Improved image quality in abdominal CT in patients who underwent treatment for hepatocellular carcinoma with small metal implants using a raw data-based metal artifact reduction algorithm.

OBJECTIVES: To determine the value of a raw data-based metal artifact reduction (SEMAR) algorithm for image quality improvement in abdominal CT for patients with small metal implants. METHODS: Fifty-eight patients with small metal implants (3-15 mm in size) who underwent treatment for hepatocellular carcinoma were imaged with CT. CT data were reconstructed by filtered back projection with and without SEMAR algorithm in axial and coronal planes. To evaluate metal artefact reduction, mean CT number (HU and SD) and artefact index (AI) values within the liver were calculated. Two readers independently evaluated image quality of the liver and pancreas and visualization of vasculature using a 5-point visual score. HU and AI values and image quality on images with and without SEMAR were compared using the paired Student's t-test and Wilcoxon signed rank test. Interobserver agreement was evaluated using linear-weighted κ test. RESULTS: Mean HU and AI on images with SEMAR was significantly lower than those without SEMAR (P < 0.0001). Liver and pancreas image qualities and visualizations of vasculature were significantly improved on CT with SEMAR (P < 0.0001) with substantial or almost perfect agreement (0.62 ≤ κ ≤ 0.83). CONCLUSIONS: SEMAR can improve image quality in abdominal CT in patients with small metal implants by reducing metallic artefacts. KEY POINTS: • SEMAR algorithm significantly reduces metallic artefacts from small implants in abdominal CT. • SEMAR can improve image quality of the liver in dynamic CECT. • Confidence visualization of hepatic vascular anatomies can also be improved by SEMAR.

Hybrid Type iterative reconstruction method vs. filter back projection method: Capability for radiation dose reduction and perfusion assessment on dynamic first-pass contrast-enhanced perfusion chest area-detector CT.

PURPOSE: To directly compare the capability of hybrid-type iterative reconstruction (i.e., adaptive iterative dose reduction using 3D processing: AIDR 3D) and filter back projection (FBP) for radiation dose reduction during dynamic contrast-enhanced (CE-) perfusion area-detector CT (ADCT) for lung and nodule perfusion assessment. MATERIALS AND METHODS: Thirty-six patients with lung cancers who underwent perfusion ADCT (SD-ADCT) at 120 mA and were enrolled in this study. ADCT data at 80 mA (reduced-dose ADCT: RD-ADCT), 60 mA (low-dose ADCT: LD-ADCT) and 40 mA (very low-dose ADCT: VLD-ADCT) were computationally simulated using SD-ADCT data, and reconstructed with and without AIDR 3D. Image noise and lung and nodule perfusion parameters were evaluated using ROI measurements. To determine the utility of AIDR 3D for dose reduction, image noise was compared between each protocol with and without AIDR 3D by means of the t-test. Correlations and limits of agreement for parameters obtained with SD-ADCT and other protocols were also evaluated. RESULTS: Image noise of all protocols with AIDR 3D was significantly lower than that of LD-ADCT and VLD-ADCT without AIDR 3D (p<0.05). Significant correlations for image noise between SD-ADCT and all protocols with AIDR 3D (0.45 ≤ r ≤ 0.99, p<0.0001) were equal to or better than that without AIDR 3D (0.28 ≤ r ≤ 0.99, p<0.0001). The limits of agreement for perfusion parameters with AIDR 3D were smaller than those without AIDR 3D for each tube current. CONCLUSION: AIDR 3D is more effective than FBP for dose reduction of perfusion ADCT while maintaining image quality and reducing measurement errors.

Adaptive iterative dose reduction 3D (AIDR 3D) vs. filtered back projection: radiation dose reduction capabilities of wide volume and helical scanning techniques on area-detector CT in a chest phantom study.

BACKGROUND: Computed tomography (CT) has important roles for lung cancer screening, and therefore radiation dose reduction by using iterative reconstruction technique and scanning methods receive widespread attention. PURPOSE: To evaluate the effect of two reconstruction techniques (filtered back projection [FBP] and adaptive iterative dose reduction using three-dimensional processing [AIDR 3D]) and two acquisition techniques (wide-volume scan [WVS] and helical scan as 64-detector-row CT [64HS]) on the lung nodule identifications of using a chest phantom. MATERIAL AND METHODS: A chest CT phantom including lung nodules was scanned using WVS and 64HS at nine different tube currents (TCs; range, 270-10 mA). All CT datasets were reconstructed with AIDR 3D and FBP. Standard deviation (SD) measurements by region of interest placement and qualitative nodule identifications were statistically compared. 64HS and WVS were evaluated separately, and FBP images acquired with 270 mA was defined as the standard reference. RESULTS: SDs of all datasets with AIDR 3D showed no significant differences (P > 0.05) with standard reference. When comparing nodule identifications, area under the curve on WVS with AIDR 3D with TC <30 mA, on 64HS with AIDR 3D with TC <40 mA, and on reconstructions with FBP and each scan method with TC <60 mA was significantly lower than with standard reference (P < 0.05). With the same TC and reconstruction, SDs and nodule identifications of WVS were not significantly different from 64HS (P > 0.05). CONCLUSION: In term of SD of lung parenchyma and nodule identification, AIDR 3D can achieve more radiation dose reduction than FBP and there is no significant different between WVS and 64HS.

Automated continuous quantitative measurement of proximal airways on dynamic ventilation CT: initial experience using an ex vivo porcine lung phantom.

BACKGROUND: The purpose of this study was to evaluate the feasibility of continuous quantitative measurement of the proximal airways, using dynamic ventilation computed tomography (CT) and our research software. METHODS: A porcine lung that was removed during meat processing was ventilated inside a chest phantom by a negative pressure cylinder (eight times per minute). This chest phantom with imitated respiratory movement was scanned by a 320-row area-detector CT scanner for approximately 9 seconds as dynamic ventilatory scanning. Obtained volume data were reconstructed every 0.35 seconds (total 8.4 seconds with 24 frames) as three-dimensional images and stored in our research software. The software automatically traced a designated airway point in all frames and measured the cross-sectional luminal area and wall area percent (WA%). The cross-sectional luminal area and WA% of the trachea and right main bronchus (RMB) were measured for this study. Two radiologists evaluated the traceability of all measurable airway points of the trachea and RMB using a three-point scale. RESULTS: It was judged that the software satisfactorily traced airway points throughout the dynamic ventilation CT (mean score, 2.64 at the trachea and 2.84 at the RMB). From the maximum inspiratory frame to the maximum expiratory frame, the cross-sectional luminal area of the trachea decreased 17.7% and that of the RMB 29.0%, whereas the WA% of the trachea increased 6.6% and that of the RMB 11.1%. CONCLUSION: It is feasible to measure airway dimensions automatically at designated points on dynamic ventilation CT using research software. This technique can be applied to various airway and obstructive diseases.

Emphysema quantification by low-dose CT: potential impact of adaptive iterative dose reduction using 3D processing.

OBJECTIVE: The purpose of this study is to investigate the effect of a novel reconstruction algorithm, adaptive iterative dose reduction using 3D processing, on emphysema quantification by low-dose CT. MATERIALS AND METHODS: Twenty-six patients who had undergone standard-dose (150 mAs) and low-dose (25 mAs) CT scans were included in this retrospective study. Emphysema was quantified by several quantitative measures, including emphysema index given by the percentage of lung region with low attenuation (lower than -950 HU), the 15th percentile of lung density, and size distribution of low-attenuation lung regions, on standard-dose CT images reconstructed without adaptive iterative dose reduction using 3D processing and on low-dose CT images reconstructed both without and with adaptive iterative dose reduction using 3D processing. The Bland-Altman analysis was used to assess whether the agreement between emphysema quantifications on low-dose CT and on standard-dose CT was improved by the use of adaptive iterative dose reduction using 3D processing. RESULTS: For the emphysema index, the mean differences between measurements on low-dose CT and on standard-dose CT were 1.98% without and -0.946% with the use of adaptive iterative dose reduction using 3D processing. For 15th percentile of lung density, the mean differences without and with adaptive iterative dose reduction using 3D processing were -6.67 and 1.28 HU, respectively. For the size distribution of low-attenuation lung regions, the ranges of the mean relative differences without and with adaptive iterative dose reduction using 3D processing were 21.4-85.5% and -14.1% to 11.2%, respectively. For 15th percentile of lung density and the size distribution of low-attenuation lung regions, the agreement was thus improved by the use of adaptive iterative dose reduction using 3D processing. CONCLUSION: The use of adaptive iterative dose reduction using 3D processing resulted in greater consistency of emphysema quantification by low-dose CT, with quantification by standard-dose CT.

3D automatic exposure control for 64-detector row CT: radiation dose reduction in chest phantom study.

PURPOSE: The purpose of this study was to determine the utility of three-dimensional (3D) automatic exposure control (AEC) for low-dose CT examination in a chest phantom study. MATERIALS AND METHODS: A chest CT phantom including simulated focal ground-glass opacities (GGOs) and nodules was scanned with a 64-detector row CT with and without AEC. Performance of 3D AEC included changing targeted standard deviations (SDs) of image noise from scout view. To determine the appropriate targeted SD number for identification, the capability of overall identification with the CT protocol adapted to each of the targeted SDs was compared with that obtained with CT without AEC by means of receiver operating characteristic analysis. RESULTS: When targeted SD values equal to or higher than 250 were used, areas under the curve (Azs) of nodule identification with CT protocol using AEC were significantly smaller than that for CT protocol without AEC (p < 0.05). When targeted SD numbers at equal to or more than 180 were adapted, Azs of CT protocol with AEC had significantly smaller than that without AEC (p < 0.05). CONCLUSION: This phantom study shows 3D AEC is useful for low-dose lung CT examination, and can reduce the radiation dose while maintaining good identification capability and good image quality.

Fluctuation in measurements of pulmonary nodule under tidal volume ventilation on four-dimensional computed tomography: preliminary results.

The present study aimed to assess the feasibility of four-dimensional (4D) chest computed tomography (CT) under tidal volume ventilation and the impact of respiratory motion on quantitative analysis of CT measurements. Forty-four pulmonary nodules in patients with metastatic disease were evaluated. CT examinations were performed using a 256 multidetector-row CT (MDCT) unit. Volume data were obtained from the lower lung fields (128 mm) above the diaphragm during dynamic CT acquisition. The CT parameters used were 120 kV, 100 or 150 mA, 0.5 s(-1), and 0.5 mm collimation. Image data were reconstructed every 0.1 s during one respiratory cycle by a 180 degrees reconstruction algorithm for four independent fractions of the respiratory cycle. Pulmonary nodules were measured along their longest and shortest axes using electronic calipers. Automated volumetry was assessed using commercially available software. The diameters of long and short axes in each frame were 9.0-9.6 mm and 7.1-7.5 mm, respectively. There was fluctuation of the long axis diameters in the third fraction. The mean volume in each fraction ranged from 365 to 394 mm(3). Statistically significant fluctuation was also found in the third fraction. 4D-CT under tidal volume ventilation is feasible to determine diameter or volume of the pulmonary nodule.

Influence of detector collimation and beam pitch for identification and image quality of ground-glass attenuation and nodules on 16- and 64-detector row CT systems: experimental study using chest phantom.

OBJECTIVE: The purpose of the present study was to determine the influence of detector collimation and beam pitch for identification and image quality of ground-glass attenuation (GGA) and nodules on 16- and 64-detector row CTs, by using a commercially available chest phantom. MATERIALS AND METHODS: A chest CT phantom including simulated GGAs and nodules was scanned with different detector collimations, beam pitches and tube currents. The probability and image quality of each simulated abnormality was visually assessed with a five-point scoring system. ROC-analysis and ANOVA were then performed to compare the identification and image quality of either protocol with standard values. RESULTS: Detection rates of low-dose CTs were significantly reduced when tube currents were set at 40mA or less by using detector collimation 16 and 64x0.5mm and 16 and 32mmx1.0mm for low pitch, and at 100mA or less by using detector collimation 16 and 64x0.5mm and 16 and 32mmx1.0mm for high pitch (p<0.05). Image qualities of low-dose CTs deteriorated significantly when tube current was set at 100mA or less by using detector collimation 16 and 64x0.5mm and 16 and 32x1.0mm for low pitch, and at 150mA or less by using detector collimation 16 and 64x0.5mm and 16 and 32x1.0mm for high pitch (p<0.05). CONCLUSION: Detector collimation and beam pitch were important factors for the image quality and identification of GGA and nodules by 16- and 64-detector row CT.