Improvement of Low-Contrast Detectability in Low-Dose Hepatic Multidetector Computed Tomography Using a Novel Adaptive Filter

Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction

OBJECTIVES

To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstruction (cDLR) algorithms.

METHODS

We retrospectively analyzed abdominal CT scans performed using a low-dose protocol. Three different image reconstruction algorithms-HIR, cDLR, and SR-DLR-were applied to the same raw image data. Objective evaluations included noise magnitude and contrast-to-noise ratio (CNR), as well as noise power spectrum (NPS) and edge rise slope (ERS). Subjective evaluations were performed by radiologists, who assessed image quality in terms of noise, artifacts, sharpness, and overall diagnostic utility.

RESULTS

Raw CT image data were obtained from 35 patients (mean CTDIvol 11.0 mGy; mean DLP 344.8 mGy/cm). cDLR yielded the lowest noise levels and highest CNR (p < 0.001). However, SR-DLR outperformed cDLR in terms of noise texture and resolution, achieving the lowest NPS peak and highest ERS (p < 0.001 and p = 0.005, respectively). Subjectively, SR-DLR was rated highest across all categories, including noise, artifacts, sharpness, and overall image quality, with statistically significant differences compared to cDLR and HIR (p < 0.001).

CONCLUSION

SR-DLR was the most effective reconstruction algorithm for low-dose abdominal CT imaging, offering superior image quality and noise reduction compared to cDLR and HIR. This suggests that SR-DLR can enhance the reliability and diagnostic accuracy of abdominal imaging, particularly in low-dose settings, making it a valuable tool in clinical practice.

Single-Energy, Dual-Energy, and Photon-Counting Computed Tomography of the Liver: Current Development and Clinical Utility for the Assessment of Focal Liver Lesions

ABSTRACT

Advancements in computed tomography (CT) technology, particularly the emergence of dual-energy CT (DE-CT) and photon-counting detector CT (PCD-CT), can improve detection, characterization, and treatment monitoring of focal liver lesions. DE-CT, through its ability to differentiate tissues with similar densities and produce diverse datasets, has enhanced lesion visibility and diagnostic precision. PCD-CT further advances imaging with superior spatial resolution and material decomposition capabilities, offering potential for complex diagnostic scenarios. This review aimed to highlight the role of CT in hepatic imaging and its application to focal liver lesions.DE-CT improves lesion detectability using low-energy virtual monochromatic images, which enhance iodine contrast and reduce radiation and contrast agent doses. It also facilitates treatment response evaluation after locoregional therapies for hepatocellular carcinoma by quantifying biomarkers, such as the extracellular volume fraction. This review underscores the transformative impact of DE-CT and PCD-CT on liver imaging, emphasizing their complementary roles alongside magnetic resonance imaging. These innovations have paved the way for more precise diagnostics, improved treatment planning, and enhanced patient outcomes in the management of liver diseases.

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

Background

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

Methods

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

Results

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

Conclusions

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

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

BACKGROUND

CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality.

OBJECTIVE

To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V).

METHODS

This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR).

RESULTS

Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001).

CONCLUSION

DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V.

CLINICAL IMPACT

Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.

An introduction to photon-counting detector CT (PCD CT) for radiologists

The basic performance of photon-counting detector computed tomography (PCD CT) is superior to conventional CT (energy-integrating detector CT: EID CT) because its spatial- and contrast resolution of soft tissues is higher, and artifacts are reduced. Because the X-ray photon energy separation is better with PCD CT than conventional EID-based dual-energy CT, it has the potential to improve virtual monochromatic- and virtual non-contrast images, material decomposition including quantification of the iodine distribution, and K-edge imaging. Therefore, its clinical applicability may be increased. Although the image quality of PCD CT scans is superior to that of EID CT currently, further improvement may be possible. The introduction of iterative image reconstruction and reconstruction with deep convolutional neural networks will be useful.

Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT

We aimed to compare the radiation dose and image quality of a low-dose abdominal computed tomography (CT) protocol reconstructed with deep learning reconstruction (DLR) with those of a routine-dose protocol reconstructed with hybrid-iterative reconstruction. This retrospective study enrolled 71 patients [61 men; average age, 71.9 years; mean body mass index (BMI), 24.3 kg/m2] who underwent both low-dose abdominal CT with DLR [advanced intelligent clear-IQ engine (AiCE)] and routine-dose abdominal CT with hybrid-iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Radiation dose parameters included volume CT dose index (CTDIvol), effective dose (ED), and size-specific dose estimate (SSDE). Mean image noise and contrast-to-noise ratio (CNR) were calculated. Image noise was measured in the hepatic parenchyma and bilateral erector spinae muscles. Moreover, subjective assessment of perceived image quality and diagnostic acceptability was performed. The low-dose protocol helped reduce the CTDIvol by 44.3%, ED by 43.7%, and SSDE by 44.9%. Moreover, the noise was significantly lower and CNR significantly higher with the low-dose protocol than with the normal-dose protocol (P<0.001). In the subjective assessment of image quality, there was no significant difference between the protocols with regard to image noise. Overall, AiCE was superior to AIDR 3D in terms of diagnostic acceptability (P=0.001). The use of AiCE can reduce overall radiation dose by more than 40% without loss of image quality compared to routine-dose abdominal CT with AIDR 3D.

Does Contrast Dose Based in Lean body Weight Allow Lesser Volumes on High BMI Patients for CT Angiography?

Objectives:

The objective was to evaluate whether contrast dose based on lean body weight (LBW) protocol has the potential to reduce contrast volume in patients with high basal metabolic index (BMI) compared to total body weight (TBW)-based protocols.

Material and Methods:

The Institutional Review Board approval was obtained for this prospective study. Initially, a pilot study with a sample size of 150 patients was conducted to estimate the average fat fraction in our population. Then, CT angiography (CTA) for the thoracic and abdominal aorta was performed using a 256-multidetector computed tomography scanner in 117 patients who were undergoing screening for aortic aneurysm and vascular assessment of prospective transplant donors. The patients were divided into two groups: A TBW group (n = 60) and LBW group (n = 57). Lean body weight (LBW) was estimated from the patient weight, height, and gender using Hume’s equation. The TBW group received 1.2 ml/kg contrast dose and the LBW group received 1.6 ml/kg contrast dose to achieve approximately equal iodine dose in both groups. Differences in the degree of aortic enhancement between the estimated LBW and TBW group were evaluated. In higher BMI patients (>25), the mean aortic enhancement (MAEnh) and the contrast volume delivered between the LBW and TBW group were compared.

Results:

Mean aortic enhancement (MAEnh) 422.45 (±74.5) Hounsfield unit (HU) in the TBW group and 432.67 (±69.4) HU in the LBW group showed no statistical difference (P = 0.439). In population with BMI >25, the contrast delivered in LBW protocol patients was significantly less (P = 0.00) compared to TBW protocol patients, with no significant difference in the MAEnh between the groups (P = 0.479).

Conclusion:

CTA using a LBW protocol helps to significantly reduce the volume of contrast delivered, especially in patients with BMI >25 compared to TBW protocol, without compromising the aortic enhancement.

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.

Deep learning reconstruction of equilibrium phase CT images in obese patients

PURPOSE

To compare abdominal equilibrium phase (EP) CT images of obese and non-obese patients to identify the reconstruction method that preserves the diagnostic value of images obtained in obese patients.

METHODS

We compared EP images of 50 obese patients whose body mass index (BMI) exceeded 25 (group 1) with EP images of 50 non-obese patients (BMI < 25, group 2). Group 1 images were subjected to deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), and model-based IR (MBIR), group 2 images to hybrid-IR; group 2 hybrid-IR images served as the reference standard. A 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 confidence scale ranging from 1 (unacceptable) to 5 (excellent). Non-inferiority and potential superiority were assessed.

RESULTS

With respect to the image noise, group 1 DLR- were superior to group 2 hybrid-IR images; group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images. The quality scores of only DLR images in group 1 were superior to hybrid-IR images of group 2 while the quality scores of group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images.

CONCLUSIONS

DLR preserved the quality of EP images obtained in obese patients.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering

Reducing patient radiation dose, while maintaining a high-quality image, is a major challenge in Computed Tomography (CT). The purpose of this work is to improve abdomen tumor low-dose CT (LDCT) image quality by using a two-step strategy: a first patch-wise non linear processing is first applied to suppress the noise and artifacts, that is based on a sparsity prior in term of a learned dictionary, then an unsharp filtering aiming to enhance the contrast of tissues and compensate the contrast loss caused by the DL processing. Preliminary results show that the proposed method is effective in suppressing mottled noise as well as improving tumor detectability.

CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction

Background

The recently developed model-based iterative reconstruction (MBIR) enables significant reduction of image noise and artifacts, compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). The purpose of this study was to evaluate lesion detectability of low-dose chest computed tomography (CT) with MBIR in comparison with ASIR and FBP.

Methods

Chest CT was acquired with 64-slice CT (Discovery CT750HD) with standard-dose (5.7 ± 2.3 mSv) and low-dose (1.6 ± 0.8 mSv) conditions in 55 patients (aged 72 ± 7 years) who were suspected of lung disease on chest radiograms. Low-dose CT images were reconstructed with MBIR, ASIR 50% and FBP, and standard-dose CT images were reconstructed with FBP, using a reconstructed slice thickness of 0.625 mm. Two observers evaluated the image quality of abnormal lung and mediastinal structures on a 5-point scale (Score 5 = excellent and score 1 = non-diagnostic). The objective image noise was also measured as the standard deviation of CT intensity in the descending aorta.

Results

The image quality score of enlarged mediastinal lymph nodes on low-dose MBIR CT (4.7 ± 0.5) was significantly improved in comparison with low-dose FBP and ASIR CT (3.0 ± 0.5, p = 0.004; 4.0 ± 0.5, p = 0.02, respectively), and was nearly identical to the score of standard-dose FBP image (4.8 ± 0.4, p = 0.66). Concerning decreased lung attenuation (bulla, emphysema, or cyst), the image quality score on low-dose MBIR CT (4.9 ± 0.2) was slightly better compared to low-dose FBP and ASIR CT (4.5 ± 0.6, p = 0.01; 4.6 ± 0.5, p = 0.01, respectively). There were no significant differences in image quality scores of visualization of consolidation or mass, ground-glass attenuation, or reticular opacity among low- and standard-dose CT series. Image noise with low-dose MBIR CT (11.6 ± 1.0 Hounsfield units (HU)) were significantly lower than with low-dose ASIR (21.1 ± 2.6 HU, p < 0.0005), low-dose FBP CT (30.9 ± 3.9 HU, p < 0.0005), and standard-dose FBP CT (16.6 ± 2.3 HU, p < 0.0005).

Conclusion

MBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT without compromising image quality. With radiation dose reduction of >70%, MBIR can provide equivalent lesion detectability of standard-dose FBP CT.

Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing

In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.

Iterative reconstruction algorithm for CT: can radiation dose be decreased while low-contrast detectability is preserved?

PURPOSE

To compare the low-contrast detectability and image quality of computed tomography (CT) at different radiation dose levels reconstructed with iterative reconstruction (IR) and filtered back projection (FBP).

MATERIALS AND METHODS

A custom liver phantom with 12 simulated hypoattenuating tumors (diameters of 5, 10, 15, and 20 mm; tumor-to-liver contrast values of -10, -20, and -40 HU) was designed. The phantom was scanned with a standard abdominal CT protocol with a volume CT dose index of 21.6 mGy (equivalent 100% dose) and four low-dose protocols (20%, 40%, 60%, and 80% of the standard protocol dose). CT data sets were reconstructed with IR and FBP. Image noise was measured, and the tumors' contrast-to-noise ratios (CNRs) were calculated. Tumor detection was independently assessed by three radiologists who were blinded to the CT technique used. A total of 840 simulated tumors were presented to the radiologists. Statistical analyses included analysis of variance.

RESULTS

IR yielded an image noise reduction of 43.9%-63.9% and a CNR increase of 74.1%-180% compared with FBP at the same dose level (P < .001). The overall sensitivity for tumor detection was 64.7%-85.3% for IR and 66.3%-85.7% for FBP at the 20%-100% doses, respectively. There was no significant difference in the sensitivity for tumor detection between IR and FBP at the same dose level (P = .99). The sensitivity of the protocol at the 20% dose with FBP and IR was significantly lower than that of the protocol at the 100% dose with FBP and IR (P = .019).

CONCLUSION

As the radiation dose at CT decreases, the IR algorithm does not preserve the low-contrast detectability.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13122349/-/DC1.

Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT

OBJECTIVE

To compare the dose-optimisation potential of a smoothing filtered backprojection (FBP) and a hybrid FBP/iterative algorithm to that of a standard FBP algorithm at three slice thicknesses for hepatic lesion detection with multidetector CT.

METHODS

A liver phantom containing a 9.5-mm opacity with a density of 10 HU below background was scanned at 125, 100, 75, 50 and 25 mAs. Data were reconstructed with standard FBP (B), smoothing FBP (A) and hybrid FBP/iterative (iDose(4)) algorithms at 5-, 3- and 1-mm collimation. 10 observers marked opacities using a four-point confidence scale. Jackknife alternative free-response receiver operating characteristic figure of merit (FOM), sensitivity and noise were calculated.

RESULTS

Compared with the 125-mAs/5-mm setting for each algorithm, significant reductions in FOM (p<0.05) and sensitivity (p<0.05) were found for all three algorithms for all exposures at 1-mm thickness and for all slice thicknesses at 25 mAs, with the exception of the 25-mAs/5-mm setting for the B algorithm. Sensitivity was also significantly reduced for all exposures at 3-mm thickness for the A algorithm (p<0.05). Noise for the A and iDose(4) algorithms was approximately 13% and 21% lower, respectively, than for the B algorithm.

CONCLUSION

Superior performance for hepatic lesion detection was not shown with either a smoothing FBP algorithm or a hybrid FBP/iterative algorithm compared with a standard FBP technique, even though noise reduction with thinner slices was demonstrated with the alternative approaches.

ADVANCES IN KNOWLEDGE

Reductions in image noise with non-standard CT algorithms do not necessarily translate to an improvement in low-contrast object detection.

Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT

BACKGROUND

Abdominal computed tomography (CT) is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality.

PURPOSE

To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images.

MATERIAL AND METHODS

Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI (vol)) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models.

RESULTS

All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P < 0.01). Standard dose images had better image quality than reduced dose 3D filtered images (P < 0.01), but similar image noise. For patients with body mass index (BMI) < 30 kg/m(2) however, 3D filtered images were rated significantly better than normal dose images for two image criteria (P < 0.05), while no significant difference was found for the remaining three image criteria (P > 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images.

CONCLUSION

The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI < 30 kg/m(2), 3D filtered images are comparable to standard dose images.

80-kVp CT using Iterative Reconstruction in Image Space algorithm for the detection of hypervascular hepatocellular carcinoma: phantom and initial clinical experience

Objective

To investigate whether the low-tube-voltage (80-kVp), intermediate-tube-current (340-mAs) MDCT using the Iterative Reconstruction in Image Space (IRIS) algorithm improves lesion-to-liver contrast at reduced radiation dosage while maintaining acceptable image noise in the detection of hepatocellular carcinomas (HCC) in thin (mean body mass index, 24 ± 0.4 kg/m²) adults.

Subjects and Methods

A phantom simulating the liver with HCC was scanned at 50-400 mAs for 80, 100, 120 and 140-kVp. In addition, fifty patients with HCC who underwent multiphasic liver CT using dual-energy (80-kVp and 140-kVp) arterial scans were enrolled. Virtual 120-kVP scans (protocol A) and 80-kVp scans (protocol B) of the late arterial phase were reconstructed with filtered back-projection (FBP), while corresponding 80-kVp scans were reconstructed with IRIS (protocol C). Contrast-to-noise ratio (CNR) of HCCs and abdominal organs were assessed quantitatively, whereas lesion conspicuity, image noise, and overall image quality were assessed qualitatively.

Results

IRIS effectively reduced image noise, and yielded 29% higher CNR than the FBP at equivalent tube voltage and current in the phantom study. In the quantitative patient study, protocol C helped improve CNR by 51% and 172% than protocols A and B (p < 0.001), respectively, at equivalent radiation dosage. In the qualitative study, protocol C acquired the highest score for lesion conspicuity albeit with an inferior score to protocol A for overall image quality (p < 0.001). Mean effective dose was 2.63-mSv with protocol A and 1.12-mSv with protocols B and C.

Conclusion

CT using the low-tube-voltage, intermediate-tube-current and IRIS help improve lesion-to-liver CNR of HCC in thin adults during the arterial phase at a lower radiation dose when compared with the standard technique using 120-kVp and FBP.

Radiation dose reduction with application of non-linear adaptive filters for abdominal CT

AIM: To evaluate the effect of non-linear adaptive filters (NLAF) on abdominal computed tomography (CT) images acquired at different radiation dose levels.

METHODS: Nineteen patients (mean age 61.6 ± 7.9 years, M:F = 8:11) gave informed consent for an Institutional Review Board approved prospective study involving acquisition of 4 additional image series (200, 150, 100, 50 mAs and 120 kVp) on a 64 slice multidetector row CT scanner over an identical 10 cm length in the abdomen. The CT images acquired at 150, 100 and 50 mAs were processed with the NLAF. Two radiologists reviewed unprocessed and processed images for image quality in a blinded randomized manner. CT dose index volume, dose length product, patient weight, transverse diameters, objective noise and CT numbers were recorded. Data were analyzed using Analysis of Variance and Wilcoxon signed rank test.

RESULTS: Of the 31 lesions detected in abdominal CT images, 28 lesions were less than 1 cm in size. Subjective image noise was graded as unacceptable in unprocessed images at 50 and 100 mAs, and in NLAF processed images at 50 mAs only. In NLAF processed images, objective image noise was decreased by 21% (14.4 ± 4/18.2 ± 4.9) at 150 mAs, 28.3% (15.7 ± 5.6/21.9 ± 4) at 100 mAs and by 39.4% (18.8 ± 9/30.4 ± 9.2) at 50 mAs compared to unprocessed images acquired at respective radiation dose levels. At 100 mAs the visibility of smaller structures improved from suboptimal in unprocessed images to excellent in NLAF processed images, whereas diagnostic confidence was respectively improved from probably confident to fully confident.

CONCLUSION: NLAF lowers image noise, improves the visibility of small structures and maintains lesion conspicuity at down to 100 mAs for abdominal CT.

CT radiation dose optimization and estimation: an update for radiologists

In keeping with the increasing utilization of CT examinations, the greater concern about radiation hazards from examinations has been addressed. In this regard, CT radiation dose optimization has been given a great deal of attention by radiologists, referring physicians, technologists, and physicists. Dose-saving strategies are continuously evolving in terms of imaging techniques as well as dose management. Consequently, regular updates of this issue are necessary especially for radiologists who play a pivotal role in this activity. This review article will provide an update on how we can optimize CT dose in order to maximize the benefit-to-risk ratio of this clinically useful diagnostic imaging method.

Hepatocellular carcinoma in patients weighing 70 kg or less: initial trial of compact-bolus dynamic CT with low-dose contrast material at 80 kVp

OBJECTIVE

The purpose of this study was to compare the diagnostic capability of hepatic dynamic CT with low-dose contrast material (420 mg I/kg body weight) at 80 kVp with that of the same modality performed with standard-dose contrast material at 120 kVp.

SUBJECTS AND METHODS

We randomly assigned 111 patients (50 women, 61 men; mean age, 69.1 years) with known or suspected hepatocellular carcinoma and a body weight of 70 kg or less to one of two protocols. In the 80-kVp protocol, the contrast material (444 mg I/kg body weight) was delivered over 15 seconds at a tube voltage of 80 kVp. In the 120-kVp protocol, a contrast dose of 600 mg I/kg was delivered over 30 seconds at 120 kVp. Of the 111 patients, 38 had hypervascular hepatocellular carcinoma. Using the Mann-Whitney U test, we compared the two protocols for the contrast-to-noise ratio of the tumors (difference between tumor attenuation and liver attenuation divided by noise in the liver) and the figure of merit (square of contrast-to-noise ratio divided by effective dose) of the tumors during the arterial phase of imaging. Effective doses also were compared.

RESULTS

The contrast-to-noise ratio of the tumors was significantly higher with the 80-kVp than with the 120-kVp protocol (median, 5.3 vs 4.2; p = 0.04). The figure of merit also was significantly higher with the 80-kVp than with the 120-kVp protocol (10.2 vs 5.3, p = 0.02). The effective dose was significantly lower with the 80-kVp than with the 120-kVp protocol (2.97 vs 3.41 mSv, p < 0.01).

CONCLUSION

With 80-kVp acquisition, the contrast-to-noise ratio and figure of merit of tumors during the arterial phase improved despite the lower contrast dose and radiation exposure.

Decreased detection of hypovascular liver tumors with MDCT in obese patients: a phantom study

OBJECTIVE

The purpose of this article is to assess the impact of large patient size on the detection of hypovascular liver tumors with MDCT and the effect of a noise filter on image quality and lesion detection in obese patients.

MATERIALS AND METHODS

A liver phantom with 45 hypovascular tumors (diameters of 5, 10, and 15 mm) was placed into two water containers mimicking intermediate and large patients. The containers were scanned with a 64-MDCT scanner. The CT dataset from the large phantom was postprocessed using a noise filter. The image noise was measured and the contrast-to-noise ratio (CNR) of the tumors was calculated. Tumor detection was independently performed by three radiologists in a blinded fashion.

RESULTS

The application of the noise filter in the large phantom yielded a reduction of image noise by 42% (p < 0.0001). The CNR values of the tumors in the nonfiltered and filtered large phantom were lower than that in the intermediate phantom (p < 0.05). In the non-filtered and filtered large phantom, 25% and 19% fewer tumors, respectively, were detected on average compared with the intermediate phantom (p < 0.01).

CONCLUSION

The risk of missing hypovascular liver tumors with CT is substantially increased in large patients. A noise filter improves image quality in obese patients.

CT urography in the urinary bladder: to compare excretory phase images using a low noise index and a high noise index with adaptive noise reduction filter

BACKGROUND

Although CT urography (CTU) is widely used for the evaluation of the entire urinary tract, the most important drawback is the radiation exposure.

PURPOSE

To evaluate the effect of a noise reduction filter (NRF) using a phantom and to quantitatively and qualitatively compare excretory phase (EP) images using a low noise index (NI) with those using a high NI and postprocessing NRF (pNRF).

MATERIAL AND METHODS

Each NI value was defined for a slice thickness of 5 mm, and reconstructed images with a slice thickness of 1.25 mm were assessed. Sixty patients who were at high risk of developing bladder tumors (BT) were divided into two groups according to whether their EP images were obtained using an NI of 9.88 (29 patients; group A) or an NI of 20 and pNRF (31 patients; group B). The CT dose index volume (CTDI(vol)) and the contrast-to-noise ratio (CNR) of the bladder with respect to the anterior pelvic fat were compared in both groups. Qualitative assessment of the urinary bladder for image noise, sharpness, streak artifacts, homogeneity, and the conspicuity of polypoid or sessile-shaped BTs with a short-axis diameter greater than 10 mm was performed using a 3-point scale.

RESULTS

The phantom study showed noise reduction of approximately 40% and 76% dose reduction between group A and group B. CTDI(vol) demonstrated a 73% reduction in group B (4.6 ± 1.1 mGy) compared with group A (16.9 ± 3.4 mGy). The CNR value was not significantly different (P = 0.60) between group A (16.1 ± 5.1) and group B (16.6 ± 7.6). Although group A was superior (P < 0.01) to group B with regard to image noise, other qualitative analyses did not show significant differences.

CONCLUSION

EP images using a high NI and pNRF were quantitatively and qualitatively comparable to those using a low NI, except with regard to image noise.

Development of a noise reduction filter algorithm for pediatric body images in multidetector CT

Recently, several types of post-processing image filter which was designed to reduce noise allowing a corresponding dose reduction in CT images have been proposed and these were reported to be useful for noise reduction of CT images of adult patients. However, these have not been reported on adaptation for pediatric patients. Because they are not very effective with small (<20 cm) display fields of view, they could not be used for pediatric (e.g., premature babies and infants) body CT images. In order to solve this restriction, we have developed a new noise reduction filter algorithm which can be applicable for pediatric body CT images. This algorithm is based on a three-dimensional post processing, in which output pixel values are calculated by multi-directional, one-dimensional median filters on original volumetric datasets. The processed directions were selected except in in-plane (axial plane) direction, and consequently the in-plane spatial resolution was not affected by the filter. Also, in other directions, the spatial resolutions including slice thickness were almost maintained due to a characteristic of non-linear filtering of the median filter. From the results of phantom studies, the proposed algorithm could reduce standard deviation values as a noise index by up to 30% without affecting the spatial resolution of all directions, and therefore, contrast-to-noise ratio was improved by up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of pediatric body CT images.

Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods

PURPOSE

Though highly desirable in radiologic procedures, low-dose CT (LDCT) images tend to be severely degraded by quantum noise and non-stationary artifacts. The purpose of this paper is to improve the abdominal LDCT images by the approach of Weighted Intensity Averaging over Large-scale Neighborhoods (WIA-LN).

MATERIALS AND METHODS

In the implementation of the proposed WIA-LN method, the processed pixel intensities are adaptively calculated as the weighted intensity averaging of the pixels with similar surrounding structures throughout a large-scale neighborhood. Both phantom and clinical abdominal CT images from a 16 detector rows Siemens CT were acquired at standard and 80% reduced tube current time products (150 mAs and 30 mAs corresponding to standard-dose and low-dose protocols, respectively). Visual comparison, statistical qualitative analysis (image quality scores and hepatic cyst diagnosis), and quantitative calculation (noise and contrast-to-noise ratio) are made.

RESULTS

Better vision and quantitative performance are realized using the proposed WIA-LN method. Compared to original LDCT and standard-dose CT (SDCT) images, statistically significant improvement of noise/artifacts suppression, contrast preservation and hepatic cyst detection in LDCT images are achieved by using the proposed method (P<0.05).

CONCLUSION

With the tube current reduced to approximate one-fifth of the standard tube current setting, clinically acceptable images can still be obtained by using the proposed method.

[Radiation dose reduction using a non-linear image filter in MDCT]

The development of MDCT enabled various high-quality 3D imaging and optimized scan timing with contrast injection in a multi-phase dynamic study. Since radiation dose tends to increase to yield such high-quality images, we have to make an effort to reduce radiation dose. A non-linear image filter (Neuro 3D filter: N3D filter) has been developed in order to improve image noise. The purpose of this study was to evaluate the physical performance and effectiveness of this non-linear image filter using phantoms, and show how we can reduce radiation dose in clinical use of this filter. This N3D filter reduced radiation dose by about 50%, with minimum deterioration of spatial reduction in phantom and clinical studies. This filter shows great potential for clinical application.

Effect of edge-preserving adaptive image filter on low-contrast detectability in CT systems: application of ROC analysis

Objective. For the multislice CT (MSCT) systems with a larger number of detector rows, it is essential to employ dose-reduction techniques. As reported in previous studies, edge-preserving adaptive image filters, which selectively eliminate only the noise elements that are increased when the radiation dose is reduced without affecting the sharpness of images, have been developed. In the present study, we employed receiver operating characteristic (ROC) analysis to assess the effects of the quantum denoising system (QDS), which is an edge-preserving adaptive filter that we have developed, on low-contrast resolution, and to evaluate to what degree the radiation dose can be reduced while maintaining acceptable low-contrast resolution. Materials and Methods. The low-contrast phantoms (Catphan 412) were scanned at various tube current settings, and ROC analysis was then performed for the groups of images obtained with/without the use of QDS at each tube current to determine whether or not a target could be identified. The tube current settings for which the area under the ROC curve (Az value) was approximately 0.7 were determined for both groups of images with/without the use of QDS. Then, the radiation dose reduction ratio when QDS was used was calculated by converting the determined tube current to the radiation dose. Results. The use of the QDS edge-preserving adaptive image filter allowed the radiation dose to be reduced by up to 38%. Conclusion. The QDS was found to be useful for reducing the radiation dose without affecting the low-contrast resolution in MSCT studies.

O valor da fase sem contraste na tomografia computadorizada do abdome

OBJETIVO: Determinar o valor agregado da fase sem meio de contraste da tomografia computadorizada do abdome em pacientes sem diagnóstico determinado ou em estadiamento tumoral. MATERIAIS E MÉTODOS: Estudo prospectivo e transversal em 100 pacientes consecutivos submetidos a tomografia computadorizada abdominal sem e com meio de contraste intravenoso. Dois examinadores avaliaram todos os exames, procurando estabelecer, através da fase com meio de contraste intravenoso (primeira análise) e posteriormente através da fase sem contraste (segunda análise), o diagnóstico principal e os secundários em função da indicação clínica do exame. Mediu-se a freqüência de mudança diagnóstica decorrente da análise combinada das fases pré- e pós-contraste intravenoso. Casos que tiveram mudança diagnóstica foram avaliados por especialistas clínicos para determinar se implicaria mudanças de conduta. RESULTADOS: Diagnósticos principal e secundário foram modificados em 1 e 18 casos, respectivamente (p = 1,000; p = 0,143). Os diagnósticos modificados foram: esteatose, definição de nódulo em adrenal, nefrolitíase, classificação de cistos renais e calcificação hepática. Nos casos em que a fase sem contraste modificou o diagnóstico, os especialistas mudaram sua conduta em 14/19 (73%) dos pacientes (p = 0,038). CONCLUSÃO: A fase sem contraste não modificou significativamente o diagnóstico principal ou secundário. Porém, as mudanças nos diagnósticos secundários influenciaram na conduta adotada pelos especialistas.

New method of evaluating edge-preserving adaptive filters for computed tomography (CT): digital phantom method

To evaluate the characteristics of edge-preserving adaptive filters for selectively eliminating noise without affecting resolution in low-dose scanning, we have developed a digital phantom image and evaluated noise statistical values, noise characteristics, and resolution characteristics. The results confirmed that edge-preserving adaptive filters function as smoothing filters in low-contrast regions containing noise, permitting the density resolution to be improved, while the strength of the smoothing filter is reduced to maintain spatial resolution in high-contrast regions containing small structures. It has therefore been confirmed that edge-preserving adaptive filters function as filters for selectively eliminating only the noise elements that are increased when the exposure dose is reduced and that such filters are effective for improving image quality. Using such digital phantom images, images acquired using conditions that are difficult to set in actual CT scanning can be obtained and images specifically for the evaluation target can easily be generated. In addition, the noise level, frequency distribution of the noise, and resolution characteristics of the objects present in the input image can be freely set. It is concluded that evaluation of processing using a digital phantom image is effective for evaluating image processing.