Model-based iterative reconstruction and adaptive statistical iterative reconstruction techniques in abdominal CT: comparison of image quality in the detection of colorectal liver metastases

Image quality and diagnostic accuracy of reduced-dose computed tomography enterography with model-based iterative reconstruction in pediatric Crohn's disease patients

This study assessed the image quality and diagnostic accuracy in determining disease activity of the terminal ileum of the reduced-dose computed tomography enterography using model-based iterative reconstruction in pediatric patients with Crohn's disease (CD). Eighteen patients were prospectively enrolled and allocated to the standard-dose (SD) and reduced-dose (RD) computed tomography enterography (CTE) groups (n = 9 per group). Image quality, reader confidence in interpreting bowel findings, accuracy in determining active CD in the terminal ileum, and radiation dose were evaluated. Objective image quality did not show intergroup differences, except for image sharpness. Although reader confidence in detecting mural stratification, ulcer, and perienteric fat stranding of the RD-CTE were inferior to SD-CTE, RD-CTE correctly diagnosed active disease in all patients. The mean values of radiation dose metrics (SD-CTE vs. RD-CTE) were 4.3 versus 0.74 mGy, 6.1 versus 1.1 mGy, 211.9 versus 34.5 mGy∙cm, and 4.4 versus 0.7 mSv mGy∙cm for CTDI_vol, size-specific dose estimation, dose-length product, and effective dose, respectively. RD-CTE showed comparable diagnostic accuracy to SD-CTE in determining active disease of the terminal ileum in pediatric CD patients. However, image quality and reader confidence in detecting ulcer and perienteric fat stranding was compromised.

Improvement of Image Quality Using Hybrid Iterative Reconstruction with Noise Power Spectrum Model in Computed Tomography During Hepatic Arteriography

Objectives:

In CT during hepatic arteriography (CTHA), the addition of a noise power spectrum (NPS) model to conventional hybrid iterative reconstruction (HIR) may improve spatial resolution and reduce image noise. This study aims at assessing the image quality provided by HIR with a NPS model at CTHA.

Methods:

This institutional review board-approved retrospective analysis included 26 patients with hepatocellular carcinomas (HCCs) who underwent CTHA. In all acquisitions, images were reconstructed with filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR), and AIDR enhanced (eAIDR) with the NPS model. Four radiologists analyzed the signal-to-noise ratio (SNR) of HCC nodules and its associated feeding arteries. The radiologists used a semiquantitative scale (–3 to +3) to rate the subjective image quality comparing both the FBP and eAIDR images with the AIDR images.

Results:

The feeding arteries’ attenuation was significantly higher in eAIDR compared to AIDR [514.3 ± 121.4 and 448.3 ± 107.3 Hounsfield units (HU), p < 0.05]. The image noise of eAIDR was significantly lower than that of FBP (15.2 ± 2.2 and 28.5 ± 4.8 HU, p < 0.05) and comparable to that of AIDR. The SNR of feeding arteries on eAIDR was significantly higher than on AIDR (34.1 ± 7.9 and 27.4 ± 6.3, p < 0.05). Subjective assessment scores showed that eAIDR provided better visibility of feeding arteries and overall image quality compared to AIDR (p < 0.05). The HCC nodule visibility was not significantly different among the three reconstructions.

Conclusion:

In CTHA, eAIDR improved the visibility of feeding arteries associated with HCC nodules without compromising nodule detection.

Comparison study of image quality at various radiation doses for CT venography using advanced modeled iterative reconstruction

Objective

We compared the image quality according to the radiation dose on computed tomography (CT) venography at 80 kVp using advanced modeled iterative reconstruction for deep vein thrombus and other specific clinical conditions considering standard-, low-, and ultralow-dose CT.

Methods

In this retrospective study, 105 consecutive CT venography examinations were included using a third-generation dual-source scanner in the dual-source mode in tubes A (reference mAs, 210 mAs at 70%) and B (reference mAs, 90 mAs at 30%) at a fixed 80 kVp. Two radiologists independently reviewed each observation of standard- (100% radiation dose), low- (70%), and ultralow-dose (30%) CT. The objective quality of large veins and subjective image quality regarding lower-extremity veins and deep vein thrombus were compared between images according to the dose. In addition, the CT dose index volumes were displayed from the images.

Results

From the patients, 24 presented deep vein thrombus in 69 venous segments of CT examinations. Standard-dose CT provided the lowest image noise at the inferior vena cava and femoral vein compared with low- and ultralow-dose CT (p < 0.001). There were no differences regarding subjective image quality between the images of popliteal and calf veins at the three doses (e.g., 3.8 ± 0.7, right popliteal vein, p = 0.977). The image quality of the 69 deep vein thrombus segments showed equally slightly higher scores in standard- and low-dose CT (4.0 ± 0.2) than in ultralow-dose CT (3.9 ± 0.4). The CT dose index volumes were 4.4 ± 0.6, 3.1 ± 0.4, and 1.3 ± 0.2 mGy for standard-, low-, and ultralow-dose CT, respectively.

Conclusions

Low- and ultralow-dose CT venography at 80 kVp using an advanced model based iterative reconstruction algorithm allows to evaluate deep vein thrombus and perform follow-up examinations while showing an acceptable image quality and reducing the radiation dose.

Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection

Objective:

This study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and filtered back projection (FBP).

Methods:

Datasets from consecutive patients who underwent low-dose liver CT were retrospectively identified. Images were reconstructed using DLR, MBIR, and FBP. Mean image noise and contrast-to-noise ratio (CNR) were calculated, and noise, artifacts, sharpness, and overall image quality were subjectively assessed. Dunnett’s test was used for statistical comparisons.

Results:

Ninety patients (67 ± 12.7 years; 63 males; mean body mass index [BMI], 25.5 kg/m²) were included. The mean noise in the abdominal aorta and hepatic parenchyma of DLR was lower than that in FBP and MBIR (p < .001). For FBP and MBIR, image noise was significantly higher for obese patients than for those with normal BMI. The CNR for the abdominal aorta and hepatic parenchyma was higher for DLR than for FBP and MBIR (p < .001). MBIR images were subjectively rated as superior to FBP images in terms of noise, artifacts, sharpness, and overall quality (p < .001). DLR images were rated as superior to MBIR images in terms of noise (p < .001) and overall quality (p = .03).

Conclusions:

Based on objective and subjective comparisons, the image quality of DLR was found to be superior to that of MBIR and FBP on low-dose abdominal CT. DLR was the only method for which image noise was not higher for obese patients than for those with a normal BMI.

Advances in knowledge:

This study provides previously unavailable information on the properties of DLR systems and their clinical utility.

Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study

Purpose

In an ultrahigh‐resolution CT (U‐HRCT), deep learning‐based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol.

Methods

For the normal‐sized abdominal models, a Catphan 600 was scanned by U‐HRCT with 100%, 50%, and 25% radiation doses. In all acquisitions, DLR was compared to model‐based iterative reconstruction (MBIR), filtered back projection (FBP), and hybrid iterative reconstruction (HIR). For the quantitative assessment, we compared image noise, which was defined as the standard deviation of the CT number, and spatial resolution among all reconstruction algorithms.

Results

Deep learning‐based reconstruction yielded lower image noise than FBP and HIR at each radiation dose. DLR yielded higher image noise than MBIR at the 100% and 50% radiation doses (100%, 50%, DLR: 15.4, 16.9 vs MBIR: 10.2, 15.6 Hounsfield units: HU). However, at the 25% radiation dose, the image noise in DLR was lower than that in MBIR (16.7 vs. 26.6 HU). The spatial frequency at 10% of the modulation transfer function (MTF) in DLR was 1.0 cycles/mm, slightly lower than that in MBIR (1.05 cycles/mm) at the 100% radiation dose. Even when the radiation dose decreased, the spatial frequency at 10% of the MTF of DLR did not change significantly (50% and 25% doses, 0.98 and 0.99 cycles/mm, respectively).

Conclusion

Deep learning‐based reconstruction performs more consistently at decreasing dose in abdominal ultrahigh‐resolution CT compared to all other commercially available reconstruction algorithms evaluated.

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.

Investigation of the efficacy of a data-driven CT artifact correction scheme for sparse and truncated projection data for intracranial hemorrhage diagnosis

Data-driven CT-image reconstruction techniques for truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts have been proposed. These approaches have shown good performance and preservation of image quality metrics. To continue these efforts, we investigated whether these techniques affect the performance of a machine-learning algorithm to identify the presence of intracranial hemorrhage (ICH). Ten-thousand head CT scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one-third truncated and one-third sparse manner. GANs were tasked with correcting the incomplete projection data in two ways. Firstly, in the sinogram domain, where the incomplete sinogram was filled by the GAN and then reconstructed. Secondly, in the reconstruction domain, where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the GAN. Eighty-five hundred images were used for artifact correction network training, and 1500 were withheld for network assessment via an already trained machine-learning algorithm tasked with diagnosis of ICH presence. Fully-sampled reconstructions were compared with the sparse and truncated reconstructions for classification accuracy. Difference in classification accuracy between the fully sampled (83.4%), sparse (82.0%), and truncated (82.3%) reconstructions was minimal, demonstrating that the network diagnosis performance is unaffected by 2/3 reduction of projection data. This work indicates that data-driven reconstructions for a sparse or truncated projection dataset can provide high diagnostic performance for ICH detection at a fraction of the typical radiation dose.

Cervical spinal computed tomography utilizing model-based iterative reconstruction reduces radiation to an equivalent of three cervical X-rays

PURPOSE

To evaluate radiation dose and image quality of cervical spinal computed tomography scanned with low-radiation dose (LD-CT) utilizing model-based iterative reconstruction (MBIR).

METHODS

We retrospectively examined 14 patients (65.5 ± 13.9 years) who underwent both standard-radiation-dose CT (SD-CT) reconstructed with hybrid iterative reconstruction and LD-CT of cervical spine. The radiation dose, objective image quality indicator, which includes signal-to-noise and contrast-to-noise, and subjective image quality score of the anatomical landmarks in the SD-CT and LD-CT were statistically compared. In addition, the measurement errors of the length of C3 vertebrae (height, anteroposterior length, inner and outer pedicle diameters) between SD-CT and LD-CT were analyzed.

RESULTS

Radiation dose of LD-CT was reduced to one-sixth of the dose of SD-CT. The objective image quality indicator of LD-CT was significantly better than that of SD-CT. The subjective image quality of LD-CT was relatively worse than that of SD-CT but generally graded as clinically accepted or higher. There was no remarkable difference between SD-CT and LD-CT in the measurement value of height and anteroposterior length. Inner pedicle diameter was significantly (0.21 ± 0.13 mm) smaller, and outer pedicle diameter was (0.24 ± 0.14 mm) larger on LD-CT than on SD-CT.

CONCLUSION

Cervical spinal LD-CT that utilized MBIR enabled radical decrease in radiation dose and provided sufficient image quality for clinical use. This scanning protocol can be a good alternative for protecting patients from exposure to unnecessary radiation, especially when a patient requires multiple CT scans.

Comparisons of Hounsfield Unit Linearity between Images Reconstructed using an Adaptive Iterative Dose Reduction (AIDR) and a Filter Back-Projection (FBP) Techniques

Background:

The HU linearity is an essential parameter in a quantitative imaging and the treatment planning systems of radiotherapy.

Objective:

This study aims to evaluate the linearity of Hounsfield unit (HU) in applying the adaptive iterative dose reduction (AIDR) on CT scanner and its comparison to the filtered back-projection (FBP).

Material and Methods:

In this experimental phantom study, a TOS-phantom was scanned using a Toshiba Alexion 6 CT scanner. The images were reconstructed using the FBP and AIDR. Measurements of HU and noise values were performed on images of the “HU linearity” module of the TOS-phantom. The module had five embedded objects, i.e., air, polypropylene, nylon, acrylic, and Delrin. On each object, a circle area of 4.32 cm² was drawn and used to measure HU and noise values. The R² of the relation between mass densities vs. HU values was used to measure HU linearities at four different tube voltages. The Mann-Whitney U test was used to compare unpaired data and p-value < 0.05 was considered statistically significant.

Results:

The AIDR method produced a significant smaller image noise than the FBP method (p-value < 0.05). There were no significant differences in HU values of images reconstructed using FBP and AIDR methods (p-value > 0.05). The HU values acquired by the methods showed the same linearity marked by coinciding linear lines with the same R² value (> 0.999).

Conclusion:

AIDR methods produce the HU linearity as FBP methods with a smaller image noise level.

Feasibility of thin-slice abdominal CT in overweight patients using a vendor neutral image-based denoising algorithm: Assessment of image noise, contrast, and quality

The purpose of this study was to investigate whether the novel image-based noise reduction software (NRS) improves image quality, and to assess the feasibility of using this software in combination with hybrid iterative reconstruction (IR) in image quality on thin-slice abdominal CT. In this retrospective study, 54 patients who underwent dynamic liver CT between April and July 2017 and had a body mass index higher than 25 kg/m² were included. Three image sets of each patient were reconstructed as follows: hybrid IR images with 1-mm slice thickness (group A), hybrid IR images with 5-mm slice thickness (group B), and hybrid IR images with 1-mm slice thickness denoised using NRS (group C). The mean image noise and contrast-to-noise ratio relative to the muscle of the aorta and liver were assessed. Subjective image quality was evaluated by two radiologists for sharpness, noise, contrast, and overall quality using 5-point scales. The mean image noise was significantly lower in group C than in group A (p < 0.01), but no significant difference was observed between groups B and C. The contrast-to-noise ratio was significantly higher in group C than in group A (p < 0.01 and p = 0.01, respectively). Subjective image quality was also significantly higher in group C than in group A (p < 0.01), in terms of noise and overall quality, but not in terms of sharpness and contrast (p = 0.65 and 0.07, respectively). The contrast of images in group C was greater than that in group A, but this difference was not significant. Compared with hybrid IR alone, the novel NRS combined with a hybrid IR could result in significant noise reduction without sacrificing image quality on CT. This combined approach will likely be particularly useful for thin-slice abdominal CT examinations of overweight patients.

[Sparse-view CT image restoration via multiscale wavelet residual network]

OBJECTIVE

Sparse-view CT has the advantages of accelerated data collection and reduced radiation dose, but data missing arising from the data collection process causes serious streaking artifact and noise in the images reconstructed using the traditional filtering back projection algorithm (FBP). To solve this problem, we propose a multi-scale wavelet residual network (MWResNet) to restore sparse-view CT images.

METHODS

The MWResNet was based on the combination of deep learning and traditional model in MWCNN, and the wavelet network was combined with the residual block to enhance the network's ability to embed image features and speed up network training. The network proposed herein was trained using the real spiral geometry CT image data, namely the Low-dose CT Grand Challenge dataset. The results of the proposed networks were visually and quantitatively compared to that by other existing networks, including the image restoration iterative residual convolution network (IRLNet), residual coding-decoding convolutional neural network (REDCNN) and the FBP convolutional neural network (FBPConvNet).

RESULTS

The results demonstrated that the proposed method was superior to other competing methods in terms of visual inspection and quantitative comparison.

CONCLUSIONS

The MWResNet network is an effective method for suppressing noise and artifacts and maintaining edges details in the sparse-view CT images.

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.

Iterative CT reconstruction in abdominal low-dose CT used for hybrid SPECT-CT applications: effect on image quality, image noise, detectability, and reader's confidence

Background

Iterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT).

Purpose

To examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment.

Material and Methods

The LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures.

Results

The image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003).

Conclusion

An ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

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.

Iterative algorithms for metal artifact reduction in children with orthopedic prostheses: preliminary results

BACKGROUND

Increased computational power allows computed tomography (CT) software to process very advanced mathematical algorithms to generate better quality images at lower doses. One such algorithm, iterative metal artifact reduction (iMAR) has proven to decrease metal artifacts seen in CT images of adults with orthopedic implants.

OBJECTIVES

To evaluate artifact reduction capability of the algorithm in lower-dose pediatric CT compared to our routine third-generation advanced modeled iterative reconstruction (ADMIRE) algorithm.

MATERIALS AND METHODS

Thirteen children (11-17 years old) with metal implants underwent routine clinically indicated CT. Data sets were reconstructed with an iMAR algorithm. Hounsfield units and image noise were measured in bone, muscle and fat in the streak artifact (near the implant) and at the greatest distance from the artifact (far from the implant). A regression model compared the effects of the algorithm (standard ADMIRE vs. iMAR) near and far from the implant.

RESULTS

Near the implant, Hounsfield units with iMAR were significantly different in our standard ADMIRE vs. iMAR for bone, muscle and fat (P<0.001). Noise was significantly different in standard ADMIRE vs. iMAR in bone (P<0.003). Far from the implant, Hounsfield units and noise were not significantly different for ADMIRE vs. iMAR, for the three tissue types.

CONCLUSION

These preliminary results demonstrate that iMAR algorithms improves Hounsfield units near the implant and decreases image noise in bone in low-dose pediatric CT. It does this without changing baseline tissue density or noise far from the implant.

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

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

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.

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

OBJECTIVE

To qualitatively and quantitatively compare abdominal CT images reconstructed with a newversion of model-based iterative reconstruction (Veo 3.0; GE Healthcare Waukesha, WI) utilizing varied presetsof resolution preference, noise reduction and slice optimization.

METHODS

This retrospective study was approved by our Institutional Review Board and was Health Insurance Portability and Accountability Act compliant. The raw datafrom 30 consecutive patients who had undergone CT abdomen scanning were used to reconstructfour clinical presets of 3.75mm axial images using Veo 3.0: 5% resolution preference (RP05n), 5%noise reduction (NR05) and 40% noise reduction (NR40) with new 3.75mm "sliceoptimization," as well as one set using RP05 with conventional 0.625mm "slice optimization" (RP05c). The images were reviewed by two independent readers in a blinded, randomized manner using a 5-point Likert scale as well as a 5-point comparative scale. Multiple two-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp cm^-1 bar pattern of the Catphan 600 phantom for evaluation of spatial resolution.

RESULTS

The NR05 image set was ranked as the best series in overall image quality (mean difference inrank 0.48, 95% CI [0.081-0.88], p = 0.01) and with specific reference to liver evaluation (meandifference 0.46, 95% CI [0.030-0.89], p = 0.03), when compared with the secondbest series ineach category. RP05n was ranked as the best for bone evaluation. NR40 was ranked assignificantly inferior across all assessed categories. Although the NR05 and RP05c image setshad nearly the same contrast-to-noise ratio and spatial resolution, NR05 was generally preferred. Image noise and spatial resolution increased along a spectrum with RP05n the highest and NR40the lowest. Compared to RP05n, the average noise was 21.01% lower for NR05, 26.88%lower for RP05c and 50.86% lower for NR40.

CONCLUSION

Veo 3.0 clinical presets allow for selection of image noise and spatial resolution balance; for contrast-enhanced CT evaluation of the abdomen, the 5% noise reduction preset with 3.75 mm slice optimization (NR05) was generally ranked superior qualitatively and, relative to other series, was in the middle of the spectrum with reference to image noise and spatial resolution. Advances in knowledge: To our knowledge, this is the first study of Veo 3.0 noise reduction presets and varied slice optimization. This study provides insight into the behaviour of slice optimization and documents the degree of noise reduction and spatial resolution changes that users can expect across various Veo 3.0 clinical presets. These results provide important parameters to guide preset selection for both clinical and research purposes.

Evaluation of Abdominal Computed Tomography Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction

PURPOSE

To qualitatively and quantitatively compare abdominal computed tomography (CT) images reconstructed with a new version of model-based iterative reconstruction (Veo 3.0; GE Healthcare) to those created with Veo 2.0.

MATERIALS AND METHODS

This retrospective study was approved by our institutional review board and was Health Insurance Portability and Accountability Act compliant. The raw data from 29 consecutive patients who had undergone CT abdomen scanning was used to reconstruct 4 sets of 3.75-mm axial images: Veo 2.0, Veo 3.0 standard, Veo 3.0 5% resolution preference (RP), and Veo 3.0 20% RP. A slice thickness optimization of 3.75 mm and texture feature was selected for Veo 3.0 reconstructions.The images were reviewed by 3 independent readers in a blinded, randomized fashion using a 5-point Likert scale and 5-point comparative scale.Multiple 2-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp/cm bar pattern of the CatPhan 600 phantom for spatial resolution evaluation.

RESULTS

The Veo 3.0 standard image set was scored better than Veo 2.0 in terms of artifacts (mean difference, 0.43; 95% confidence interval [95% CI], 0.25-0.6; P < 0.0001), overall image quality (mean difference, 0.87; 95% CI, 0.62-1.13; P < 0.0001) and qualitative resolution (mean difference, 0.9; 95% CI, 0.69-1.1; P < 0.0001). Although the Veo 3.0 standard and RP05 presets were preferred across most categories, the Veo 3.0 RP20 series ranked best for bone detail. Image noise and spatial resolution increased along a spectrum with Veo 2.0 the lowest and RP20 the highest.

CONCLUSION

Veo 3.0 enhances imaging evaluation relative to Veo 2.0; readers preferred Veo 3.0 image appearance despite the associated mild increases in image noise. These results provide suggested parameters to be used clinically and as a basis for future evaluations, such as focal lesion detection, in the oncology setting.

Prospective Evaluation of Reduced Dose Computed Tomography for the Detection of Low-Contrast Liver Lesions: Direct Comparison with Concurrent Standard Dose Imaging

OBJECTIVES

To prospectively compare the diagnostic performance of reduced-dose (RD) contrast-enhanced CT (CECT) with standard-dose (SD) CECT for detection of low-contrast liver lesions.

METHODS

Seventy adults with non-liver primary malignancies underwent abdominal SD-CECT immediately followed by RD-CECT, aggressively targeted at 60-70 % dose reduction. SD series were reconstructed using FBP. RD series were reconstructed with FBP, ASIR, and MBIR (Veo). Three readers-blinded to clinical history and comparison studies-reviewed all series, identifying liver lesions ≥4 mm. Non-blinded review by two experienced abdominal radiologists-assessing SD against available clinical and radiologic information-established the reference standard.

RESULTS

RD-CECT mean effective dose was 2.01 ± 1.36 mSv (median, 1.71), a 64.1 ± 8.8 % reduction. Pooled per-patient performance data were (sensitivity/specificity/PPV/NPV/accuracy) 0.91/0.78/0.60/0.96/0.81 for SD-FBP compared with RD-FBP 0.79/0.75/0.54/0.91/0.76; RD-ASIR 0.84/0.75/0.56/0.93/0.78; and RD-MBIR 0.84/0.68/0.49/0.92/0.72. ROC AUC values were 0.896/0.834/0.858/0.854 for SD-FBP/RD-FBP/RD-ASIR/RD-MBIR, respectively. RD-FBP (P = 0.002) and RD-MBIR (P = 0.032) AUCs were significantly lower than those of SD-FBP; RD-ASIR was not (P = 0.052). Reader confidence was lower for all RD series (P < 0.001) compared with SD-FBP, especially when calling patients entirely negative.

CONCLUSIONS

Aggressive CT dose reduction resulted in inferior diagnostic performance and reader confidence for detection of low-contrast liver lesions compared to SD. Relative to RD-ASIR, RD-FBP showed decreased sensitivity and RD-MBIR showed decreased specificity.

KEY POINTS

• Reduced-dose CECT demonstrates inferior diagnostic performance for detecting low-contrast liver lesions. • Reader confidence is lower with reduced-dose CECT compared to standard-dose CECT. • Overly aggressive dose reduction may result in misdiagnosis, regardless of reconstruction algorithm. • Careful consideration of perceived risks versus benefits of dose reduction is crucial.

Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography

The purpose of this study was to compare the image noise-reducing abilities of iterative model reconstruction (IMR) with those of traditional filtered back projection (FBP) and statistical iterative reconstruction (IR) in abdominal computed tomography (CT) imagesThis institutional review board-approved retrospective study enrolled 103 patients; informed consent was waived. Urinary bladder (n = 83) and renal cysts (n = 44) were used as targets for evaluating imaging quality. Raw data were retrospectively reconstructed using FBP, statistical IR, and IMR. Objective image noise and signal-to-noise ratio (SNR) were calculated and analyzed using one-way analysis of variance. Subjective image quality was evaluated and analyzed using Wilcoxon signed-rank test with Bonferroni correction.Objective analysis revealed a reduction in image noise for statistical IR compared with that for FBP, with no significant differences in SNR. In the urinary bladder group, IMR achieved up to 53.7% noise reduction, demonstrating a superior performance to that of statistical IR. IMR also yielded a significantly superior SNR to that of statistical IR. Similar results were obtained in the cyst group. Subjective analysis revealed reduced image noise for IMR, without inferior margin delineation or diagnostic confidence.IMR reduced noise and increased SNR to greater degrees than did FBP and statistical IR. Applying the IMR technique to abdominal CT imaging has potential for reducing the radiation dose without sacrificing imaging quality.

Using 80 kVp on a 320-row scanner for hepatic multiphasic CT reduces the contrast dose by 50 % in patients at risk for contrast-induced nephropathy

OBJECTIVES

We evaluated the effects of a low contrast material (CM) dose protocol using 80-kVp on the image quality of hepatic multiphasic CT scans acquired on a 320-row CT scanner.

METHODS

We scanned 30 patients with renal insufficiency (eGFR < 45 mL/min/1.73 m²) using 80-kVp and a CM dose of 300mgI/kg. Another 30 patients without renal insufficiency (eGFR > 60 mL/min/1.73 m²) were scanned with the conventional 120-kVp protocol and the standard CM dose of 600mgI/kg. Quantitative image quality parameters, i.e. CT attenuation, image noise, and the contrast-to-noise ratio (CNR) were compared and the visual image quality was scored on a four-point scale. The volume CT dose index (CTDI_vol) and the size-specific dose estimate (SSDE) recorded with the 80- and the 120-kVp protocols were also compared.

RESULTS

Image noise and contrast enhancement were equivalent for the two protocols. There was no significant difference in the CNR of all anatomic sites and in the visual scores for overall image quality. The CTDI_vol and SSDE were approximately 25-30 % lower under the 80-kVp protocol.

CONCLUSION

Hepatic multiphase CT using 80-kVp on a 320-row CT scanner allowed for a decrease in the CM dose and a reduction in the radiation dose without image quality degradation in patients with renal insufficiency.

KEY POINTS

• The 80-kVp CT protocol enabled reduction of contrast dose by 50 % • The 80-kVp CT protocol reduced the radiation dose by 25-33 % • There was no degradation in the image quality of the 80-kVp protocol.

Automatic spectral imaging protocol selection and iterative reconstruction in abdominal CT with reduced contrast agent dose: initial experience

OBJECTIVE

To evaluate the feasibility, image quality, and radiation dose of automatic spectral imaging protocol selection (ASIS) and adaptive statistical iterative reconstruction (ASIR) with reduced contrast agent dose in abdominal multiphase CT.

METHODS

One hundred and sixty patients were randomly divided into two scan protocols (n = 80 each; protocol A, 120 kVp/450 mgI/kg, filtered back projection algorithm (FBP); protocol B, spectral CT imaging with ASIS and 40 to 70 keV monochromatic images generated per 300 mgI/kg, ASIR algorithm. Quantitative parameters (image noise and contrast-to-noise ratios [CNRs]) and qualitative visual parameters (image noise, small structures, organ enhancement, and overall image quality) were compared.

RESULTS

Monochromatic images at 50 keV and 60 keV provided similar or lower image noise, but higher contrast and overall image quality as compared with 120-kVp images. Despite the higher image noise, 40-keV images showed similar overall image quality compared to 120-kVp images. Radiation dose did not differ between the two protocols, while contrast agent dose in protocol B was reduced by 33 %.

CONCLUSION

Application of ASIR and ASIS to monochromatic imaging from 40 to 60 keV allowed contrast agent dose reduction with adequate image quality and without increasing radiation dose compared to 120 kVp with FBP.

KEY POINTS

• Automatic spectral imaging protocol selection provides appropriate scan protocols. • Abdominal CT is feasible using spectral imaging and 300 mgI/kg contrast agent. • 50-keV monochromatic images with 50 % ASIR provide optimal image quality.

Comparison of the image qualities of filtered back-projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction for CT venography at 80 kVp

PURPOSE

To evaluate the subjective and objective qualities of computed tomography (CT) venography images at 80 kVp using model-based iterative reconstruction (MBIR) and to compare these with those of filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) using the same CT data sets.

MATERIALS AND METHODS

Forty-four patients (mean age: 56.1 ± 18.1) who underwent 80 kVp CT venography (CTV) for the evaluation of deep vein thrombosis (DVT) during 4 months were enrolled in this retrospective study. The same raw data were reconstructed using FBP, ASIR, and MBIR. Objective and subjective image analysis were performed at the inferior vena cava (IVC), femoral vein, and popliteal vein.

RESULTS

The mean CNR of MBIR was significantly greater than those of FBP and ASIR and images reconstructed using MBIR had significantly lower objective image noise (p < .001). Subjective image quality and confidence of detecting DVT by MBIR group were significantly greater than those of FBP and ASIR (p < .005), and MBIR had the lowest score for subjective image noise (p < .001).

CONCLUSION

CTV at 80 kVp with MBIR was superior to FBP and ASIR regarding subjective and objective image qualities.

KEY POINTS

• MBIR provides superior image quality compared with FBP and ASIR • CTV at 80kVp with MBIR improves diagnostic confidence in diagnosing DVT • CTV at 80kVp with MBIR presents better image quality with low radiation.

Optimization of SPECT-CT Hybrid Imaging Using Iterative Image Reconstruction for Low-Dose CT: A Phantom Study

Background

Hybrid imaging combines nuclear medicine imaging such as single photon emission computed tomography (SPECT) or positron emission tomography (PET) with computed tomography (CT). Through this hybrid design, scanned patients accumulate radiation exposure from both applications. Imaging modalities have been the subject of long-term optimization efforts, focusing on diagnostic applications. It was the aim of this study to investigate the influence of an iterative CT image reconstruction algorithm (ASIR) on the image quality of the low-dose CT images.

Methodology/Principal Findings

Examinations were performed with a SPECT-CT scanner with standardized CT and SPECT-phantom geometries and CT protocols with systematically reduced X-ray tube currents. Analyses included image quality with respect to photon flux. Results were compared to the standard FBP reconstructed images. The general impact of the CT-based attenuation maps used during SPECT reconstruction was examined for two SPECT phantoms. Using ASIR for image reconstructions, image noise was reduced compared to FBP reconstructions for the same X-ray tube current. The Hounsfield unit (HU) values reconstructed by ASIR were correlated to the FBP HU values(R² ≥ 0.88) and the contrast-to-noise ratio (CNR) was improved by ASIR. However, for a phantom with increased attenuation, the HU values shifted for low X-ray tube currents I ≤ 60 mA (p ≤ 0.04). In addition, the shift of the HU values was observed within the attenuation corrected SPECT images for very low X-ray tube currents (I ≤ 20 mA, p ≤ 0.001).

Conclusion/Significance

In general, the decrease in X-ray tube current up to 30 mA in combination with ASIR led to a reduction of CT-related radiation exposure without a significant decrease in image quality.

Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms

Dose reduction in children undergoing CT scanning is an important priority for the radiology community and public at large. Drawbacks of radiation reduction are increased image noise and artifacts, which can affect image interpretation. Iterative reconstruction techniques have been developed to reduce noise and artifacts from reduced-dose CT examinations, although reconstruction algorithm, magnitude of dose reduction and effects on image quality vary. We review the reconstruction principles, radiation dose potential and effects on image quality of several iterative reconstruction techniques commonly used in clinical settings, including 3-D adaptive iterative dose reduction (AIDR-3D), adaptive statistical iterative reconstruction (ASIR), iDose, sinogram-affirmed iterative reconstruction (SAFIRE) and model-based iterative reconstruction (MBIR). We also discuss clinical applications of iterative reconstruction techniques in pediatric abdominal CT.

Impact of model-based iterative reconstruction on image quality of contrast-enhanced neck CT

BACKGROUND AND PURPOSE

Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck.

MATERIALS AND METHODS

Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed retrospectively by using 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction. Objective image quality was assessed by comparing SNR, contrast-to-noise ratio, and background noise at levels 1 (mandible) and 2 (superior mediastinum). Two independent blinded readers subjectively graded the image quality on a scale of 1-5, (grade 5 = excellent image quality without artifacts and grade 1 = nondiagnostic image quality with significant artifacts). The percentage of agreement and disagreement between the 2 readers was assessed.

RESULTS

Compared with 30% adaptive statistical iterative reconstruction, model-based iterative reconstruction significantly improved the SNR and contrast-to-noise ratio at levels 1 and 2. Model-based iterative reconstruction also decreased background noise at level 1 (P = .016), though there was no difference at level 2 (P = .61). Model-based iterative reconstruction was scored higher than 30% adaptive statistical iterative reconstruction by both reviewers at the nasopharynx (P < .001) and oropharynx (P < .001) and for overall image quality (P < .001) and was scored lower at the vocal cords (P < .001) and sternoclavicular junction (P < .001), due to artifacts related to thyroid shielding that were specific for model-based iterative reconstruction.

CONCLUSIONS

Model-based iterative reconstruction offers improved subjective and objective image quality as evidenced by a higher SNR and contrast-to-noise ratio and lower background noise within the same dataset for contrast-enhanced neck CT. Model-based iterative reconstruction has the potential to reduce the radiation dose while maintaining the image quality, with a minor downside being prominent artifacts related to thyroid shield use on model-based iterative reconstruction.

Prospective trial of the detection of urolithiasis on ultralow dose (sub mSv) noncontrast computerized tomography: direct comparison against routine low dose reference standard

PURPOSE

In this prospective trial we compared ultralow dose computerized tomography reconstruction algorithms and routine low dose computerized tomography for detecting urolithiasis.

MATERIALS AND METHODS

A total of 48 consenting adults prospectively underwent routine low dose noncontrast computerized tomography immediately followed by an ultralow dose series targeted at a 70% to 90% reduction from the routine low dose technique (sub mSv range). Ultralow dose series were reconstructed with filtered back projection, and adaptive statistical and model based iterative reconstruction techniques. Transverse (axial) and coronal images were sequentially reviewed by 3 relatively inexperienced trainees, including a radiology resident, a urology fellow and an abdominal imaging fellow. Three experienced abdominal radiologists independently reviewed the routine low dose filtered back projection images, which served as the reference standard.

RESULTS

The mean effective dose for the ultralow dose scans was 0.91 mSv (median 0.82), representing a mean ± SD 78% ± 5% decrease compared to the routine low dose. Overall sensitivity and positive predictive value per stone for ultralow dose computerized tomography at a 4 mm threshold was 0.91 and 0.98, respectively. Sensitivity, specificity, positive and negative predictive values, and accuracy per patient were 0.87, 1.00, 1.00, 0.94 and 0.96, respectively. At a 4 mm threshold the sensitivity and positive predictive value per stone of the ultralow dose series for filtered back projection, and adaptive statistical and model based iterative reconstruction was 0.89 and 0.96, 0.91 and 0.98, and 0.93 and 1.00, respectively. Sensitivity, specificity, positive and negative predictive values, and accuracy per patient at the 4 mm threshold were 0.82, 1.00, 1.00, 0.91 and 0.94 for filtered back projection, 0.85, 1.00, 1.00, 0.93 and 0.95 for adaptive statistical iterative reconstruction, and 0.94, 1.00, 1.00, 0.97 and 0.98 for model based iterative reconstruction, respectively. Sequential review of coronal images changed the final stone reading in 13% of cases and improved diagnostic confidence in 49%.

CONCLUSIONS

At a 4 mm renal calculus size threshold ultralow dose computerized tomography is accurate for detection when referenced against routine low dose series with dose reduction to below the level of a typical 2-view plain x-ray of the kidneys, ureters and bladder. Slight differences were seen among the reconstruction algorithms. There was mild improvement with model based iterative reconstruction over filtered back projection and adaptive statistical iterative reconstruction. Coronal images improved detection and diagnostic confidence over axial images alone.