Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study

PURPOSE

To investigate the feasibility of ultra-low-dose CT (ULDCT) reconstructed with the artificial intelligence iterative reconstruction (AIIR) algorithm in total-body PET/CT imaging.

METHODS

The study included both the phantom and clinical parts. An anthropomorphic phantom underwent CT imaging with ULDCT (10mAs) and standard-dose CT (SDCT) (120mAs), respectively. ULDCT was reconstructed with AIIR and hybrid iterative reconstruction (HIR) (expressed as ULDCT-AIIR_phantom and ULDCT-HIR_phantom), respectively, and SDCT was reconstructed with HIR (SDCT-HIR_phantom) as control. In the clinical part, 52 patients with malignant tumors underwent the total-body PET/CT scan. ULDCT with AIIR (ULDCT-AIIR) and HIR (ULDCT-HIR), respectively, was reconstructed for PET attenuation correction, followed by the SDCT reconstructed with HIR (SDCT-HIR) for anatomical location. PET/CT images' quality was qualitatively assessed by two readers. The CT_mean, as well as the CT standard deviation (CT_sd), SUV_max, SUV_mean, and the SUV standard deviation (SUV_sd), was recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared.

RESULTS

The image quality of ULDCT-HIR_phantom was inferior to the SDCT-HIR_phantom, but no significant difference was found between the ULDCT-AIIR_phantom and SDCT-HIR_phantom. The subjective score of ULDCT-AIIR in the neck, chest and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the change rates of CT_mean in thyroid, neck muscle, lung, mediastinum, back muscle, liver, lumbar muscle, first lumbar spine and sigmoid colon were -2.15, -1.52, 0.66, 2.97, 0.23, 8.91, 0.06, -4.29 and 8.78%, respectively, while all CT_sd of ULDCT-AIIR was lower than that of SDCT-HIR. Except for the brain, the CNR of ULDCT-AIIR was the same as the SDCT-HIR, but the SNR was higher. The change rates of SUV_max, SUV_mean and SUV_sd were within [Formula: see text] 3% in all ROIs. For the lesions, the SUV_max, SUV_sd and TBR showed no significant difference between PET-AIIR and PET-HIR.

CONCLUSION

The SDCT-HIR could not be replaced by the ULDCT-AIIR at date, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstances in PET/CT examination.

Can optimized model-based iterative reconstruction improve the contrast of liver lesions in CT?

BACKGROUND

Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability.

PURPOSE

To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection.

MATERIAL AND METHODS

Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion.

RESULTS

A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRST_MILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRST_MILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRST_STR showing the closest results (P < 0.001).

CONCLUSION

Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.

Feasibility study of three-dimensional printing knee model using the ultra-low-dose CT scan for preoperative planning and simulated surgery

Objective

To explore the feasibility of the three-dimensional printing (3DP) knee model using the ultra-low-dose computed tomography (CT) scan for preoperative planning and simulated surgery.

Methods

Thirty-six patients were divided into the standard-dose protocol group (A) and ultra-low-dose protocol group (B). The anteroposterior diameter, left and right diameter of femur, anteroposterior diameter of tibial plateau (APTP), left and right diameter, distance from the intercondylar ridge to tibial tuberosity, lower femur angle, and upper tibial angle were measured on CT images. On the 3D printed knee joint model, Vernier calipers were used to measure: anteroposterior diameter, left and right diameter of the internal and external condyles of femur; left and right diameters, anteroposterior diameters of tibial plateau; upper and lower meridian, left and right diameters of patella.

Results

With group A as reference, the effective radiation dose in group B was significantly reduced to 97.0% (36.4 ± 3.7 uSv and 1.1 ± 0.2 uSv, respectively). There was no difference in objective parameters for 3DP model (p = 0.31–0.84). None of the quantitative parameters of image quality showed significant difference (p = 0.11–0.96). Despite lower score of image quality and 3DP model in group B (3.0 ± 0.0 vs. 2.1 ± 0.2, 2.9 ± 0.3 vs. 2.2 ± 0.4; p < 0.05), the diagnostic performance was consistent in the two groups (all scores ≥ 2). Image quality and 3DP printed models were highly consistent (k = 0.97).

Conclusions

Ultra-low-dose protocol reduces the radiation dose while maintaining the image quality of knee. It meets the requirement for 3DP model, internal fixation model selection, and simulated surgery.

Impact of ROI Size on the Accuracy of Noise Measurement in CT on Computational and ACR Phantoms

BACKGROUND

The effect of region of interest (ROI) size variation on producing accurate noise levels is not yet studied.

OBJECTIVE

This study aimed to evaluate the influence of ROI sizes on the accuracy of noise measurement in computed tomography (CT) by using images of a computational and American College of Radiology (ACR) phantoms.

MATERIAL AND METHODS

In this experimental study, two phantoms were used, including computational and ACR phantoms. A computational phantom was developed by using Matlab R215a software (Mathworks Inc., Natick, MA Natick, MA) with a homogeneously +100 Hounsfield Unit (HU) value and an added-Gaussian noise with various levels of 5, 10, 25, 50, 75, and 100 HU. The ACR phantom was scanned with a Philips MX-16 slice CT scanner in different slice thicknesses of 1.5, 3, 5, and 7 mm to obtain noise variation. Noise measurement was conducted at the center of the phantom images and four locations close to the edge of the phantom images using different ROI sizes from 3 × 3 to 41 × 41 pixels, with an increased size of 2 × 2 pixels.

RESULTS

The use of a minimum ROI size of 21 × 21 pixels shows noise in the range of ± 5% ground truth noise. The measured noise increases above the ± 5% range if the used ROI is smaller than 21 × 21 pixels.

CONCLUSION

A minimum acceptable ROI size is required to maintain the accuracy of noise measurement with a size of 21 × 21 pixels.

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.

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies

Purpose

In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiation. We compared image quality and radiologists’ acceptance of model-based iterative (MBIR) and deep learning (DLR) reconstructions of low-dose CE-APCT by UHRCT.

Methods

Using our high-resolution (matrix size: 1024) and low-dose (tube voltage 100 kV; noise index: 20–40 HU) protocol, we scanned phantoms to compare the modulation transfer function and noise power spectrum between MBIR and DLR and assessed findings in 36 consecutive patients who underwent CE-APCT (noise index: 35 HU; mean CTDI_vol: 4.2 ± 1.6 mGy) by UHRCT. We used paired t-test to compare objective noise and contrast-to-noise ratio (CNR) and Wilcoxon signed-rank test to compare radiologists’ subjective acceptance regarding noise, image texture and appearance, and diagnostic confidence between MBIR and DLR using our routine protocol (matrix size: 512; tube voltage: 120 kV; noise index: 15 HU) for reference.

Results

Phantom studies demonstrated higher spatial resolution and lower low-frequency noise by DLR than MBIR at equal doses. Clinical studies indicated significantly worse objective noise, CNR, and subjective noise by DLR than MBIR, but other subjective characteristics were better (P < 0.001 for all). Compared with the routine protocol, subjective noise was similar or better by DLR, and other subjective characteristics were similar or worse by MBIR.

Conclusion

Image quality, except regarding noise characteristics, and acceptance by radiologists were better by DLR than MBIR in low-dose CE-APCT by UHRCT.

Graphical abstract

Evaluation of Apparent Noise on CT Images Using Moving Average Filters

This study aims to devise a simple method for evaluating the magnitude of texture noise (apparent noise) observed on computed tomography (CT) images scanned at a low radiation dose and reconstructed using iterative reconstruction (IR) and deep learning reconstruction (DLR) algorithms, and to evaluate the apparent noise in CT images reconstructed using the filtered back projection (FBP), IR, and two types of DLR (AiCE Body and AiCE Body Sharp) algorithms. We set a square region of interest (ROI) on CT images of standard- and obese-sized low-contrast phantoms, slid different-sized moving average filters in the ROI vertically and horizontally in steps of 1 pixel, and calculated the standard deviation (SD) of the mean CT values for each filter size. The SD of the mean CT values was fitted with a curve inversely proportional to the filter size, and an apparent noise index was determined from the curve-fitting formula. The apparent noise index of AiCE Body Sharp images for a given mAs value was approximately 58, 23, and 18% lower than that of the FBP, AIDR 3D, and AiCE Body images, respectively. The apparent noise index was considered to reflect noise power spectrum values at lower spatial frequency. Moreover, the apparent noise index was inversely proportional to the square roots of the mAs values. Thus, the apparent noise index could be a useful indicator to quantify and compare texture noise on CT images obtained with different scan parameters and reconstruction algorithms.

Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images

Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon's statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTF_Task), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNR_LO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTF_Taskwas measured at four contrast levels, and CNR_LOwas measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

Influence of tube voltage, tube current and newer iterative reconstruction algorithms in CT perfusion imaging in rabbit liver VX2 tumors

PURPOSE

We aimed to explore the influence of tube voltage, current and iterative reconstruction (IR) in computed tomography perfusion imaging (CTPI) and to compare CTPI parameters with microvessel density (MVD).

METHODS

Hepatic CTPI with three CTPI protocols (protocol A, tube voltage/current 80 kV/40 mAs; protocol B, tube voltage/current 80 kV/80 mAs; protocol C: tube voltage/current 100 kV/80 mAs) were performed in 25 rabbit liver VX2 tumor models, and filtered back projection (FBP) and IR were used for reconstruction of raw data. Hepatic arterial perfusion (HAP), hepatic portal perfusion (HPP), total perfusion (TP), hepatic arterial perfusion index (HPI), blood flow (BF) and blood volume (BV) of VX2 tumor and normal hepatic parenchyma were measured. Image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified and radiation dose was recorded. MVD was counted using CD34 stain and compared with CTPI parameters.

RESULTS

The highest radiation dose was found in protocol C, followed by protocols B and A. IR lowered image noise and improved SNR and CNR in all three protocols. There was no statistical difference between HAP, HPP, TP, HPI, BF and BV of VX2 tumor and normal hepatic parenchyma among the three protocols (P > 0.05) with FBP or IR reconstruction, and no statistical difference between IR and FBP reconstruction (P > 0.05) in either protocol. MVD had a positive linear correlation with HAP, TP, BF, with best correlation observed with HAP; MVD of VX2 tumor showed no or poor correlation with HPI and BV.

CONCLUSION

CTPI parameters are not affected by tube voltage, current or reconstruction algorithm; HAP can best reflect MVD, but no correlation exists between BV and MVD.

Double Dose Reduction in the Equilibrium Phase of Chest-Pelvic CT With Low Tube Voltage and Forward-Projected Model-Based Iterative Reconstruction Solution

Objectives

This study aimed to examine whether a new imaging method (80-kV forward-projected model-based iterative reconstruction solution [FIRST] protocol) that uses a combination of low tube voltage and FIRST can reduce radiation dose and contrast medium volume by comparing the quality of the resulting image with that of the image obtained by 120-kV adaptive iterative dose reduction 3D protocol in the equilibrium phase of chest-pelvic computed tomography (CT).

Subjects and methods

Twenty-seven patients underwent CT by both protocols on different days. Two radiologists subjectively assessed image quality by scoring axial images for sharpness, contrast enhancement, noise, artifacts, and overall quality. The mean CT values, standard deviations, contrast-to-noise ratios, and signal-to-noise ratios in the liver, aorta, and erector spinae muscles were used for objective assessment. Radiation dose parameters included the CT dose index volume, dose-length product, effective dose, and size-specific dose estimate. Results were compared for different body mass index categories.

Results

The 80-kV FIRST protocol helped achieve mean reductions of 36.3%, 35.7%, and 36.6% in CT dose index volume, effective dose, and size-specific dose estimate, respectively (p < 0.01). Therefore, this protocol was regarded as comparable to the conventional protocol in image quality, except for visual sharpness.

Conclusions

The 80-kV FIRST protocol is capable of reducing radiation dose and contrast medium volume compared to the adaptive iterative dose reduction 3D protocol in the equilibrium phase of chest-pelvic CT.

Diagnostic Reference Levels based on clinical indications in computed tomography: a literature review

BACKGROUND

In August 2017, the European Commission awarded the "European Study on Clinical Diagnostic Reference levels for X-ray Medical Imaging" project to the European Society of Radiology, to provide up-to-date Diagnostic Reference Levels based on clinical indications. The aim of this work was to conduct an extensive literature review by analysing the most recent studies published and the data provided by the National Competent Authorities, to understand the current situation regarding Diagnostic Reference Levels based on clinical indications for computed tomography.

RESULTS

The literature review has identified 23 papers with Diagnostic Reference Levels based on clinical indications for computed tomography from 15 countries; 12 of them from Europe. A total of 28 clinical indications for 6 anatomical areas (head, cervical spine/neck, chest, abdomen, abdomen-pelvis, chest-abdomen-pelvis) have been identified.

CONCLUSIONS

In all the six anatomical areas for which Diagnostic Reference Levels based on clinical indications were found, a huge variation of computed tomography dose descriptor values was identified, providing evidence for a need to develop strategies to standardise and optimise computed tomography protocols.

Characterization of office laser printers for 3-D printing of soft tissue CT phantoms

The purpose of our study is to develop and evaluate a method for radiopaque 3-D printing (R3P) of soft tissue computed tomography (CT) phantoms with office laser printers. Five laser printers from different vendors are tested for toner CT attenuation. A liver phantom is created by printing CT images of a patient liver on office paper. One thousand eight hundred sixty paper sheets are printed with three repeated prints per page, resulting in a stack of 18.6 cm. The phantom is examined with 12 tube current settings. Images are reconstructed using filtered back projection (FBP) and iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Seven radiologists rated image quality of all acquisitions. Toner attenuation of all investigated printers increased linearly with the print template grayscale. The liver phantom reproduced anatomic detail and attenuation values of the patient ( mean ± SD HU difference 12.68 ± 7.74 ). Image quality scores increased with dose but did not vary significantly above a threshold dose for AIDR 3D. Overall, AIDR 3D reconstructed images are rated superior to FBP reconstructions ( p < 0.001 ). In conclusion, R3P with standard office laser printers can generate soft tissue CT phantoms without hardware manipulations but with limited flexibility regarding attenuation properties of the printed toner material.

Evaluating the effect of increased pitch, iterative reconstruction and dual source CT on dose reduction and image quality

Objective:

To compare radiation dose and image quality of thoracoabdominal scans obtained with a high-pitch protocol (pitch 3.2) and iterative reconstruction (Sinogram Affirmed Iterative Reconstruction) in comparison to standard pitch reconstructed with filtered back projection (FBP) using dual source CT.

Methods:

114 CT scans (Somatom Definition Flash, Siemens Healthineers, Erlangen, Germany), 39 thoracic scans, 54 thoracoabdominal scans and 21 abdominal scans were performed. Analysis of three protocols was undertaken; pitch of 1 reconstructed with FBP, pitch of 3.2 reconstructed with SAFIRE, pitch of 3.2 with stellar detectors reconstructed with SAFIRE. Objective and subjective image analysis were performed. Dose differences of the protocols used were compared.

Results:

Dose was reduced when comparing scans with a pitch of 1 reconstructed with FBP to high-pitch scans with a pitch of 3.2 reconstructed with SAFIRE with a reduction of volume CT dose index of 75% for thoracic scans, 64% for thoracoabdominal scans and 67% for abdominal scans. There was a further reduction after the implementation of stellar detectors reflected in a reduction of 36% of the dose–length product for thoracic scans. This was not at the detriment of image quality, contrast-to-noise ratio, signal-to-noise ratio and the qualitative image analysis revealed a superior image quality in the high-pitch protocols.

Conclusion:

The combination of a high pitch protocol with iterative reconstruction allows significant dose reduction in routine chest and abdominal scans whilst maintaining or improving diagnostic image quality, with a further reduction in thoracic scans with stellar detectors.

Advances in knowledge:

High pitch imaging with iterative reconstruction is a tool that can be used to reduce dose without sacrificing image quality.