Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study

Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction

PURPOSE

We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR).

METHODS

This retrospective study enrolled 36 patients (15 men, 21 women; age, 53.9 ± 19.5 years) who had undergone high-resolution CT of the temporal bone. Axial and coronal images were reconstructed using DLR, HIR, and filtered back projection (FBP). In qualitative image analyses, two radiologists independently compared the DLR and HIR images with FBP in terms of depiction of structures, image noise, and overall quality, using a 5-point scale (5 = better than FBP, 1 = poorer than FBP) to evaluate image quality. The other two radiologists placed regions of interest on the tympanic cavity and measured the standard deviation of CT attenuation (i.e., quantitative image noise). Scores from the qualitative and quantitative analyses of the DLR and HIR images were compared using, respectively, the Wilcoxon signed-rank test and the paired t-test.

RESULTS

Qualitative and quantitative image noise was significantly reduced in DLR images compared with HIR images (all comparisons, p ≤ 0.016). Depiction of the otic capsule, auditory ossicles, and tympanic membrane was significantly improved in DLR images compared with HIR images (both readers, p ≤ 0.003). Overall image quality was significantly superior in DLR images compared with HIR images (both readers, p < 0.001).

CONCLUSION

Compared with HIR, DLR provided significantly better-quality high-resolution CT images of the temporal bone.

Spectral CT image reconstruction using a constrained optimization approach-An algorithm for AAPM 2022 spectral CT grand challenge and beyond

BACKGROUND

CT reconstruction is of essential importance in medical imaging. In 2022, the American Association of Physicists in Medicine (AAPM) sponsored a Grand Challenge to investigate the challenging inverse problem of spectral CT reconstruction, with the aim of achieving the most accurate reconstruction results. The authors of this paper participated in the challenge and won as a runner-up team.

PURPOSE

This paper reports details of our PROSPECT algorithm (Prior-based Restricted-variable Optimization for SPEctral CT) and follow-up studies regarding the algorithm's accuracy and enhancement of its convergence speed.

METHODS

We formulated the reconstruction task as an optimization problem. PROSPECT employed a one-step backward iterative scheme to solve this optimization problem by allowing estimation of and correction for the difference between the actual polychromatic projection model and the monochromatic model used in the optimization problem. PROSPECT incorporated various forms of prior information derived by analyzing training data provided by the Grand Challenge to reduce the number of unknown variables. We investigated the impact of projection data precision on the resulting solution accuracy and improved convergence speed of the PROSPECT algorithm by incorporating a beam-hardening correction (BHC) step in the iterative process. We also studied the algorithm's performance under noisy projection data.

RESULTS

Prior knowledge allowed a reduction of the number of unknown variables by85.9 % $85.9\%$ . PROSPECT algorithm achieved the average root of mean square error (RMSE) of3.3 × 10 - 6 $3.3\,\times \,10^{-6}$ in the test data set provided by the Grand Challenge. Performing the reconstruction with the same algorithm but using double-precision projection data reduced RMSE to1.2 × 10 - 11 $1.2\,\times \,10^{-11}$ . Including the BHC step in the PROSPECT algorithm accelerated the iteration process with a 40% reduction in computation time.

CONCLUSIONS

PROSPECT algorithm achieved a high degree of accuracy and computational efficiency.

Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images

This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.

Artificial Intelligence Iterative Reconstruction in Computed Tomography Angiography: An Evaluation on Pulmonary Arteries and Aorta With Routine Dose Settings

OBJECTIVE

The objective of this study is to investigate whether a newly introduced deep learning-based iterative reconstruction algorithm, namely, the artificial intelligence iterative reconstruction (AIIR), has a clinical value in computed tomography angiography (CTA), especially for visualizing vascular structures and related lesions, with routine dose settings.

METHODS

A total of 63 patients were retrospectively collected from the triple rule-out CTA examinations, where both pulmonary and aortic data were available for each patient and were taken as the example for investigation. The images were reconstructed using the filtered back projection (FBP), hybrid iterative reconstruction (HIR), and the AIIR. The visibility of vasculature and pulmonary emboli and the general image quality were assessed.

RESULTS

Artificial intelligence iterative reconstruction resulted in significantly ( P < 0.001) lower noise as well as higher signal-to-noise ratio and contrast-to-noise ratio compared with FBP and HIR. Besides, AIIR achieved the highest subjective scores on general image quality ( P < 0.05). For the vasculature visibility, AIIR offered the best vessel conspicuity, especially for the small vessels ( P < 0.05). Also, >90% of emboli on the AIIR images were graded as sharp (score 5), whereas <15% of emboli on FBP and HIR images were scored 5.

CONCLUSION

As demonstrated for pulmonary and aortic CTAs, AIIR improves the image quality and offers a better depiction for vascular structures compared with FBP and HIR. The visibility of the pulmonary emboli was also increased by AIIR.

Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis

PURPOSE

To evaluate diagnostic performance and image quality of ultralow-dose CT (ULDCT) in diagnosing acute appendicitis with an image-based deep-learning denoising algorithm (IDLDA).

METHODS

This retrospective multicenter study included 180 patients (mean ± standard deviation, 29 ± 9 years; 91 female) who underwent contrast-enhanced 2-mSv CT for suspected appendicitis from February 2014 to August 2016. We simulated ULDCT from 2-mSv CT, reducing the dose by at least 50%. Then we applied an IDLDA on ULDCT to produce denoised ULDCT (D-ULDCT). Six radiologists with different experience levels (three board-certified radiologists and three residents) independently reviewed the ULDCT and D-ULDCT. They rated the likelihood of appendicitis and subjective image qualities (subjective image noise, diagnostic acceptability, and artificial sensation). One radiologist measured image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). We used the receiver operating characteristic (ROC) analyses, Wilcoxon's signed-rank tests, and paired t-tests.

RESULTS

The area under the ROC curves (AUC) for diagnosing appendicitis ranged 0.90-0.97 for ULDCT and 0.94-0.97 for D-ULDCT. The AUCs of two residents were significantly higher on D-ULDCT (AUC difference = 0.06 [95% confidence interval, 0.01-0.11; p = .022] and 0.05 [0.00-0.10; p = .046], respectively). D-ULDCT provided better subjective image noise and diagnostic acceptability to all six readers. However, the response of board-certified radiologists and residents differed in artificial sensation (all p ≤ .003). D-ULDCT showed significantly lower image noise, higher SNR, and higher CNR (all p < .001).

CONCLUSION

An IDLDA can provide better ULDCT image quality and enhance diagnostic performance for less-experienced radiologists.

Optimization of the "Perth CT" Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty

Background and Objectives: Total knee arthroplasty (TKA) has become the treatment of choice for advanced osteoarthritis. The aim of this paper was to show the possibilities of optimizing the Perth CT protocol, which is highly effective for preoperative planning and postoperative assessment of alignment. Materials and Methods: The cross-sectional study comprised 16 patients for preoperative planning or postoperative evaluation of TKA. All patients were examined with the standard and optimized Perth CT protocol using advance techniques, including automatic exposure control (AEC), iterative image reconstruction (IR), as well as a single-energy projection-based metal artifact reduction algorithm for eliminating prosthesis artifacts. The effective radiation dose (E) was determined based on the dose report. Imaging quality is determined according to subjective and objective (values of signal to noise ratio (SdNR) and figure of merit (FOM)) criteria. Results: The effective radiation dose with the optimized protocol was significantly lower compared to the standard protocol (p < 0.001), while in patients with the knee prosthesis, E increased significantly less with the optimized protocol compared to the standard protocol. No significant difference was observed in the subjective evaluation of image quality between protocols (p > 0.05). Analyzing the objective criteria for image quality optimized protocols resulted in lower SdNR values and higher FOM values. No significant difference of image quality was determined using the SdNR and FOM as per the specified protocols and parts of extremities, and for the presence of prothesis. Conclusions: Retrospecting the ALARA ('As Low As Reasonably Achievable') principles, it is possible to optimize the Perth CT protocol by reducing the kV and mAs values and by changing the collimation and increasing the pitch factor. Advanced IR techniques were used in both protocols, and AEC was used in the optimized protocol. The effective dose of radiation can be reduced five times, and the image quality will be satisfactory.

Assessing the effect of scanning parameter on the size and density of pulmonary nodules: a phantom study

BACKGROUND

Lung cancer remains a leading cause of death among cancer patients. Computed tomography (CT) plays a key role in lung cancer screening. Previous studies have not adequately quantified the effect of scanning protocols on the detected tumor size. The aim of this study was to assess the effect of various CT scanning parameters on tumor size and densitometry based on a phantom study and to investigate the optimal energy and mA image quality for screening assessment.

METHODS

We proposed a new model using the LUNGMAN N1 phantom multipurpose anthropomorphic chest phantom (diameters: 8, 10, and 12 mm; CT values: - 100, - 630, and - 800 HU) to evaluate the influence of changes in tube voltage and tube current on the size and density of pulmonary nodules. In the LUNGMAN N1 model, three types of simulated lung nodules representing solid tumors of different sizes were used. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used to evaluate the image quality of each scanning combination. The consistency between the calculated results based on segmentation from two physicists was evaluated using the interclass correlation coefficient (ICC).

RESULTS

In terms of nodule size, the longest diameters of ground-glass nodules (GGNs) were closest to the ground truth on the images measured at 100 kVp tube voltage, and the longest diameters of solid nodules were closest to the ground truth on the images measured at 80 kVp tube voltage. In respect to density, the CT values of GGNs and solid nodules were closest to the ground truth when measured at 80 kVp and 100 kVp tube voltage, respectively. The overall agreement demonstrates that the measurements were consistent between the two physicists.

CONCLUSIONS

Our proposed model demonstrated that a combination of 80 kVp and 140 mA scans was preferred for measuring the size of the solid nodules, and a combination of 100 kVp and 100 mA scans was preferred for measuring the size of the GGNs when performing lung cancer screening. The CT values at 80 kVp and 100 kVp were preferred for the measurement of GGNs and solid nodules, respectively, which were closest to the true CT values of the nodules. Therefore, the combination of scanning parameters should be selected for different types of nodules to obtain more accurate nodal data.

Assessing the Effects of Deep Learning Reconstruction on Abdominal CT Without Arm Elevation

Purpose: To evaluate the effects of deep learning reconstruction (DLR) on image quality of abdominal computed tomography (CT) in patients without arm elevation compared with hybrid-iterative reconstruction (Hybrid-IR) and filtered back projection (FBP). Methods: In this retrospective study, axial images of 26 patients who underwent CT without arm elevation were reconstructed using DLR, Hybrid-IR, and FBP. Streak artifact index (SAI) was calculated by dividing the standard deviation of CT attenuation in the liver or spleen by that in fat. Two other blinded radiologists evaluated streak artifacts on images (in the liver, spleen, and kidney), depiction of liver vessels, subjective image noise, and overall quality. They were also asked to detect space-occupying lesions other than cysts in the liver, spleen, and kidney. Results: The SAI (liver/spleen) in DLR images was significantly reduced compared with Hybrid-IR and FBP. Regarding qualitative image analysis, streak artifacts in the 3 organs, qualitative image noise, and overall quality in DLR images were rated by both readers as significantly improved compared with Hybrid-IR (P ≤ .012) and FBP (P < .001). Both blinded readers detected more lesions on DLR images than on Hybrid-IR and FBP ones. Conclusion: DLR resulted in significantly better-quality abdominal CT images in patients scanned without elevating their arms with reducing streak artifacts compared with Hybrid-IR and FBP.

Impact of a Deep Learning-based Super-resolution Image Reconstruction Technique on High-contrast Computed Tomography: A Phantom Study

RATIONALE AND OBJECTIVES

Deep-learning-based super-resolution image reconstruction (DLSRR) is a novel image reconstruction technique that is expected to contribute to improvement in spatial resolution as well as noise reduction through learning from high-resolution computed tomography (CT). This study aims to evaluate image quality obtained with DLSRR and assess its clinical potential.

MATERIALS AND METHODS

CT images of a Mercury CT 4.0 phantom were obtained using a 320-row multi-detector scanner at tube currents of 100, 200, and 300 mA. Image data were reconstructed by filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), deep-learning-based image reconstruction (DLR), and DLSRR at image reconstruction strength levels of mild, standard, and strong. Noise power spectrum (NPS), task transfer function (TTF), and detectability index were calculated.

RESULTS

The magnitude of the noise-reducing effect in comparison with FBP was in the order MBIR

CONCLUSION

The present results suggest that DLSRR can achieve greater noise reduction and improved spatial resolution in the high-contrast region compared with conventional DLR and iterative reconstruction techniques.

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Key Words

• deep-learning-based super-resolution image reconstruction
• detectability index
• iterative reconstruction
• noise power spectrum
• task transfer function

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MESH Headings Collapse

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Affiliation(s)

Hideyuki Sato Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
Shinichiro Fujimoto Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Nobuo Tomizawa Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
Hidekazu Inage Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
Takuya Yokota Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
Hikaru Kudo Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
Ruiheng Fan Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
Keiichi Kawamoto Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
Yuri Honda Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
Takayuki Kobayashi Department of Radiological Technology, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
Tohru Minamino Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
Yosuke Kogure Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan

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The efficacy of low-dose CT with deep learning image reconstruction in the surveillance of incidentally detected pancreatic cystic lesions

PURPOSE

To evaluate the efficacy of low-dose CT (LDCT) with deep learning image reconstruction (DLIR) for the surveillance of pancreatic cystic lesions (PCLs) compared with standard-dose CT (SDCT) with adaptive statistical iterative reconstruction (ASIR-V).

METHODS

The study enrolled 103 patients who underwent pancreatic CT for follow-up of incidentally detected PCLs. The CT protocol included LDCT in the pancreatic phase with 40% ASIR-V, DLIR at medium (DLIR-M) and high levels (DLIR-H), and SDCT in the portal-venous phase with 40% ASIR-V. The overall image quality and conspicuity of PCLs were qualitatively assessed using five-point scales by two radiologists. The size of PCLs, presence of thickened/enhancing walls, enhancing mural nodules, and main pancreatic duct dilatation were reviewed. CT noise and cyst-to-pancreas contrast-to-noise ratio (CNR) were measured. Qualitative and quantitative parameters were analyzed using the chi-squared test, one-way ANOVA, and t-test. Additionally, interobserver agreement was analyzed using the kappa and weighted-kappa statistics.

RESULTS

The volume CT dose-indexes in LDCT and SDCT were 3.0 ± 0.6 mGy and 8.4 ± 2.9 mGy, respectively. LDCT with DLIR-H showed the highest overall image quality, the lowest noise, and the highest CNR. The PCL conspicuity in LDCT with either DLIR-M or DLIR-H was not significantly different from that in SDCT with ASIR-V. Other findings depicting PCLs also revealed no significant differences between LDCT with DLIR and SDCT with ASIR-V. Moreover, the results revealed good or excellent interobserver agreement.

CONCLUSION

LDCT with DLIR has a comparable performance with SDCT for the follow-up of incidentally detected PCLs.

[Possible Radiation Dose Reduction in Abdominal Plain CT Using Deep Learning Reconstruction]

PURPOSE

The purposes of this study were to evaluate the low-contrast detectability of CT images assuming hepatocellular carcinoma and to determine whether dose reduction in abdominal plain CT imaging is possible.

METHODS

A Catphan 600 was imaged at 350, 250, 150, and 50 mA using an Aquilion ONE PRISM Edition (Canon) and reconstructed using deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR). A low-contrast object-specific contrast-to-noise ratio (CNR_LO) was measured and compared in a 5-mm module with a CT value difference of 10 HU, assuming hepatocellular carcinoma; a visual examination was also performed. Moreover, an NPS within a uniform module was measured.

RESULTS

CNR_LO was higher for DLR at all doses (1.12 at 150 mA for DLR and 1.07 at 250 mA for MBIR). On visual evaluation, DLR could detect up to 150 mA and MBIR up to 250 mA. The NPS was lower for DLR at 0.1 cycles/mm at 150 mA.

CONCLUSION

The low-contrast detection performance was better with DLR than with MBIR, indicating the possibility of dose reduction.

Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice

Background

This study investigated the feasibility of channelized hoteling observer (CHO) model in computed tomography (CT) protocol optimization regarding the image quality and patient exposure. While the utility of using model observers such as to optimize the clinical protocol is evident, the pitfalls associated with the use of this method in practice require investigation.

Materials and Methods

This study was performed using variable tube current and adaptive statistical iterative reconstruction (ASIR) level (ASIR 10% to ASIR 100%). Various criteria including noise, high-contrast spatial resolution, CHOs model were used to compare image quality at different captured levels. For the implementation of CHO, we first tuned the model in a restricted dataset and then it to the evaluation of a large dataset of images obtained with different reconstruction ASIR and filtered back projection (FBP) levels.

Results

The results were promising in terms of CHO use for the stated purposes. Comparisons of the noise of reconstructed images with 30% ASIR and higher levels of noise in rebuilding images using the FBP approach showed a significant difference (P < 0.05). The spatial resolution obtained using various ASIR levels and tube currents were 0.8 pairs of lines per millimeter, which did not differ significantly from the FBP method (P > 0.05).

Conclusions

Based on the results, using 80% ASIR can reduce the radiation dose on lungs, abdomen, and pelvis CT scans while maintaining image quality. Furthermore using ASIR 60% only for the reconstruction of lungs, abdomen, and pelvis images at standard radiation dose leads to optimal image quality.

A novel protocol for abdominal low-dose CT scans adapted with a model-based iterative reconstruction method

PURPOSE

This study aims to introduce a novel low-dose abdominal computed tomography (CT) protocol adapted with model-based iterative reconstruction (MBIR), To validate the adaptability of this protocol, objective image quality and subjective clinical scores of low-dose MBIR images are compared with the normal-dose images.

METHODS

Normal-dose abdominal CT images of 58 patients and low-dose abdominal CT images of 52 patients are reconstructed using both conventional filtered back projection (FBP) and MBIR methods with and without smooth applying. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are used to compare image quality between the normal-dose and low-dose CT scans. CT dose indices (CTDI) of normal-dose and low-dose abdominal CT images on post-contrast venous phase are also compared.

RESULTS

The SNR, CNR and clinical score of low-dose MBIR images all show significant higher values (Bonferroni p < 0.05) than those of normal-dose images with conventional FBP method. A total of around 40% radiation dose reduction (CTDI: 5.3 vs 8.7 mGy) could be achieved via our novel abdominal CT protocol.

CONCLUSIONS

With the higher SNR/CNR and clinical scores, the low-dose CT abdominal imaging protocol with MBIR could effectively reduce the radiation for patients and provide equal or even higher image quality and also its adaptability in clinical abdominal CT image diagnosis.

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

Efficacy Evaluation of 64-Slice Spiral Computed Tomography Images in Laparoscopic-Assisted Distal Gastrectomy for Gastric Cancer under the Reconstruction Algorithm

This study was aimed to analyze the application value of the filtered back-projection (FBP) reconstruction algorithm of computed tomography (CT) images in laparoscopic-assisted distal gastrectomy. In this study, 56 patients with gastric cancer were selected as research subjects and randomly divided into the control group (CT-guided laparoscopic radical gastrectomy) and the observation group (CT-guided laparoscopic radical gastrectomy with the FBP reconstruction algorithm), with 28 patients in each group. Fourier transform and iterative reconstruction were introduced for comparison, and finally, the postoperative curative effect and adverse events were compared between the two groups. The results showed that the CT image quality score processed by the FBP reconstruction algorithm (4.31 ± 0.31) was significantly higher than that of the iterative reconstruction method (3.5 ± 0.29) and the Fourier transform method (3.97 ± 0.38) (P < 0.05). The incidences of postoperative wound infection and gastric motility disorder (5.88% and 8.16%, respectively) in the observation group were significantly lower than those in the control group (8.21% and 10.82%, respectively) (P < 0.05). The levels of serum interleukin-6 (IL-6) (280.35 ± 15.08 ng/L) and tumor necrosis factor-α (TNF-α) (144.32 ± 10.32 ng/L) in the observation group after the treatment were significantly lower than those in the control group, which were 399.71 ± 14.19 ng/L and 165.33 ± 10.08 ng/L, respectively (P < 0.05). In conclusion, the FBP reconstruction algorithm was better than other algorithms in the processing of gastric cancer CT images. The FBP reconstruction algorithm showed a good reconstruction effect on CT images of gastric cancer; CT images based on this algorithm helped to formulate targeted surgical treatment plans for gastric cancer, showing a high clinical application value.

Pediatric craniosynostosis computed tomography: an institutional experience in reducing radiation dose while maintaining diagnostic image quality

BACKGROUND

Children with craniosynostosis may undergo multiple computed tomography (CT) examinations for diagnosis and post-treatment follow-up, resulting in cumulative radiation exposure.

OBJECTIVE

To reduce the risks associated with radiation exposure, we evaluated the compliance, radiation dose reduction and clinical image quality of a lower-dose CT protocol for pediatric craniosynostosis implemented at our institution.

MATERIALS AND METHODS

The standard of care at our institution was modified to replace pediatric head CT protocols with a lower-dose CT protocol utilizing 100 kV, 5 mAs and iterative reconstruction. Study-ordered, protocol-utilized and radiation-dose indices were collected for studies performed with routine pediatric brain protocols (n=22) and with the lower-dose CT protocol (n=135). Two pediatric neuroradiologists evaluated image quality in a subset (n=50) of the lower-dose CT studies by scoring visualization of cranial structures, confidence of diagnosis and the need for more radiation dose.

RESULTS

During the 30-month period, the lower-dose CT protocol had high compliance, with 2/137 studies performed with routine brain protocols. With the lower-dose CT protocol, volume CT dose index (CTDI_vol) was 1.1 mGy for all patients (0-9 years old) and effective dose ranged from 0.06 to 0.22 mSv, comparable to a 4-view skull radiography examination. CTDI_vol was reduced by 98% and effective dose was reduced up to 67-fold. Confidence in diagnosing craniosynostosis was high and more radiation dose was considered unnecessary in all studies (n=50) by both radiologists.

CONCLUSION

Replacing the routine pediatric brain CT protocol with a lower-dose CT craniosynostosis protocol substantially reduced radiation exposure without compromising image quality or diagnostic confidence.

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.

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.

Recent Imaging Advancements for Lung Metastases in Children with Sarcoma

In children and adolescents affected by musculoskeletal sarcomas (both soft tissue and bone sarcomas), the presence of lung metastases is a frequent complication, that should be known since the patient's prognosis, as management, and treatment depend on it. During the staging phase, the detection of lung metastases should be sensitive and specific, and it should be carried out by minimizing the radiation exposure. To deal with this problem, imaging has reached important goals in recent years, thanks to the development of cone-beam CT or low-dose computed tomography, with some new iterative reconstruction methods, such as Veo and ASIR. Imaging is also fundamental for the possibility to perform lung biopsies under CT guidance, with less morbidity, less time-consumption, and shorter recovery time, compared to surgical biopsies.Moreover, important results have also been demonstrated in the treatment of lung metastases, due to the improvement of new mini-invasive image-guided percutaneous thermal ablation procedures, which proved to be safe and effective also in young patients.

Radiologic Assessment of Osteosarcoma Lung Metastases: State of the Art and Recent Advances

The lung is the most frequent site of osteosarcoma (OS) metastases, which are a critical point in defining a patient’s prognosis. Chest computed tomography (CT) represents the gold standard for the detection of lung metastases even if its sensitivity widely ranges in the literature since lung localizations are often atypical. ESMO guidelines represent one of the major references for the follow-up program of OS patients. The development of new reconstruction techniques, such as the iterative method and the deep learning-based image reconstruction (DLIR), has led to a significant reduction of the radiation dose with the low-dose CT. The improvement of these techniques has great importance considering the young-onset of the disease and the strict chest surveillance during follow-up programs. The use of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT is still controversial, while volume doubling time (VDT) and computer-aided diagnosis (CAD) systems are recent diagnostic tools that could support radiologists for lung nodules evaluation. Their use, well-established for other malignancies, needs to be further evaluated, focusing on OS patients.

Impact of increasing levels of adaptive statistical iterative reconstruction on image quality in oil-based postmortem CT angiography in coronary arteries

Introduction

Postmortem multi-detector computed tomography (PMCT) has become an important part in forensic imaging. Modern reconstruction techniques such as iterative reconstruction (IR) are frequently used in postmortem CT angiography (PMCTA). The image quality of PMCTA depends on the strength of IR. For this purpose, we aimed to investigate the impact of different advanced IR levels on the objective and subjective PMCTA image quality.

Material and methods

We retrospectively analyzed the coronary arteries of 27 human cadavers undergoing whole-body postmortem CT angiography between July 2017 and March 2018 in a single center. Iterative reconstructions of the coronary arteries were processed in five different level settings (0%; 30%; 50%; 70%; 100%) by using an adaptive statistical IR method. We evaluated the objective (contrast-to-noise ratio (CNR)) and subjective image quality in several anatomical locations.

Results

Our results demonstrate that the increasing levels of an IR technique have relevant impact on the image quality in PMCTA scans in forensic postmortem examinations. Higher levels of IR have led to a significant reduction of image noise and therefore to a significant improvement of objective image quality (+ 70%). However, subjective image quality is inferior at higher levels of IR due to plasticized image appearance.

Conclusion

Objective image quality in PMCTA progressively improves with increasing level of IR with the best CNR at the highest IR level. However, subjective image quality is best at low to medium levels of IR. To obtain a “classic” image appearance with optimal image quality, PMCTAs should be reconstructed at medium levels of IR.

The future of CT: deep learning reconstruction

There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstruction algorithm to be introduced, which harnesses advances in artificial intelligence (AI) and affordable supercomputer technology to achieve the previously elusive triad of high image quality, low radiation dose, and fast reconstruction speeds. The dose reductions achieved with DLR are redefining ultra-low-dose into the realm of plain radiographs whilst maintaining image quality. This review aims to demonstrate the advantages of DLR over other reconstruction methods in terms of dose reduction and image quality in addition to being able to tailor protocols to specific clinical situations. DLR is the future of CT technology and should be considered when procuring new scanners.

Effect of New Model-Based Iterative Reconstruction on Quantitative Analysis of Airway Tree by Computer-Aided Detection Software in Chest Computed Tomography

OBJECTIVE

Compared the performance of computer-aided detection (CAD) software for quantitative analysis of airway using computed tomography (CT) images reconstructed with versions of model-based iterative reconstruction (MBIR) that either balances spatial and density resolution (MBIRSTND) or prefers spatial resolution (MBIRRP20), and adaptive statistical iterative reconstruction (ASIR) with lung kernel.

METHODS

Thirty patients were included who were scanned for pulmonary disease using a routine dose multidetector CT system. Data were reconstructed with ASIR, MBIRSTND, and MBIRRP20. Airway dimensions from the 3 reconstructions were measured using an automated, quantitative CAD software designed to segment and quantify the bronchial tree automatically using a skeletonization algorithm. For each patient and reconstruction algorithm, the right middle lobe bronchus was selected as a representative for measuring the bronchial length of the matched airways. Two radiologists used a semiquantitative 5-point scale to rate the subjective image quality of MBIRSTND and MBIRRP20 reconstructions on airway trees analysis.

RESULTS

Algorithm impacts the measurement variability of bronchus length in chest CT, MBIRRP20 were the best, whereas ASIR were the worst (P < 0.05). In addition, the optimal reconstruction algorithm was found to be MBIRSTND for the airway trees being assessed about subjective noise and MBIRRP20 about bronchial end shows, and there were no significant differences in the continuity and completeness of bronchial wall, whereas ASIR performed inferiorly compared with them (P < 0.05).

CONCLUSIONS

Compared with ASIR, MBIRSTND, and MBIRRP20 from MBIRn algorithm potentially allow the desired airway quantification accuracy to be achieved on the performance of CAD, especially for MBIRRP20.

Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction

PURPOSE

To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.

METHOD

Pre and post-contrast abdominal CT images in 50 patients were reconstructed with 2 different algorithms: hybrid iterative reconstruction (hybrid IR: ASiR-V 50%) and DLIR (TrueFidelity). Standard deviation of attenuation in normal liver parenchyma was measured as the image noise on pre and post-contrast CT. The contrast-to-noise ratio (CNR) for the aorta, and the signal-to-noise ratio (SNR) of the liver were calculated on post-contrast CT. The overall image quality was graded on a 5-point scale ranging from 1 (poor) to 5 (excellent).

RESULTS

The image noise was significantly decreased by DLIR compared to hybrid-IR [hybrid IR, median 8.3 Hounsfield unit (HU) (interquartile range (IQR) 7.6-9.2 HU); DLIR, median 5.2 HU (IQR 4.6-5.8), P < 0.0001 for post-contrast CT]. The CNR and SNR were significantly improved by DLIR [CNR, median 4.5 (IQR 3.8-5.6) vs 7.3 (IQR 6.2-8.8), P < 0.0001; SNR, median 9.4 (IQR 8.3-10.1) vs 15.0 (IQR 13.2-16.4), P < 0.0001]. The overall image quality score was also higher for DLIR compared to hybrid-IR (hybrid IR 3.1 ± 0.6 vs DLIR 4.6 ± 0.5, P < 0.0001 for post-contrast CT).

CONCLUSIONS

Image noise, overall image quality, CNR and SNR for abdominal CT images are improved with DLIR compared to hybrid IR.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions

OBJECTIVE

To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT.

METHODS

This was a prospective study. 40 patients with hepatic lesions underwent abdominal CT using routine dose (120kV, noise index (NI) setting of 11 with automatic tube current modulation) in the arterial-phase (AP) and portal-phase (PP), and low dose (NI = 24) in the delayed-phase (DP). All images were reconstructed at 1.25 mm thickness using ASIR-V at 50% strength. In addition, images in DP were reconstructed using DLIR in high setting (DLIR-H). The CT value and standard deviation (SD) of hepatic parenchyma, spleen, paraspinal muscle and lesion were measured. The overall image quality includes subjective noise, sharpness, artifacts and diagnostic confidence were assessed by two radiologists blindly using a 5-point scale (1, unacceptable and 5, excellent). Dose between AP and DP was compared, and image quality among different reconstructions were compared using SPSS20.0.

RESULTS

Compared to AP, DP significantly reduced radiation dose by 76% (0.76 ± 0.09 mSv vs 3.18 ± 0.48 mSv), DLIR-H DP images had lower image noise (14.08 ± 2.89 HU vs 16.67 ± 3.74 HU, p < 0.001) but similar overall image quality score as the ASIR-V50% AP images (3.88 ± 0.34 vs 4.05 ± 0.44, p > 0.05). For the DP images, DLIR-H significantly reduced image noise in hepatic parenchyma, spleen, muscle and lesion to (14.77 ± 2.61 HU, 14.26 ± 2.67 HU, 14.08 ± 2.89 HU and 16.25 ± 4.42 HU) from (24.95 ± 4.32 HU, 25.42 ± 4.99 HU, 23.99 ± 5.26 HU and 27.01 ± 7.11) with ASIR-V50%, respectively (all p < 0.001) and improved image quality score (3.88 ± 0.34 vs 2.87 ± 0.53; p < 0.05).

CONCLUSION

DLIR-H significantly reduces image noise and generates images with clinically acceptable quality and diagnostic confidence with 76% dose reduction.

ADVANCES IN KNOWLEDGE

(1) DLIR-H yielded a significantly lower image noise, higher CNR and higher overall image quality score and diagnostic confidence than the ASIR-V50% under low signal conditions. (2) Our study demonstrated that at 76% lower radiation dose, the DLIR-H DP images had similar overall image quality to the routine-dose ASIR-V50% AP images.

Value of using adaptive statistical iterative reconstruction-V (ASIR-V) technology in pediatric head CT dose reduction

Background

With widespread use of pediatric head CT, it is critically important to protect patients from radiation hazards, using reduced dose CT techniques. In this regard, adaptive statistical iterative reconstruction-V (ASIR-V) algorithm can decrease image noise, generating CT images of reasonable diagnostic quality with less radiation. The objective of this study was radiation dose assessment, quantitative and qualitative evaluation of reduced dose pediatric head CT using ASIR-V 60% and 80% reconstruction.

Results

Retrospective analysis was performed on two groups of pediatric head CT examinations, a reduced dose CT examination group with ASIR-V reconstruction (ASIR group) (n = 27) and a standard dose CT examination group without ASIR reconstruction (non-ASIR group) (n = 14). The average effective dose (ED) of ASIR group was significantly lower than that of the non-ASIR group (1.04 ± 0.1 mS vs 3.48 ± 0.45 mS; p = 0.001). Quantitative analysis revealed comparable results of signal to noise ratio (SNR) and contrast to noise ratio (CNR) of ASIR and non-ASIR groups (p > 0.05). Qualitative evaluation of resulting images by two readers revealed comparable results of both ASIR and non-ASIR groups (p > 0.05) with excellent inter-reader agreement (κ = 0.97). Both quantitative and qualitative assessment demonstrated better ASIR-V 80% than ASIR-V 60% reconstructed images.

Conclusion

ASIR-V algorithm is a promising technology for effective dose reduction of pediatric head CT with preservation of diagnostic image quality.

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

AIM

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Ultra-low-dose chest CT in adult patients with cystic fibrosis using a third-generation dual-source CT scanner

INTRODUCTION

Chest computed tomography (CT) examinations are performed routinely in some cystic fibrosis (CF) centers in order to evaluate lung disease progression in CF patients. Continuous CT technological advancement in theory could allows a lower radiation exposure of CF patients during chest CT examinations without an image quality reduction, and this could become increasingly important over time in order to reduce the cumulative radiation dose effects given the continuous increase of CF patients predicted median survival.

OBJECTIVE

The aim of this study was to compare objective and subjective image quality and radiation dose between low-dose chest CT examinations performed in adult CF patients using a third-generation DSCT scanner and a 64-slices single-source CT (SSCT) scanner.

MATERIALS AND METHODS

Between January 2016 and August 2019, 81 CF patients underwent low-dose chest CT examinations using both a 64-slices SSCT scanner (2016-2017) and a third-generation DSCT scanner (2018-2019). Objective image noise standard deviation (INSD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall subjective image quality (OSIQ), subjective image noise (SIN), subjective evaluation of streaking artifacts (SA), movement artifacts (MA) and edge resolution (ER), dose-length product (DLP), volume computed tomography dose index (CTDIvol) and effective radiation dose (ERD) were compared between DSCT and SSCT examinations. DSCT examinations consisted in spiral inspiratory end expiratory acquisitions. SSCT examinations consisted in spiral inspiratory acquisitions and five axial expiratory ones.

RESULTS

DSCT protocol showed statistically significant lower spiral inspiratory phase mean DLP, CTDIvol and ERD than SSCT protocol, with a 25% DLP, CTDIvol and ERD reduction. DSCT protocol showed statistically significant higher overall (inspiratory and expiratory phases) mean DLP, CTDIvol and ERD than SSCT protocol, with a 40% DLP, CTDIvol and ERD increase. Objective image quality (INSD, SNR and CNR) and SIN differences were not statistically significant, but subjective evaluation of DSCT images showed statistically significant better OSIQ and ER, as well as statistically significant lower SA and MA with respect to SSCT images.

CONCLUSIONS

To our knowledge, this is the first study evaluating chest CT image quality and radiation dose in adult CF patients using a third-generation DSCT scanner, and it showed that technological advancements could be used in order to reduce radiation exposure of volumetric examinations. The spiral inspiratory dose reduction can be obtained with concomitant improvements in subjective image quality with comparable objective quality. This will probably allow a wider use of this imaging modality in order to assess bronchiectasis and will probably foster spiral expiratory acquisition for small airways disease evaluation.

The effect of patellofemoral pain syndrome on patellofemoral joint kinematics under upright weight-bearing conditions

Patellofemoral pain (PFP) is commonly caused by abnormal pressure on the knee due to excessive load while standing, squatting, or going up or down stairs. To better understand the pathophysiology of PFP, we conducted a noninvasive patellar tracking study using a C-arm computed tomography (CT) scanner to assess the non-weight-bearing condition at 0° knee flexion (NWB0°) in supine, weight-bearing at 0° (WB0°) when upright, and at 30° (WB30°) in a squat. Three-dimensional (3D) CT images were obtained from patients with PFP (12 women, 6 men; mean age, 31 ± 9 years; mean weight, 68 ± 9 kg) and control subjects (8 women, 10 men; mean age, 39 ± 15 years; mean weight, 71 ± 13 kg). Six 3D-landmarks on the patella and femur were used to establish a joint coordinate system (JCS) and kinematic degrees of freedom (DoF) values on the JCS were obtained: patellar tilt (PT, °), patellar flexion (PF, °), patellar rotation (PR, °), patellar lateral-medial shift (PT_x, mm), patellar proximal-distal shift (PT_y, mm), and patellar anterior-posterior shift (PT_z, mm). Tests for statistical significance (p < 0.05) showed that the PF during WB30°, the PT_y during NWB0°, and the PT_z during NWB0°, WB0°, and WB30° showed clear differences between the patients with PFP and healthy controls. In particular, the PF during WB30° (17.62°, extension) and the PT_z during WB0° (72.5‬0 mm, posterior) had the largest rotational and translational differences (JCS Δ = patients with PFP—controls), respectively. The JCS coordinates with statistically significant difference can serve as key biomarkers of patellar motion when evaluating a patient suspected of having PFP. The proposed method could reveal diagnostic biomarkers for accurately identifying PFP patients and be an effective addition to clinical diagnosis before surgery and to help plan rehabilitation strategies.

Impact of various iodine concentrations of iohexol and iodixanol contrast media on image reconstruction techniques in a vascular-specific contrast media phantom: quantitative and qualitative image quality assessment

PURPOSE

The aim of our study is to investigate the impact of iodine quantification on image reconstruction when employing a vascular-specific contrast media phantom with varying iodine concentrations.

MATERIALS AND METHODS

A 30-cm phantom simulating arterial and venous blood vessel diameters was manufactured. Small (9 mm) and medium (12 mm) cylinders contained iodine concentrations from 10 to 100% while large (21 mm) cylinders were in quartiles from 25 to 100% diluted in blood equivalent medium. Each phantom was filled with either iohexol 350 mgI/mL (Group A) or iodixanol 320 mgI/mL (Group B) and then scanned separately. For each group, tube potential (80-140 kVp) and current (50-400 mAs) were changed and all image series were reconstructed with filtered back projection (FBP), hybrid-based iterative reconstruction (HBIR) and model-based iterative reconstruction (MBIR). Mean opacification was measured in all groups. All data were compared employing an independent t test and Pearson's correlation. Visual grading characteristic (VGC) and Cohens' kappa analyses were performed.

RESULTS

At 80 kVp, mean opacification using HBIR was significantly higher in Group B (2165 ± 1108 HU) than in Group A (2040 ± 1036 HU) (p < 0.009). At 140 kVp, MBIR and HBIR were greater in Group A (1704 ± 1033 HU and 1685 ± 1023 HU) versus Group B (1567 ± 1036 HU and 1567 ± 1034 HU) (p < 0.022). CNR using FBP, HBIR and MBIR was higher in Group B (46 ± 42 HU, 70 ± 163 HU and 83 ± 74 HU, respectively) than in Group A (43 ± 39 HU, 174 ± 130 HU and 80 ± 65 HU, respectively) (p < 0.0001-0.035). Qualitative image analysis demonstrated no difference in Cohen's kappa analysis. VGC was higher in Group A at all image reconstruction groups.

CONCLUSION

Iohexol outperforms iodixanol in observer performance when assessing image reconstruction techniques and iodine concentrations in a vascular-specific contrast media phantom.

Image Quality and Radiation Dose in CT Venography Using Model-Based Iterative Reconstruction at 80 kVp versus Adaptive Statistical Iterative Reconstruction-V at 70 kVp

Objective

To compare the objective and subjective image quality indicators and radiation doses of computed tomography (CT) venography performed using model-based iterative reconstruction (MBIR) at 80 kVp and adaptive statistical iterative reconstruction (ASIR)-V at 70 kVp.

Materials and Methods

Eighty-three patients who had undergone CT venography of the lower extremities with MBIR at 80 kVp (Group A; 21 men and 20 women; mean age, 55.5 years) or ASIR-V at 70 kVp (Group B; 18 men and 24 women; mean age, 57.3 years) were enrolled. Two radiologists retrospectively evaluated the objective (vascular enhancement, image noise, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and subjective (quantum mottle, delineation of contour, venous enhancement) image quality indicators at the inferior vena cava and femoral and popliteal veins. Clinical information, radiation dose, reconstruction time, and objective and subjective image quality indicators were compared between groups A and B.

Results

Vascular enhancement, SNR, and CNR were significantly greater in Group B than in Group A (p ≤ 0.015). Image noise was significantly lower in Group B (p ≤ 0.021), and all subjective image quality indicators, except for delineation of vein contours, were significantly better in Group B (p ≤ 0.021). Mean reconstruction time was significantly shorter in Group B than in Group A (1 min 43 s vs. 131 min 1 s; p < 0.001). Clinical information and radiation dose were not significantly different between the two groups.

Conclusion

CT venography using ASIR-V at 70 kVp was better than MBIR at 80 kVp in terms of image quality and reconstruction time at similar radiation doses.

Effect of iterative reconstruction techniques on image quality in low radiation dose chest CT: a phantom study

PURPOSE

We aimed to evaluate the quality of chest computed tomography (CT) images obtained with low-dose CT using three iterative reconstruction (IR) algorithms.

METHODS

Two 64-detector spiral CT scanners (HDCT and iCT) were used to scan a chest phantom containing 6 ground-glass nodules (GGNs) at 11 radiation dose levels. CT images were reconstructed by filtered back projection or three IR algorithms. Reconstructed images were analyzed for CT values, average noise, contrast-to-noise ratio (CNR) values, subjective image noise, and diagnostic acceptability of the GGNs. Repeated-measures analysis of variance was used for statistical analyses.

RESULTS

Average noise decreased and CNR increased with increasing radiation dose when the same reconstruction algorithm was applied. Average image noise was significantly lower when reconstructed with MBIR than with iDOSE4 at the same low radiation doses. The two radiologists showed good interobserver consistency in image quality with kappa 0.83. A significant relationship was found between image noise and diagnostic acceptability of the GGNs.

CONCLUSION

Three IR algorithms are able to reduce the image noise and improve the image quality of low-dose CT. In the same radiation dose, the low-dose CT image quality reconstructed with MBIR algorithms is better than that of other IR algorithms.

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

BACKGROUND

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

A phantom study of the performance of model-based iterative reconstruction in low-dose chest and abdominal CT: When are benefits maximized?

INTRODUCTION

The aim of this study was to assess and compare the effects of CT image reconstruction techniques on low-dose CT image quality using phantoms.

METHODS

Anthropomorphic torso and spatial/contrast-resolution phantoms were scanned at decreasing tube currents between 400 and 10 mA. CT thorax and abdomen/pelvis series were reconstructed with filtered back projection (FBP) alone, combined 40% adaptive statistical iterative reconstruction & FBP (ASIR40), and model-based iterative reconstruction (MBIR) [(resolution-preference 05 (RP05) and RP20 in the thorax and RP05 and noise-reduction 05 (NR05) in the abdomen)]. Two readers rated image quality quantitatively and qualitatively.

RESULTS

In thoracic CT, objective image noise on MBIR RP05 data sets outperformed FBP at 200, 100, 50 and 10 mA and outperformed ASIR40 at 50 and 10 mA (p < 0.001). MBIR RP20 outperformed FBP at 50 and 10 mA and outperformed ASIR40 at 10 mA (p < 0.001). Compared with both FBP and ASIR40, MBIR RP05 demonstrated significantly better signal-to-noise ratio (SNR) at 10 mA. In abdomino-pelvic CT, MBIR RP05 and NR05 outperformed FBP and ASIR at all tube current levels for objective image noise. NR05 demonstrated greater SNR at 200, 100, 50 and 10 mA and RP05 demonstrated greater SNR at 50 and 10 mA compared with both FBP and ASIR. MBIR images demonstrated better subjective image quality scores. Spatial resolution, low-contrast detectability and contrast-to-noise ratio (CNR) were comparable between image reconstruction techniques.

CONCLUSION

CTs reconstructed with MBIR have lower image noise and improved image quality compared with FBP and ASIR. These effects increase with reduced radiation exposure confirming optimal use for low-dose CT imaging.

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.

Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer

OBJECTIVES

To investigate whether adaptive statistical iterative reconstruction (ASIR), a hybrid iterative CT image reconstruction algorithm, affects radiomics feature quantification in primary colorectal cancer compared to filtered back projection. Additionally, to establish whether radiomics from single-slice analysis undergo greater change than those from multi-slice analysis.

METHODS

Following review board approval, contrast-enhanced CT studies from 32 prospective primary colorectal cancer patients were reconstructed with 20% ASIR level increments, from 0 to 100%. Radiomics analysis was applied to single-slice and multi-slice regions of interest outlining the tumour: 70 features, including statistical (first-, second- and high-order) and fractal radiomics, were generated per dataset. The effect of ASIR was calculated by means of multilevel linear regression.

RESULTS

Twenty-eight CT datasets were suitable for analysis. Incremental ASIR levels determined a significant change (p < 0.001) in most statistical radiomics features, best described by a simple linear relationship. First-order statistical features, including mean, standard deviation, skewness, kurtosis, energy and entropy, underwent a relatively small change in both single-slice and multi-slice analysis (median standardised effect size B = 0.08). Second-order statistical features, including grey-level co-occurrence and difference matrices, underwent a greater change in single-slice analysis (median B = 0.36) than in multi-slice analysis (median B = 0.13). Fractal features underwent a significant change only in single-slice analysis (median B = 0.49).

CONCLUSIONS

Incremental levels of ASIR affect significantly CT radiomics quantification in primary colorectal cancer. Second-order statistical and fractal features derived from single-slice analysis undergo greater change than those from multi-slice analysis.

KEY POINTS

• Incremental levels of ASIR determine a significant change in most statistical (first-, second- and high-order) CT radiomics features measured in primary colorectal cancer, best described by a linear relationship. • First-order statistical features undergo a small change, both from single-slice and multi-slice radiomics analyses. • Most second-order statistical features undergo a greater change in single-slice analysis than in multi-slice analysis. Fractal features are only affected in single-slice analysis.

The Application of a New Model-Based Iterative Reconstruction in Low-Dose Upper Abdominal CT

RATIONALE AND OBJECTIVES

To compare upper abdominal computed tomography (CT) image quality of new model-based iterative reconstruction (MBIR) with low-contrast resolution preference (MBIR_NR40), conventional MBIR (MBIR_c), and adaptive statistical iterative reconstruction (ASIR) at low dose with ASIR at routine-dose.

MATERIALS AND METHODS

Study included phantom and 60 patients who had initial and follow-up CT scans. For patients, the delay phase was acquired at routine-dose (noise index = 10 HU) for the initial scan and low dose (noise index = 20 HU) for the follow-up. The low-dose CT was reconstructed with 40% and 60% ASIR, MBIR_c, and MBIR_NR40, while routine-dose CT was reconstructed with 40% ASIR. CT value and noise measurements of the subcutaneous fat, back muscle, liver, and spleen parenchyma were compared using one-way ANOVA. Two radiologists used semiquantitative 7-scale (-3 to +3) to rate image quality and artifacts.

RESULTS

The phantom study revealed superior low-contrast resolution with MBIR_NR40. For patient scans, the CT dose index for the low-dose CT was 3.00 ± 1.32 mGy, 75% lower than the 11.90 ± 4.75 mGy for the routine-dose CT. Image noise for the low-dose MBIR_NR40 images was significantly lower than the low-dose MBIR_c and ASIR images, and routine-dose ASIR images (p < 0.05). Subjective ratings showed higher image quality for low-dose MBIR_NR40, with lower noise, better low-contrast resolution for abdominal structures, and finer lesion contours than those of low-dose MBIR_c and ASIR images, and routine-dose ASIR images (p < 0.05).

CONCLUSION

MBIR_NR40 with low-contrast resolution preference provides significantly lower noise and better image quality than MBIR_c and ASIR in low-dose abdominal CT; significantly better objective and subjective image quality than the routine-dose ASIR with 75% dose reduction.

Impact of CT reconstruction algorithm on auto-segmentation performance

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

Comparison of standard-dose and half-dose dual-source abdominopelvic CT scans for evaluation of acute abdominal pain

Background

With the increasing number of computed tomography (CT) scans used for evaluation of acute abdominal pain, patient radiation exposure has increased rapidly.

Purpose

To determine whether the diagnostic performance of half-dose abdominopelvic CT is non-inferior to that of standard-dose CT for patients with acute abdominal pain.

Material and Methods

Ninety-eight patients with acute abdominal pain underwent dual-source abdominopelvic CT. Three sets of CT images were reconstructed: standard-dose filtered back projection (FBP); half-dose FBP; and half-dose sinogram-affirmed iterative reconstruction (SAFIRE3). Diagnostic performance of the standard-dose scan was compared with that of the half-dose scans by using a non-inferiority test with a 10% margin. The overall image quality was subjectively measured.

Results

Diagnostic performance for overall disease diagnosis with half-dose scans (area under the receiver operating characteristic curve [AUC] = 0.835 for FBP, 0.881 for SAFIRE3) was non-inferior to that of standard-dose FBP (AUC = 0.891) (95% confidence interval lower limit difference = −5.6% [half-dose FBP], −1.2% [half-dose SAFIRE3]). The diagnostic sensitivity for detection of neoplastic disease was lower with half-dose (75.0%) than with standard-dose FBP (91.7%). Effective dose and dose-length product with standard-dose imaging were 7.99 ± 2.55 mSv and 533.1 ± 170.3 mGy·cm, respectively; those of half-dose imaging were 3.99 ± 1.28 mSv and 266.6 ± 85.2 mGy·cm, respectively. The image quality was lower with half-dose than with standard-dose FBP scans ( P < 0.01).

Conclusion

Diagnostic performance of half-dose CT is non-inferior to that of standard-dose scan for evaluation of acute abdominal pain, despite inferior image quality.

Known-component 3D image reconstruction for improved intraoperative imaging in spine surgery: A clinical pilot study

PURPOSE

Intraoperative imaging plays an increased role in support of surgical guidance and quality assurance for interventional approaches. However, image quality sufficient to detect complications and provide quantitative assessment of the surgical product is often confounded by image noise and artifacts. In this work, we translated a three-dimensional model-based image reconstruction (referred to as "Known-Component Reconstruction," KC-Recon) for the first time to clinical studies with the aim of resolving both limitations.

METHODS

KC-Recon builds upon a penalized weighted least-squares (PWLS) method by incorporating models of surgical instrumentation ("known components") within a joint image registration-reconstruction process to improve image quality. Under IRB approval, a clinical pilot study was conducted with 17 spine surgery patients imaged under informed consent using the O-arm cone-beam CT system (Medtronic, Littleton MA) before and after spinal instrumentation. Volumetric images were generated for each patient using KC-Recon in comparison to conventional filtered backprojection (FBP). Imaging performance prior to instrumentation ("preinstrumentation") was evaluated in terms of soft-tissue contrast-to-noise ratio (CNR) and spatial resolution. The quality of images obtained after the instrumentation ("postinstrumentation") was assessed by quantifying the magnitude of metal artifacts (blooming and streaks) arising from pedicle screws. The potential low-dose advantages of the algorithm were tested by simulating low-dose data (down to one-tenth of the dose of standard protocols) from images acquired at normal dose.

RESULTS

Preinstrumentation images (at normal clinical dose and matched resolution) exhibited an average 24.0% increase in soft-tissue CNR with KC-Recon compared to FBP (N = 16, P = 0.02), improving visualization of paraspinal muscles, major vessels, and other soft-tissues about the spine and abdomen. For a total of 72 screws in postinstrumentation images, KC-Recon yielded a significant reduction in metal artifacts: 66.3% reduction in overestimation of screw shaft width due to blooming (P < 0.0001) and reduction in streaks at the screw tip (65.8% increase in attenuation accuracy, P < 0.0001), enabling clearer depiction of the screw within the pedicle and vertebral body for an assessment of breach. Depending on the imaging task, dose reduction up to an order of magnitude appeared feasible while maintaining soft-tissue visibility and metal artifact reduction.

CONCLUSIONS

KC-Recon offers a promising means to improve visualization in the presence of surgical instrumentation and reduce patient dose in image-guided procedures. The improved soft-tissue visibility could facilitate the use of cone-beam CT to soft-tissue surgeries, and the ability to precisely quantify and visualize instrument placement could provide a valuable check against complications in the operating room (cf., postoperative CT).