Submillisievert chest CT with filtered back projection and iterative reconstruction techniques

Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

Purpose

This study aimed to evaluate the feasibility of ultrafast (2 min) cervical spine MRI protocol using a deep learning-assisted 3D iterative image enhancement (DL-3DIIE) system, compared to a conventional MRI protocol (6 min 14s).

Patients and Methods

Fifty-one patients were recruited and underwent cervical spine MRI using conventional and ultrafast protocols. A DL-3DIIE system was applied to the ultrafast protocol to compensate for the spatial resolution and signal-to-noise ratio (SNR) of images. Two radiologists independently assessed and graded the quality of images from the dimensions of artifacts, boundary sharpness, visibility of lesions and overall image quality. We recorded the presence or absence of different pathologies. Moreover, we examined the interchangeability of the two protocols by computing the 95% confidence interval of the individual equivalence index, and also evaluated the inter-protocol intra-observer agreement using Cohen's weighted kappa.

Results

Ultrafast-DL-3DIIE images were significantly better than conventional ones for artifacts and equivalent for other qualitative features. The number of cases with different kinds of pathologies was indistinguishable based on the MR images from ultrafast-DL-3DIIE and conventional protocols. With the exception of disc degeneration, the 95% confidence interval for the individual equivalence index across all variables did not surpass 5%, suggesting that the two protocols are interchangeable. The kappa values of these evaluations by the two radiologists ranged from 0.65 to 0.88, indicating good-to-excellent agreement.

Conclusion

The DL-3DIIE system enables 67% spine MRI scan time reduction while obtaining at least equivalent image quality and diagnostic results compared to the conventional protocol, suggesting its potential for clinical utility.

Deep learning-based reconstruction can improve the image quality of low radiation dose head CT

OBJECTIVES

To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images.

METHODS

This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured.

RESULTS

The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively.

CONCLUSION

DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time.

KEY POINTS

• For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.

Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT

RATIONALE AND OBJECTIVES

To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR).

MATERIALS AND METHODS

An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT 1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR; ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR). Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images as reference standards, ULDCT image qualities were subjectively scored on a 5-point scale (1 = substantially inferior to LDCT-2, 3 = comparable to LDCT-2, 5 = comparable to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR, MBIR, and DLR; the subjective acceptability was ranked.

RESULTS

Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1, LDCT-2, and ULDCT-HIR (p < 0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR (p < 0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded the highest NPS peak frequency and TTF_50% for high-contrast object. In clinical ULDCT images, the subjective acceptability of DLR was higher than of HIR and MBIR (p < 0.01).

CONCLUSION

DLR optimized for lung CT improves image quality and provides possible greater dose optimization opportunity than HIR and MBIR.

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.

Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study

BACKGROUND. Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images with standard-dose iterative reconstruction (IR) images or explored application of DLR to low-tube-voltage scanning in children. OBJECTIVE. The purpose of this study was to assess whether DLR can be used to reduce radiation dose while maintaining diagnostic image quality in comparison with hybrid IR (HIR) and model-based IR (MBIR) for low-tube-voltage pediatric CT. METHODS. This retrospective study included children 6 years old or younger who underwent contrast-enhanced 80-kVp CT with a standard-dose or lower-dose protocol. Standard images were reconstructed with HIR, and lower-dose images were reconstructed with HIR, MBIR, and DLR. Size-specific dose estimate (SSDE) was calculated for both protocols. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantified. Two radiologists independently evaluated noise magnitude, noise texture, streak artifact, edge sharpness, and overall quality. Interreader agreement was assessed, and mean values were calculated. To evaluate task-based object detection performance, a phantom was imaged with 80-kVp CT at six doses (SSDE, 0.6-5.3 mGy). Detectability index (d') was calculated from the noise power spectrum and task-based transfer function. Reconstruction methods were compared. RESULTS. Sixty-five children (mean age, 25.0 ± 25.2 months) who underwent CT with standard- (n = 31) or lower-dose (n = 34) protocol were included. SSDE was 54% lower for the lower-dose than for the standard-dose group (1.9 ± 0.4 vs 4.1 ± 0.8 mGy). Lower-dose DLR and MBIR yielded lower image noise and higher SNR and CNR than standard-dose HIR (p < .05). Interobserver agreement on subjective features ranged from a kappa coefficient of 0.68 to 0.78. The readers subjectively scored noise texture, edge sharpness, and overall quality lower for lower-dose MBIR than for standard-dose HIR (p < .001), though higher for lower-dose DLR than for standard-dose HIR (p < .001). In the phantom, DLR provided higher d' than HIR and MBIR at each dose. Object detectability was greater for 2.0-mGy DLR than for 4.0-mGy HIR for low-contrast (3.67 vs 3.57) and high-contrast (1.20 vs 1.04) objects. CONCLUSION. Compared with IR algorithms, DLR results in substantial dose reduction with preserved or even improved image quality for low-tube-voltage pediatric CT. CLINICAL IMPACT. Use of DLR at 80 kVp allows greater dose reduction for pediatric CT than do current IR techniques.

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study

OBJECTIVES

The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting.

METHODS

Artificial ground-glass nodules (6 mm and 10 mm diameters, -660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements.

RESULTS

DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement.

CONCLUSIONS

DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT.

ADVANCES IN KNOWLEDGE

DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

The effect of a post-scan processing denoising system on image quality and morphometric analysis

PURPOSE

MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelligent Quick Magnetic Resonance (iQMR), on MR image quality and brain morphometric analysis.

METHODS

We used 1.5T MP-RAGE MR images acquired from the Alzheimer's Disease Neuroimaging Initiative 1 database. The images of 21 subjects were used for cross-sectional analysis and 15 for longitudinal analysis. In the longitudinal analysis, two timepoints over a 2-year interval were used. Each subject was scanned twice at each timepoint. MR images processed with and without the denoising system were compared both visually and objectively using FreeSurfer cortical thickness analysis.

RESULTS

The denoising system reduced the noise with good white-gray matter contrast (noise: p < 0.001; contrast: p = 0.49). The mean intraclass correlation coefficients (ICCs) of cortical thickness were slightly better in the images processed with the denoising system (0.739/0.859/0.883; Gaussian smoothing kernel of full width at half maximum = 0/10/20) compared with the unprocessed images (0.718/0.854/0.880). In the longitudinal analysis, the mean ICCs of symmetrized percent change improved in images processed with the denoising system (0.202/0.349/0.431) compared with the unprocessed images (0.167/0.325/0.404). In addition, the detectability of significant cortical thickness atrophy improved with denoising.

CONCLUSION

We confirm that the AI-based denoising system could effectively reduce the noise while retaining the contrast. We also confirm the improvement of the reliability and detectability of brain morphometric analysis with the denoising system.

Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations

Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive pediatric population. The image quality of low-dose CT can be severely degraded by increased image noise with filtered back projection (FBP) reconstruction. Iterative reconstruction (IR) techniques partially resolve the trade-off relationship between noise and radiation dose but still suffer from degraded noise texture and low-contrast detectability at considerably low-dose settings. Furthermore, sophisticated model-based IR usually requires a long reconstruction time, which restricts its clinical usability. With recent advances in artificial intelligence technology, deep learning-based reconstruction (DLR) has been introduced to overcome the limitations of the FBP and IR approaches and is currently available clinically. DLR incorporates convolutional neural networks-which comprise multiple layers of mathematical equations-into the image reconstruction process to reduce image noise, improve spatial resolution, and preserve preferable noise texture in the CT images. For DLR development, numerous network parameters are iteratively optimized through an extensive learning process to discriminate true attenuation from noise by using low-dose training and high-dose teaching image data. After rigorous validations of network generalizability, the DLR engine can be used to generate high-quality images from low-dose projection data in a short reconstruction time in a clinical environment. Application of the DLR technique allows substantial dose reduction in pediatric CT performed for various clinical indications while preserving the diagnostic image quality. The authors present an overview of the basic concept, technical principles, and image characteristics of DLR and its clinical feasibility for low-dose pediatric CT. ^©RSNA, 2021.

Effect of energy level on the spatial resolution and noise frequency characteristics of virtual monochromatic images: a phantom experiment using four types of CT scanners

PURPOSE

The purpose of the study is to evaluate the effect of energy level on the modulation transfer functions (MTF) and noise power spectra (NPS) of virtual monochromatic images (VMIs) obtained using four types of computed-tomographic (CT) scanners: Revolution, SOMATOM, IQon, and Aquilion.

MATERIALS AND METHODS

VMIs were obtained at 70, 60, and 50 kiloelectron volts (keV), and also at the lowest keV available in each scanner. We evaluated the MTF and NPS in the VMIs obtained at each keV.

RESULTS

No significant effect of the energy level on the MTF was observed in IQon, whereas the spatial resolution decreased as the energy level decreased in the other types of scanners. The NPS curves tended to increase as the energy levels decreased with three types of scanners other than Aquilion.

CONCLUSION

The spatial resolution and noise frequency characteristics of VMIs may be affected by the energy level, and the effects of energy level on these characteristics differ depending on the type of CT scanners.

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

Objective

To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis.

Materials and Methods

Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively.

Results

With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT.

Conclusion

The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

Evaluation of three-dimensional iterative image reconstruction in virtual monochromatic imaging at 40 kilo-electron volts: phantom and clinical studies to assess the image noise and image quality in comparison with other reconstruction techniques

OBJECTIVE

The purpose of this study was to evaluate the image quality in virtual monochromatic imaging (VMI) at 40 kilo-electron volts (keV) with three-dimensional iterative image reconstruction (3D-IIR).

METHODS

A phantom study and clinical study (31 patients) were performed with dual-energy CT (DECT). VMI at 40 keV was obtained and the images were reconstructed using filtered back projection (FBP), 50% adaptive statistical iterative reconstruction (ASiR), and 3D-IIR. We conducted subjective and objective evaluations of the image quality with each reconstruction technique.

RESULTS

The image contrast-to-noise ratio and image noise in both the clinical and phantom studies were significantly better with 3D-IIR than with 50% ASiR, and with 50% ASiR than with FBP (all, p < 0.05). The standard deviation and noise power spectra of the reconstructed images decreased in the order of 3D-IIR to 50% ASiR to FBP, while the modulation transfer function was maintained across the three reconstruction techniques. In most subjective evaluations in the clinical study, the image quality was significantly better with 3D-IIR than with 50% ASiR, and with 50% ASiR than with FBP (all, p < 0.001). Regarding the diagnostic acceptability, all images using 3D-IIR were evaluated as being fully or probably acceptable.

CONCLUSIONS

The quality of VMI at 40 keV is improved by 3D-IIR, which allows the image noise to be reduced and structural details to be maintained.

ADVANCES IN KNOWLEDGE

The improvement of the image quality of VMI at 40 keV by 3D-IIR may increase the subjective acceptance in the clinical setting.

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.

Detection of pulmonary nodules: a clinical study protocol to compare ultra-low dose chest CT and standard low-dose CT using ASIR-V

INTRODUCTION

Lung cancer screening in individuals at risk has been recommended by various scientific institutions. One of the main concerns for CT screening is repeated radiation exposure, with the risk of inducing malignancies in healthy individuals. Therefore, lowering the radiation dose is one of the main objectives for radiologists. The aim of this study is to demonstrate that an ultra-low dose (ULD) chest CT protocol, using recently introduced hybrid iterative reconstruction (ASiR-V, GE medical Healthcare, Milwaukee, Wisconsin, USA), is as performant as a standard 'low dose' (LD) CT to detect non-calcified lung nodules ≥4 mm.

METHODS AND ANALYSIS

The total number of patients to include is 150. Those are referred for non-enhanced chest CT for detection or follow-up of lung nodule and will undergo an additional unenhanced ULD CT acquisition, the dose of which is on average 10 times lower than the conventional LD acquisition. Total dose of the entire exam (LD+ULD) is lower than the French diagnostic reference level for a chest CT (6.65 millisievert). ULD CT images will be reconstructed with 50% and 100% ASiR-V and LD CT with 50%. The three sets of images will be read in random order by two pair of radiologists, in a blind test, where patient identification and study outcomes are concealed. Detection rate (sensitivity) is the primary outcome. Secondary outcomes will include concordance of nodule characteristics; interobserver reproducibility; influence of subjects' characteristics, nodule location and nodule size; and concordance of emphysema, coronary calcifications evaluated by visual scoring and bronchial alterations between LD and ULD CT. In case of discordance, a third radiologist will arbitrate.

ETHICS AND DISSEMINATION

The study was approved by the relevant ethical committee. Each study participant will sign an informed consent form.

TRIAL REGISTRATION NUMBER

NCT03305978; Pre-results.

Effect of a New Model-Based Reconstruction Algorithm for Evaluating Early Peripheral Lung Cancer With Submillisievert Chest Computed Tomography

OBJECTIVE

The aim of this study was to compare a new model-based iterative reconstruction algorithm with either spatial and density resolution balance (MBIRSTND) or spatial resolution preference (MBIRRP20) with the adaptive statistical iterative reconstruction (ASIR) in evaluating early small peripheral lung cancer (SPLC) with submillisievert chest computed tomography (CT).

METHODS

Low-contrast and spatial resolutions were assessed in a phantom and with 30 pathologically confirmed SPLC patients. Images were reconstructed using 40% ASIR, MBIRSTND, and MBIRRP20. Computed tomography value and image noise were measured by placing the regions of interest on back muscle and subcutaneous fat at 3 levels. Two radiologists used a 4-point scale (1, worst, and 4, best) to rate subjective image quality in 3 aspects: image noise, nodule imaging signs, and nodule internal clarity.

RESULTS

The phantom study revealed an improved detectability of low-contrast targets and small objects for MBIRSTND and MBIRRP20 compared with ASIR. The effective dose for patient scans was 0.88 ± 0.83 mSv. There was no significant difference in CT value between the 3 reconstructions (P > 0.05), but MBIRSTND and MBIRRP20 significantly reduced image noise compared with ASIR (P < 0.05): 15.69 ± 1.83 HU and 29.97 ± 3.84 HU versus 51.06 ± 11.02 HU in the back muscle, and 15.96 ± 3.07 HU and 27.37 ± 3.88 HU versus 38.04 ± 8.87 HU in subcutaneous fat, respectively. Among the 3 reconstructions, MBIRSTND was the best in reducing image noise and identifying the internal compositions of cancer nodules, and MBIRRP20 was the best in analyzing the internal and external signs of pulmonary nodules.

CONCLUSIONS

Submillisievert chest CT reconstructed with MBIRSTND and MBIRRP20 provides superior images for the detailed analyses of SPLC compared with ASIR.

Quality evaluation of image-based iterative reconstruction for CT: Comparison with hybrid iterative reconstruction

The purpose of this study is to evaluate the physical image quality of a commercially available image‐based iterative reconstruction (IIR) system for two object contrasts to resemble a soft tissue (60 HU) and an enhanced vessel (270 HU), and compare the results with those of filtered back projection (FBP) and iterative reconstruction (IR). A 192‐slice computed tomography (CT) scanner was used for data acquisitions. IIR images were processed from the FBP images. Task‐based in‐plane transfer function (TTF) and slice sensitivity profile (SSP_task) were measured from rod objects inside of a 25‐cm diameter water phantom at four dose levels (2.5, 5, 10, and 20 mGy). Noise power spectrum (NPS) was measured from the water‐only part. System performance (SP) function was calculated as TTF²/NPS over FBP, IR, and IIR for comparison. In addition, an image subtraction was performed using images of rod objects, a bar‐pattern phantom, and a clinical abdomen case to observe the noise reduction performance of IIR. As a results, IIR mostly preserved TTF and SSP_task of FBP, whereas IR exhibited enhanced TTF at 10 and 20 mGy for 60 HU contrast and at all doses for 270 HU contrast. SP of IIR at 2.5, 5, 10 mGy (half doses) were similar to those of FBP at 5, 10, 20 mGy, respectively. IR exhibited enhanced SP at medium to high frequencies. The subtracted images showed weak remained edge signals in the bar‐pattern and abdominal images. In conclusion, IIR uniformly improved the task‐based image quality of FBP over the entire frequency range, whereas IR improved the characteristics over medium to high frequencies. The dose reduction potential of IIR estimated from SP is approximately 50%, when allowing the slight signal reductions.

Ultra-low-dose chest computed tomography for interstitial lung disease using model-based iterative reconstruction with or without the lung setting

The aim of this study was to assess the effects of reconstruction on the image quality and quantitative analysis for interstitial lung disease (ILD) using filtered back projection (FBP) and model-based iterative reconstruction (MBIR) with the lung setting and the conventional setting on ultra-low-dose computed tomography (CT).Fifty-two patients with known ILD were prospectively enrolled and underwent CT at an ultra-low dose (0.18 ± 0.02 mSv) and a standard dose (7.01 ± 2.66 mSv). Ultra-low-dose CT was reconstructed using FBP (uFBP) and MBIR with the lung setting (uMBIR-Lung) and the conventional setting (uMBIR-Stnd). Standard-dose CT was reconstructed using FBP (sFBP). Three radiologists subjectively evaluated the images on a 3-point scale (1 = worst, 3 = best). For objective image quality analysis, regions of interest were placed in the lung parenchyma and the axillary fat, and standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated. For 32 patients with clinically diagnosed idiopathic interstitial pneumonia, quantitative measurements including total lung volume (TLV) and the percentage of ILD volume (%ILDV) were obtained. The medians of 3 radiologists' scores were analyzed using the Wilcoxon signed-rank test and the objective noise was analyzed using the paired t test. The Bonferroni correction was used for multiple comparisons. The quantitative measurements were analyzed using the Bland-Altman method.uMBIR-Lung scored better than uMBIR-Stnd and worse than sFBP (P < .001), except for noise and streak artifact in subjective analysis. The SD decreased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). The SNR and CNR increased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). For TLV, there was no significant bias between ultra-low-dose MBIRs and sFBP (P > .3). For %ILDV, there was no significant bias between uMBIR-Lung and sFBP (p = 0.8), but uMBIR-Stnd showed significantly lower %ILDV than sFBP (P = .013).uMBIR-Lung provided more appropriate image quality than uMBIR-Stnd. Although inferior to standard-dose CT for image quality, uMBIR-Lung showed equivalent CT quantitative measurements to standard-dose CT.

Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality

OBJECTIVES

To evaluate the image quality of ultralow-dose computed tomography (ULDCT) reconstructed with knowledge-based iterative model reconstruction (IMR) in patients with pulmonary tuberculosis (TB).

METHODS

This IRB-approved prospective study enrolled 59 consecutive patients (mean age, 43.9 ± 16.6 years; F:M 18:41) with known or suspected pulmonary TB. Patients underwent a low-dose CT (LDCT) using automatic tube current modulation followed by an ULDCT using fixed tube current. Raw image data were reconstructed with filtered-back projection (FBP), hybrid iterative reconstruction (iDose), and IMR. Objective measurements including CT attenuation, image noise, and contrast-to-noise ratio (CNR) were assessed and compared using repeated-measures analysis of variance. Overall image quality and visualization of normal and pathological findings were subjectively scored on a five-point scale. Radiation output and subjective scores were compared by the paired Student t test and Wilcoxon signed-rank test, respectively.

RESULTS

Compared with FBP and iDose, IMR yielded significantly lower noise and higher CNR values at both dose levels (p < 0.01). Subjective ratings for pathological findings including centrilobular nodules, consolidation, tree-in-bud, and cavity were significantly better with ULDCT IMR images than those with LDCT iDose images (p < 0.01), but blurred edges were observed. With IMR implementation, a 59% reduction of the mean effective dose was achieved with ULDCT (0.28 ± 0.02 mSv) compared with LDCT (0.69 ± 0.15 mSv) without impairing image quality (p < 0.001).

CONCLUSIONS

IMR offers considerable noise reduction and improvement in image quality for patients with pulmonary TB undergoing chest ULDCT at an effective dose of 0.28 mSv.

KEY POINTS

• Radiation dose is a major concern for tuberculosis patients requiring repeated follow-up CT. • IMR allows substantial radiation dose reduction in chest CT without compromising image quality. • ULDCT reconstructed with IMR allows accurate depiction of CT features of pulmonary tuberculosis.

Evaluation of three-dimensional iterative image reconstruction in C-arm-based interventional cone-beam CT: A phantom study in comparison with customary reconstruction technique

We compared images obtained using a three-dimensional iterative image reconstruction (3D-IIR) algorithm for C-arm-based interventional cone-beam computed tomography (CBCT) with that using the customary reconstruction technique to quantify the effect of reconstruction techniques on image quality.We scanned 2 phantoms using an angiography unit with digital flat-panel system-an elliptical cylinder acrylic phantom to evaluate spatial resolution and a Catphan phantom to evaluate CT number linearity, image noise, and low-contrast resolution. Three-dimensional imaging was calculated using Feldkamp algorithms, and additional image sets were reconstructed using 3D-IIR at 5 settings (Sharp, Default, Soft+, Soft++, Soft+++). We evaluated quality of images obtained using the 6 reconstruction techniques and analyzed variance to test values of the 10% value of each MTF, mean CT number, and contrast-to-noise ratio (CNR), with P < .05 considered statistically significant.Modulation transfer function curves and CT number linearity among images obtained using the customary technique and the 5 3D-IIR techniques showed excellent agreement. Noise power spectrum curves demonstrated uniform noise reduction across the spatial frequency in the iterative reconstruction, and CNR obtained using all but the Sharp 3D-IIR technique was significantly better than that using the customary reconstruction technique (Sharp, P = .1957; Default, P = .0042; others, P < .0001). Use of 3D-IIR, especially the Soft++ and Soft+++ settings, improved visualization of low-contrast targets.Use of a 3D-IIR can significantly improve image noise and low-contrast resolution while maintaining spatial resolution in C-arm-based interventional CBCT, yielding higher quality images that may increase safety and efficacy in interventional radiology.

High-pitch, 120 kVp/30 mAs, low-dose dual-source chest CT with iterative reconstruction: Prospective evaluation of radiation dose reduction and image quality compared with those of standard-pitch low-dose chest CT in healthy adult volunteers

PURPOSE

Objective of this study was to evaluate the effectiveness of the iterative reconstruction of high-pitch dual-source chest CT (IR-HP-CT) scanned with low radiation exposure compared with low dose chest CT (LDCT).

MATERIALS AND METHODS

This study was approved by the institutional review board. Thirty healthy adult volunteers (mean age 44 years) were enrolled in this study. All volunteers underwent both IR-HP-CT and LDCT. IR-HP-CT was scanned with 120 kVp tube voltage, 30 mAs tube current and pitch 3.2 and reconstructed with sinogram affirmed iterative reconstruction. LDCT was scanned with 120 kVp tube voltage, 40 mAs tube current and pitch 0.8 and reconstructed with B50 filtered back projection. Image noise, and signal to noise ratio (SNR) of the infraspinatus muscle, subcutaneous fat and lung parenchyma were calculated. Cardiac motion artifact, overall image quality and artifacts was rated by two blinded readers using 4-point scale. The dose-length product (DLP) (mGy∙cm) were obtained from each CT dosimetry table. Scan length was calculated from the DLP results. The DLP parameter was a metric of radiation output, not of patient dose. Size-specific dose estimation (SSDE, mGy) was calculated using the sum of the anteroposterior and lateral dimensions and effective radiation dose (ED, mSv) were calculated using CT dosimetry index.

RESULTS

Approximately, mean 40% of SSDE (2.1 ± 0.2 mGy vs. 3.5 ± 0.3 mGy) and 34% of ED (1.0 ± 0.1 mSv vs. 1.5 ± 0.1 mSv) was reduced in IR-HP-CT compared to LDCT (P < 0.0001). Image noise was reduced in the IR-HP-CT (16.8 ± 2.8 vs. 19.8 ± 3.4, P = 0.0001). SNR of lung and aorta of IR-HP-CT showed better results compared with that of LDCT (22.2 ± 5.9 vs. 33.0 ± 7.8, 1.9 ± 0.4 vs 1.1 ± 0.3, P < 0.0001). The score of cardiac pulsation artifacts were significantly reduced on IR-HP-CT (3.8 ± 0.4, 95% confidence interval, 3.7‒4.0) compared with LDCT (1.6 ± 0.6, 95% confidence interval, 1.3‒1.8) (P < 0.0001). SNR of muscle and fat, beam hardening artifact and overall subjective image quality of the mediastinum, lung and chest wall were comparable on both scans (P ≥ 0.05).

CONCLUSION

IR-HP-CT with 120 kVp and 30 mAs tube setting in addition to an iterative reconstruction reduced cardiac motion artifact and radiation exposure while representing similar image quality compared with LDCT.

The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma: A Phantom Study

RATIONALE AND OBJECTIVES

This phantom study aimed to evaluate low-dose (LD) chest computed tomography (CT) protocols using model-based iterative reconstruction (MBIR) for diagnosing lung metastases in patients with sarcoma.

MATERIALS AND METHODS

An adult female anthropomorphic phantom was scanned with a 64-slice CT using four LD protocols and a standard-dose protocol. Absorbed organ doses were measured with 10 metal-oxide-semiconductor field-effect transistor dosimeters. Furthermore, Monte Carlo simulations were performed to estimate organ and effective doses. Image quality in terms of image noise, contrast, and resolution was measured from the CT images reconstructed with conventional filtered back projection, adaptive statistical iterative reconstruction, and MBIR algorithms. All the results were compared to the performance of the standard-dose protocol.

RESULTS

Mean absorbed organ and effective doses were reduced by approximately 95% with the LD protocol (100-kVp tube voltage and a fixed 10-mA tube current) compared to the standard-dose protocol (120-kVp tube voltage and tube current modulation) while yielding an acceptable image quality for diagnosing round-shaped lung metastases. The effective doses ranged from 0.16 to 2.83 mSv in the studied protocols. The image noise, contrast, and resolution were maintained or improved when comparing the image quality of LD protocols using MBIR to the performance of the standard-dose chest CT protocol using filtered back projection. The small round-shaped lung metastases were delineated at levels comparable to the used protocols.

CONCLUSIONS

Radiation exposure in patients can be reduced significantly by using LD chest CT protocols and MBIR algorithm while maintaining image quality for detecting round-shaped lung metastases.

Forward-Projected Model-Based Iterative Reconstruction in Screening Low-Dose Chest CT: Comparison With Adaptive Iterative Dose Reduction 3D

OBJECTIVE

The objective of this study is to compare forward-projected model-based iterative reconstruction solution (FIRST), a newer fully iterative CT reconstruction method, with adaptive iterative dose reduction 3D (AIDR 3D) in low-dose screening CT for lung cancer. Differences in image noise, image quality, and pulmonary nodule detection, size, and characterization were specifically evaluated.

MATERIALS AND METHODS

Low-dose chest CT images obtained for 50 consecutive patients between December 2015 and January 2016 were retrospectively reviewed. Images were reconstructed using FIRST and AIDR 3D for both lung and soft-tissue reconstruction. Images were independently reviewed to assess image noise, subjective image quality (with use of a 5-point Likert scale, with 1 denoting far superior image quality; 2, superior quality; 3, equivalent quality; 4, inferior quality; and 5, far inferior quality), pulmonary nodule count, size of the largest pulmonary nodule, and characterization of the largest pulmonary nodule (i.e., solid, part solid, or ground glass).

RESULTS

Across all 50 cases, measured image noise was lower with FIRST than with AIDR 3D (lung window, 44% reduction, 41 ± 7 vs 74 ± 8 HU, respectively; soft-tissue window, 32% reduction, 11 ± 2 vs 16 ± 2 HU, respectively). Readers subjectively rated images obtained with FIRST as comparable to images obtained with AIDR 3D (mean [± SD] Likert score for FIRST vs AIDR 3D, 3.2 ± 0.3 for soft-tissue reconstructions and 3.0 ± 0.3 for lung reconstructions). For each reader, very good agreement regarding nodule count was noted between FIRST and AIDR 3D (interclass correlation coefficient [ICC], 0.83 for reader 1 and 0.78 for reader 2). Excellent agreement regarding nodule size (ICC, 0.99 for reader 1 and 0.99 for reader 2) and characterization of the largest nodule (kappa value, 0.92 for reader 1 and 0.82 for reader 2) also existed.

CONCLUSION

Images reconstructed with FIRST are superior to those reconstructed AIDR 3D with regard to image noise and are equivalent with regard to subjective image quality, pulmonary nodule count, and nodule characterization.

Submillisievert chest dual energy computed tomography: a pilot study

OBJECTIVE

To assess if diagnostic dual energy CT (DECT) of the chest can be achieved at submillisievert (sub-mSv) doses.

METHODS

Our IRB-approved prospective study included 20 patients who were scanned on dual-source multidector CT(MDCT). All patients gave written informed consent for acquisition of additional image series at reduced radiation dose on a dual-source MDCT (80/140 kV) within 10 s after the standard of care acquisition. Dose reduction was achieved by reducing the quality reference milliampere-second, with combined angular exposure control. Four readers, blinded to all clinical data, evaluated the image sets. Image noise, signal-to-noise and contrast-to-noise ratio were assessed. Volumetric CT dose index (CTDI_vol), doselength product (DLP), size specific dose estimate, and effective dose were also recorded.

RESULTS

The mean age and body mass index of the patients were 71 years ± 9 and 24 kg m^-2 ± 3, respectively. Although images became noisier, overall image quality and image sharpness on blended images were considered good or excellent in all cases (20/20). All findings made on the reduced dose images presented with good demarcation. The intraobserver and interobserver agreements were κ = 0.83 and 0.73, respectively. Mean CTDI_vol, size specific dose estimate, DLP and effective dose for reduced dose DECT were: 1.3 ± 0.2 mGy, 1.8 ± 0.2 mGy, 51 ± 9.9 mGy.cm and 0.7 ± 0.1 mSv, respectively.

CONCLUSION

Routine chest DECT can be performed at sub-mSv doses with good image quality and without loss of relevant diagnostic information. Advances in knowledge: (1) Contrast-enhanced DECT of the chest can be performed at sub-mSv doses, down to mean CTDI_vol 1.3 mGy and DLP 51 mGy.cm in patients with body mass index <31 kg m^-2. (2) To our knowledge, this is the first time that sub-mSv doses have been successfully applied in a patient study using a dual source DECT scanner.

Imaging of thoracic tuberculosis in children: current and future directions

Tuberculosis continues to be an important cause of morbidity and mortality worldwide. It is the leading cause of infection-related deaths worldwide. Children are amongst the high-risk groups for developing tuberculosis and often pose a challenge to the clinicians in making a definitive diagnosis. The newly released global tuberculosis report from World Health Organization reveals a 50% increase in fatality from tuberculosis in children. Significantly, diagnostic and treatment algorithms of tuberculosis for children differ from those of adults. Bacteriologic confirmation of the disease is often difficult in children; hence radiologists have an important role to play in early diagnosis of this disease. Despite advancing technology, the key diagnostic imaging modalities for primary care and emergency services, especially in rural and low-resource areas, are chest radiography and ultrasonography. In this article, we discuss various diagnostic imaging modalities used in diagnosis and treatment of tuberculosis and their indications. We highlight the use of US as point-of-care service along with mediastinal US and rapid MRI protocols, especially in mediastinal lymphadenopathy and thoracic complications. MRI is the ideal modality in high-resource areas when adequate infrastructure is available. Because the prevalence of tuberculosis is highest in lower-resource countries, we also discuss global initiatives in low-resource settings.

Comparison of Model-Based Iterative Reconstruction, Adaptive Statistical Iterative Reconstruction, and Filtered Back Projection for Detecting Hepatic Metastases on Submillisievert Low-Dose Computed Tomography

OBJECTIVE

The aim of the study was to compare the diagnostic performance of model-based iterative reconstruction (MBIR), adaptive statistical iterative reconstruction (ASIR), and filtered back projection (FBP) on submillisievert low-dose computed tomography (LDCT) for detecting hepatic metastases.

METHODS

Thirty-eight patients having hepatic metastases underwent abdomen CT. Computed tomography protocol consisted of routine standard-dose portal venous phase scan (120 kVp) and 90-second delayed low-dose scan (80 kVp). The LDCT images were reconstructed with FBP, ASIR, and MBIR, respectively. Two readers recorded the number of hepatic metastases on each image set.

RESULTS

A total of 105 metastatic lesions were analyzed. For reader 1, sensitivity for detecting metastases was stationary between FBP (49%) and ASIR (52%, P = 0.0697); however, sensitivity increased in MBIR (66%, P = 0.0035). For reader 2, it was stationary for all the following sets: FBP (65%), ASIR (68%), and MBIR (67%, P > 0.05).

CONCLUSIONS

The MBIR and ASIR showed a limited sensitivity for detecting hepatic metastases in submillisievert LDCT.

Assessment of chest CT at CTDIvol less than 1 mGy with iterative reconstruction techniques

OBJECTIVE

To assess the image quality of chest CT reconstructed with image-based iterative reconstruction (SafeCT; MedicVision^®, Tirat Carmel, Israel), adaptive statistical iterative reconstruction (ASIR; GE Healthcare, Waukesha, WI) and model-based iterative reconstruction (MBIR; GE Healthcare, Waukesha, WI) techniques at CT dose index volume (CTDI_vol) <1 mGy.

METHODS

In an institutional review board-approved study, 25 patients gave written informed consent for acquisition of three reduced dose (0.25-, 0.4- and 0.8-mGy) chest CT after standard of care CT (8 mGy) on a 64-channel multidetector CT (MDCT) and reconstructed with SafeCT, ASIR and MBIR. Two board-certified thoracic radiologists evaluated images from the lowest to the highest dose of the reduced dose CT series and subsequently for standard of care CT.

RESULTS

Out of the 182 detected lesions, the missed lesions were 35 at 0.25, 24 at 0.4 and 9 at 0.8 mGy with SafeCT, ASIR and MBIR, respectively. The most missed lesions were non-calcified lung nodules (NCLNs) 25/112 (<5 mm) at 0.25, 18/112 (<5 mm) at 0.4 and 3/112 (<4 mm) at 0.8 mGy. There were 78%, 84% and 97% lung nodules detected at 0.25, 0.4 and 0.8 mGy, respectively regardless of iterative reconstruction techniques (IRTs), Most mediastinum structures were not sufficiently seen at 0.25-0.8 mGy.

CONCLUSION

NCLNs can be missed in chest CT at CTDI_vol of <1 mGy (0.25, 0.4 and 0.8 mGy) regardless of IRTs. The most lung nodules (97%) were detected at CTDI_vol of 0.8 mGy. The most mediastinum structures were not sufficiently seen at 0.25-0.8 mGy. Advances in knowledge: NCLNs can be missed regardless of IRTs in chest CT at CTDI_vol of <1 mGy. The performance of ASIR, SafeCT and MBIR was similar for lung nodule detection at 0.25, 0.4 and 0.8 mGy.

Influence of model based iterative reconstruction algorithm on image quality of multiplanar reformations in reduced dose chest CT

Background

Model-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown.

Purpose

To evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition.

Material and Methods

Raw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2–3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT.

Results

The lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts.

Conclusion

The MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique.

Submillisievert Computed Tomography of the Chest Using Model-Based Iterative Algorithm: Optimization of Tube Voltage With Regard to Patient Size

OBJECTIVE

The aim of this study was to define optimal tube potential for soft tissue and vessel visualization in dose-reduced chest CT protocols using model-based iterative algorithm in average and overweight patients.

METHODS

Thirty-six patients receiving chest CT according to 3 protocols (120 kVp/noise index [NI], 60; 100 kVp/NI, 65; 80 kVp/NI, 70) were included in this prospective study, approved by the ethics committee. Patients' physical parameters and dose descriptors were recorded. Images were reconstructed with model-based algorithm. Two radiologists evaluated image quality and lesion conspicuity; the protocols were intraindividually compared with preceding control CT reconstructed with statistical algorithm (120 kVp/NI, 20). Mean and standard deviation of attenuation of the muscle and fat tissues and signal-to-noise ratio of the aorta were measured.

RESULTS

Diagnostic images (lesion conspicuity, 95%-100%) were acquired in average and overweight patients at 1.34, 1.02, and 1.08 mGy and at 3.41, 3.20, and 2.88 mGy at 120, 100, and 80 kVp, respectively. Data are given as CT dose index volume values.

CONCLUSIONS

Model-based algorithm allows for submillisievert chest CT in average patients; the use of 100 kVp is recommended.

CT Pulmonary Angiography at Reduced Radiation Exposure and Contrast Material Volume Using Iterative Model Reconstruction and iDose4 Technique in Comparison to FBP

Purpose

To assess image quality of CT pulmonary angiography (CTPA) at reduced radiation exposure (RD-CTPA) and contrast medium (CM) volume using two different iterative reconstruction (IR) algorithms (iDose⁴ and iterative model reconstruction (IMR)) in comparison to filtered back projection (FBP).

Materials and Methods

52 patients (body weight < 100 kg, mean BMI: 23.9) with suspected pulmonary embolism (PE) underwent RD-CTPA (tube voltage: 80 kV; mean CTDIvol: 1.9 mGy) using 40 ml CM. Data were reconstructed using FBP and two different IR algorithms (iDose⁴ and IMR). Subjective and objective image quality and conspicuity of PE were assessed in central, segmental, and subsegmental arteries.

Results

Noise reduction of 55% was achieved with iDose⁴ and of 85% with IMR compared to FBP. Contrast-to-noise ratio significantly increased with iDose⁴ and IMR compared to FBP (p<0.05). Subjective image quality was rated significantly higher at IMR reconstructions in comparison to iDose⁴ and FBP. Conspicuity of central and segmental PE significantly improved with the use of IMR. In subsegmental arteries, iDose⁴ was superior to IMR.

Conclusions

CTPA at reduced radiation exposure and contrast medium volume is feasible with the use of IMR, which provides improved image quality and conspicuity of pulmonary embolism in central and segmental arteries.

Comparison of image quality and visibility of normal and abnormal findings at submillisievert chest CT using filtered back projection, iterative model reconstruction (IMR) and iDose4™

OBJECTIVE

To compare both image quality and visibility of normal and abnormal findings at submillisievert chest CT (smSv-CT) using filtered back projection (FBP) and the two different iterative reconstruction (IR) techniques iterative model reconstruction (IMR) and iDose⁴™.

MATERIALS AND METHODS

This institutional review board approved study was based on retrospective interpretation of clinically indicated acquired data. The requirement to obtain informed consent was waived. 81 patients with suspected pneumonia underwent smSv-CT (Brilliance iCT, Philips Healthcare; mean effective dose: 0.86±0.2mSv). Data were reconstructed using FBP and two different IR techniques iDose⁴™ and IMR (Philips Healthcare) at various iteration levels. Objective image noise (OIN) was measured. Two experienced readers independently assessed all images for image noise, image appearance and visibility of normal anatomic and abnormal findings. A random intercept model was used for statistical analysis.

RESULTS

Compared to FBP and iDose⁴™, IMR reduced OIN up to 88% and 72%, respectively (p<0.001). A mild blotchy image appearance was seen in IMR images, affecting diagnostic confidence. iDose⁴™ images provided satisfactory to good image quality for visibility of normal and abnormal findings and were superior to FBP (p<0.001). IMR images were significantly inferior for visibility of normal structures compared to iDose⁴™, while being superior for visibility of abnormal findings except for reticular pattern (p<0.001).

CONCLUSION

IMR results for visibility of normal and abnormal lung findings are heterogeneous, indicating that IMR may not represent a priority technique for clinical routine. iDose⁴™ represents a suitable method for evaluation of lung tissue at submillisievert chest CT.

Submillisievert CT using model-based iterative reconstruction with lung-specific setting: An initial phantom study

OBJECTIVE

To assess image quality of filtered back-projection (FBP) and model-based iterative reconstruction (MBIR) with a conventional setting and a new lung-specific setting on submillisievert CT.

METHODS

A lung phantom with artificial nodules was scanned with 10 mA at 120 kVp and 80 kVp (0.14 mSv and 0.05 mSv, respectively); images were reconstructed using FBP and MBIR with conventional setting (MBIR_Stnd) and lung-specific settings (MBIR_RP20/Tx and MBIR_RP20). Three observers subjectively scored overall image quality and image findings on a 5-point scale (1 = worst, 5 = best) compared with reference standard images (50 mA-FBP at 120, 100, 80 kVp). Image noise was measured objectively.

RESULTS

MBIR_RP20/Tx performed significantly better than MBIR_Stnd for overall image quality in 80-kVp images (p < 0.01), blurring of the border between lung and chest wall in 120p-kVp images (p < 0.05) and the ventral area of 80-kVp images (p < 0.001), and clarity of small vessels in the ventral area of 80-kVp images (p = 0.037). At 120 kVp, 10 mA-MBIR_RP20 and 10 mA-MBIR_RP20/Tx showed similar performance to 50 mA-FBP. MBIR_Stnd was better for noise reduction. Except for blurring in 120 kVp-MBIR_Stnd, MBIRs performed better than FBP.

CONCLUSION

Although a conventional setting was advantageous in noise reduction, a lung-specific setting can provide more appropriate image quality, even on submillisievert CT.

KEY POINTS

• Lung-specific submillisievert 10 mA-MBIR CT setting has similar performance to 50 mA-FBP • The new lung-specific settings improve vessel clarity and blurring of borders • The new settings may provide more appropriate images than conventional settings.

Medical imaging in occupational and environmental lung disease

PURPOSE OF REVIEW

The purpose of this review is to provide an up-to-date summary of developments in medical imaging in the diagnosis, surveillance, treatment, and screening of occupational and environmental lung diseases, focusing on articles published within the past 2 years.

RECENT FINDINGS

Many new exposures resulting in lung disease have been described worldwide; medical imaging, particularly computed tomography (CT), is often pivotal in recognition and characterization of these new patterns of lung injury. Chest radiography remains important to surveillance studies tracking the long-term evolution of disease and effectiveness of air quality regulation. Finally, studies are proving the utility of screening with low-dose CT, and technical advances offer the prospect of further CT dose reduction with ultra-low-dose CT.

SUMMARY

In understanding the best practices and new developments in medical imaging, the occupational and environmental medicine clinician can optimize diagnosis and management of related lung diseases.

The 10 Pillars of Lung Cancer Screening: Rationale and Logistics of a Lung Cancer Screening Program

On the basis of the National Lung Screening Trial data released in 2011, the U.S. Preventive Services Task Force made lung cancer screening (LCS) with low-dose computed tomography (CT) a public health recommendation in 2013. The Centers for Medicare and Medicaid Services (CMS) currently reimburse LCS for asymptomatic individuals aged 55-77 years who have a tobacco smoking history of at least 30 pack-years and who are either currently smoking or had quit less than 15 years earlier. Commercial insurers reimburse the cost of LCS for individuals aged 55-80 years with the same smoking history. Effective care for the millions of Americans who qualify for LCS requires an organized step-wise approach. The 10-pillar model reflects the elements required to support a successful LCS program: eligibility, education, examination ordering, image acquisition, image review, communication, referral network, quality improvement, reimbursement, and research frontiers. Examination ordering can be coupled with decision support to ensure that only eligible individuals undergo LCS. Communication of results revolves around the Lung Imaging Reporting and Data System (Lung-RADS) from the American College of Radiology. Lung-RADS is a structured decision-oriented reporting system designed to minimize the rate of false-positive screening examination results. With nodule size and morphology as discriminators, Lung-RADS links nodule management pathways to the variety of nodules present on LCS CT studies. Tracking of patient outcomes is facilitated by a CMS-approved national registry maintained by the American College of Radiology. Online supplemental material is available for this article.