.2, p ≤ 0.001), while no difference was found between CI-IR and CI-SR (p = 1). Denoising was capable of noise reduction by 40%. In total, 20 patients exhibiting 51 liver lesions were assessed. Attenuation was the highest in VMI_40keV, while image noise was comparable to CI-IR resulting in a threefold increase of CNR/LLR (CI-IR 1.3 ± 0.8/4.4 ± 2.0, VMI_40keV: 3.8 ± 2.7/14.2 ± 7.5, p ≤ 0.001). Subjective lesion delineation was the best in VMI_40keV image (p ≤ 0.01), which also provided the lowest perceptible noise and the best overall image quality.

CONCLUSIONS

VMIs improve assessment of arterially hyper-enhancing liver lesions since they increase lesion contrast while maintaining low image noise throughout the entire keV spectrum. These data suggest that to consider VMI screening after arterially hyper-enhancing liver lesions.

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction

PURPOSE

To estimate potential dose reduction in abdominal CT by visually comparing images reconstructed with filtered back projection (FBP) and strengths of 3 and 5 of a specific MBIR.

MATERIAL AND METHODS

A dual-source scanner was used to obtain three data sets each for 50 recruited patients with 30, 70 and 100% tube loads (mean CTDIvol 1.9, 3.4 and 6.2 mGy). Six image criteria were assessed independently by five radiologists. Potential dose reduction was estimated with Visual Grading Regression (VGR).

RESULTS

Comparing 30 and 70% tube load, improved image quality was observed as a significant strong effect of log tube load and reconstruction method with potential dose reduction relative to FBP of 22-47% for MBIR strength 3 (p < 0.001). For MBIR strength 5 no dose reduction was possible for image criteria 1 (liver parenchyma), but dose reduction between 34 and 74% was achieved for other criteria. Interobserver reliability showed agreement of 71-76% (κw 0.201-0.286) and intra-observer reliability of 82-96% (κw 0.525-0.783).

CONCLUSION

MBIR showed improved image quality compared to FBP with positive correlation between MBIR strength and increasing potential dose reduction for all but one image criterion.

KEY POINTS

• MBIR's main advantage is its de-noising properties, which facilitates dose reduction. • MBIR allows for potential dose reduction in relation to FBP. • Visual Grading Regression (VGR) produces direct numerical estimates of potential dose reduction. • MBIR strengths 3 and 5 dose reductions were 22-34 and 34-74%. • MBIR strength 5 demonstrates inferior performance for liver parenchyma.