Task-based Transfer Function Research Articles

Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment

PurposeTo assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform. Materials and methodsAcquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDIvol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d′) was computed to model the detection task of two enhanced iodine lesions as function of keV. ResultsNoise-magnitude was significantly reduced between 40 to 70 keV by −56±0% (SD) (range: −56%–−55%) with FBP; −56±0% (SD) (−56%–−56%) with A3; and −57±1% (SD) (range: −57%–−56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2mg/mL, d’ values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d’ peaked at 60 keV. For the other simulated lesion, d’ values were highest at 40 keV and decreased beyond. ConclusionAt low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.

Performance of four dual-energy CT platforms for abdominal imaging: a task-based image quality assessment based on phantom data.

To compare the spectral performance of dual-energy CT (DECT) platforms using task-based image quality assessment based on phantom data. Two CT phantoms were scanned on four DECT platforms: fast kV-switching CT (KVSCT), split filter CT (SFCT), dual-source CT (DSCT), and dual-layer CT (DLCT). Acquisitions on each phantom were performed using classical parameters of abdomen-pelvic examination and a CTDIvol at 10 mGy. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 140 keV of virtual monoenergetic images. A detectability index (d') was computed to model the detection task of two contrast-enhanced lesions as function of keV. The noise magnitude decreased from 40 to 70 keV for all DECT platforms, and the highest noise magnitude values were found for KVSCT and SFCT and the lowest for DSCT and DLCT. The average NPS spatial frequency shifted towards lower frequencies as the energy level increased for all DECT platforms, smoothing the image texture. TTF values decreased with the increase of keV deteriorating the spatial resolution. For both simulated lesions, higher detectability (d' value) was obtained at 40 keV for DLCT, DSCT, and SFCT but at 70 keV for KVSCT. The detectability of both simulated lesions was highest for DLCT and DSCT. Highest detectability was found for DLCT for the lowest energy levels. The task-based image quality assessment used for the first time for DECT acquisitions showed the benefit of using low keV for the detection of contrast-enhanced lesions. • Detectability of both simulated contrast-enhanced lesions was higher for dual-layer CT for the lowest energy levels. • The image noise increased and the image texture changed for the lowest energy levels. • The detectability of both simulated contrast-enhanced lesions was highest at 40 keV for all dual-energy CT platforms except for fast kV-switching platform.

Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study.

To assess whether a deep learning image reconstruction algorithm (TrueFidelity) can preserve the image texture of conventional filtered back projection (FBP) at reduced dose levels attained by ASIR-V in chest CT. Phantom images were acquired using a clinical chest protocol (7.6mGy) and two levels of dose reduction (60% and 80%). Images were reconstructed with FBP, ASIR-V (50% and 100% blending) and TrueFidelity (low (DL-L), medium (DL-M) and high (DL-H) strength). Noise (SD), noise power spectrum (NPS) and task-based transfer function (TTF) were calculated. Noise texture was quantitatively compared by computing root-mean-square deviations (RMSD) of NPS with respect to FBP. Four experienced readers performed a contrast-detail evaluation. The dose reducing potential of TrueFidelity compared to ASIR-V was assessed by fitting SD and contrast-detail as a function of dose. DL-M and DL-H reduced noise and NPS area compared to FBP and 50% ASIR-V, at all dose levels. At 7.6mGy, NPS of ASIR-V 50/100% was shifted towards lower frequencies (fpeak=0.22/0.13mm-1, RMSD=0.14/0.38), with respect to FBP (fpeak=0.30mm-1). Marginal difference was observed for TrueFidelity: fpeak=0.33/0.30/0.30mm-1 and RMSD=0.03/0.04/0.07 for L/M/H strength. Values of TTF50% were independent of DL strength and higher compared to FBP and ASIR-V, at all dose and contrast levels. Contrast-detail was highest for DL-H at all doses. Compared to 50% ASIR-V, DL-H had an estimated dose reducing potential of 50% on average, without impairing noise, texture and detectability. TrueFidelity preserves the image texture of FBP, while outperforming ASIR-V in terms of noise, spatial resolution and detectability at lower doses.

Impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images

To assess the impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images from a Fast kV-Switching Dual Energy CT (KVSCT) platform. Acquisitions on an image-quality phantom were performed using a KVSCT for three dose levels (CTDIvol:12.72/10.76/8.79mGy). Raw data were reconstructed for five energy levels (40/50/60/70/80keV) using Filtered Back Projection (FBP) and four levels of ASIR (ASIR30/ASIR50/ASIR70/ASIR100). Noise power spectrum (NPS) and task-based transfer function (TTF) were measured before computing a Detectability index (d') to model the detection task of liver metastasis (LM) and hepatocellular carcinoma (HCC) as function of keV. From 40 to 70keV, noise-magnitude was reduced on average by -68%±1% with FBP; -61%±3% with ASIR50 and -52%±6% with ASIR100. The mean spatial frequency of the NPS decreased when the energy level decreased and the iterative level increased. TTF values at 50% decreased as the energy level increased and as the percentage of ASIR increased. The detectability of both lesions increased with increasing dose level and percentage of ASIR. For the LM, d' peaked at 70keV for all reconstruction types, except for ASIR70 at 12.72mGy and ASIR100, where d' peaked at 50keV. For HCC, d' peaked at 60keV for FBP and ASIR30 but peaked at 50keV for ASIR50, ASIR70 and ASIR100. Using percentage of ASIR above 50% at low-energy monochromatic images could limit the increase of noise-magnitude, benefit from spatial resolution improvement and hence enhance detectability of subtle low contrast focal liver lesions such as HCC.

Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study.

To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm. Data acquisitions were performed at seven dose levels (CTDIvol :15/10/7.5/5/2.5/1/0.5mGy) using a standard phantom designed for image quality assessment. Raw data were reconstructed using the filtered back projection (FBP), two levels of IR (ASiR-V50% (AV50); ASiR-V100% (AV100)), and three levels of DLIR (TrueFidelity™ low, medium, high). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index (d') was computed to model a large mass in the liver, a small calcification, and a small subtle lesion with low contrast. NPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF50% obtained with DLIR was higher than that with IR. d' was higher with DLIR than with AV50 but lower with DLIR-L and DLIR-M than with AV100. d' values were higher with DLIR-H than with AV100 for the small low-contrast lesion (10 ± 4%) and in the same range for the other simulated lesions. New DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR. • This study assessed the impact on image quality and radiation dose of a new deep learning image reconstruction (DLIR) algorithm as compared with hybrid iterative reconstruction (IR) algorithm. • The new DLIR algorithm reduced noise and improved spatial resolution and detectability without perceived alteration of the texture, commonly reported with IR. • As compared with IR, DLIR seems to open further possibility of dose optimization.

Effect of tin filter-based spectral shaping CT on image quality and radiation dose for routine use on ultralow-dose CT protocols: A phantom study

PurposeThe purpose of this study was to assess the impact of tin filter (TF) on X-ray beam quality, image quality and radiation dose and its suitability for routine use for chest and lumbar-spine/pelvis-hip ultralow-dose (ULD) CT examination protocols. Materials and methodsThe X-ray beam quality was determined by measuring the half-value layer (HVL) and calculating the mean weighted energy for 100, 120, 150kVp (using standard filtration) and for 100 and 150kVp using TF (Sn100kVp and Sn150kVp, respectively). Acquisitions were performed on a phantom at four dose levels for each previously definedkVp. The mean attenuation (NCT), noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. The detectability index (d’) was computed to model the detection of two lesions in spine and pelvic/hip examination and two for chest exploration. Image quality and detectability using a TF were assessed for two routinely used ULD protocols. ResultsThe HVL and mean weighted energy increased using a TF for the same tube voltage. Using a TF for the same tube voltage changed NCT for bone and acrylic inserts, decreased the NPS peak without changing the NPS spatial frequency and increased the TTF values. The d’ values were improved using a TF and with the dose increase. d’ values of all modeled lesions were improved using Sn100kVp and Sn150kVp for the lumbar-spine/pelvis-hip and chest ULD protocols except for sclerotic bone lesion using Sn150kVp. ConclusionThe use of TF increases the X-ray beam quality and improves the image quality characteristics in phantom images, thus appearing as a promising tool for reducing dose and/or improving the image quality of ULD protocols.

CT dose optimization for the detection of pulmonary arteriovenous malformation (PAVM): A phantom study

PurposeTo determine the lowest suitable dose level for the detection of pulmonary arteriovenous malformation (PAVM) using a task-based image quality assessment. Material and methodsA phantom was scanned using the standard chest protocol (STD) and 4 other ultra-low dose protocols (ULD) using various kVp. Raw data were reconstructed using level 5 of the hybrid iterative reconstruction algorithm (iDose4) for the STD protocol, and level 6 of iDose4 and levels 1 to 3 of model-based iterative reconstruction (IMR) for the ULD protocols. Both quantitative criteria and qualitative analysis were used to compare protocols. Noise-power-spectrum and Task-based transfer function were computed using imQuest software. The detectability-index (d’) was computed for the detection of PAVM. A subjective analysis was performed by 2 chest radiologists to validate the image-quality obtained on the anthropomorphic phantom for all protocols. ResultsSimilar d’ values were found for ULD-140 using iDose4 6 compared to STD protocol. Greater d’ values were found for all ULD protocols using IMR compared to STD. Subjective image quality was rated as acceptable to excellent for ULD-140 and ULD-120 for all reconstruction types, for ULD-100 and ULD-80 using IMR2, and for ULD-100 using IMR1. Image smoothing was poor for IMR3 for ULD-100 and ULD-80. Finally, the ULD-80 protocol reconstructed with IMR2 was chosen for the detection of PAVM. With this protocol, the dose (CTDIvol of 0.3mGy) was reduced by 91% compared with the STD protocol. ConclusionA dose level as low as 0.3mGy reconstructed with IMR2 provides an image quality suitable for the detection of PAVM.

Optimization of radiation dose for CT detection of lytic and sclerotic bone lesions: a phantom study

To determine the best compromise between low radiation dose and suitable image quality for the detection of lytic and sclerotic bone lesions of the lumbar spine and pelvis. A phantom was scanned using the routine protocol (STD, 13mGy) and six decreasing dose levels. Raw data were reconstructed using level 3 of iterative reconstruction (IR3) with 1-mm slice thickness for the STD protocol and highest IR levels with 3-mm slice thickness for the others. CTDIvol was used for radiation dose assessment. Quantitative criteria (noise power spectrum [NPS], task-based transfer function [TTF], and the detectability index [d']), as well as qualitative analysis, were used to compare protocols. NPS and TTF were computed using specific software (imQuest). d' was computed for two imaging tasks: lytic and sclerotic bone lesions. A subjective analysis was performed to validate the image quality obtained on the anthropomorphic phantom with the different dose values. Similar d' values were found for CTDIvol from 3 to 4mGy with IR4 and from 1 to 2mGy for IR5 compared with d' values using the STD protocol. Image quality was validated subjectively for IR4 but rejected for IR5 (image smoothing). Finally, for the same d', the dose was reduced by 74% compared with the STD protocol, with the CTDIvol being 3.4mGy for the lumbar spine and for the pelvis. A dose level as low as 3.4mGy, in association with high levels of IR, provides suitable image quality for the detection of lytic and sclerotic bone lesions of the lumbar spine and pelvis. • A CTDI volof 3.4mGy, in association with high iterative reconstruction level, provides suitable image quality for the detection of lytic and sclerotic bone lesions, both at objective and subjective analysis. • Compared with the standard protocol, radiation dose can be reduced up to 74% for the lumbar spine and pelvis. • A task-based image quality assessment using the detectability index represents an objective method for the assessment of image quality and bridgesthe gap between complex physical metrics and subjective image analysis.

CT iterative reconstruction algorithms: a task-based image quality assessment.

To assess the dose performance in terms of image quality of filtered back projection (FBP) and two generations of iterative reconstruction (IR) algorithms developed by the most common CT vendors. We used four CT systems equipped with a hybrid/statistical IR (H/SIR) and a full/partial/advanced model-based IR (MBIR) algorithms. Acquisitions were performed on an ACR phantom at five dose levels. Raw data were reconstructed using a standard soft tissue kernel for FBP and one iterative level of the two IR algorithm generations. The noise power spectrum (NPS) and the task-based transfer function (TTF) were computed. A detectability index (d') was computed to model the detection task of a large mass in the liver (large feature; 120HU and 25-mm diameter) and a small calcification (small feature; 500HU and 1.5-mm diameter). With H/SIR, the highest values of d' for both features were found for Siemens, then for Canon and the lowest values for Philips and GE. For the large feature, potential dose reductions with MBIR compared with H/SIR were - 35% for GE, - 62% for Philips, and - 13% for Siemens; for the small feature, corresponding reductions were - 45%, - 78%, and - 14%, respectively. With the Canon system, a potential dose reduction of - 32% was observed only for the small feature with MBIR compared with the H/SIR algorithm. For the large feature, the dose increased by 100%. This multivendor comparison of several versions of IR algorithms allowed to compare the different evolution within each vendor. The use of d' is highly adapted and robust for an optimization process. • The performance of four CT systems was evaluated by using imQuest software to assess noise characteristic, spatial resolution, and lesion detection. • Two task functions were defined to model the detection task of a large mass in the liver and a small calcification. • The advantage of task-based image quality assessment for radiologists is that it does not include only complicated metrics, but also clinically meaningful image quality.

A three-dimensional cross-directional bilateral filter for edge-preserving noise reduction of low-dose computed tomography images

BackgroundImage-based noise reduction techniques are useful because they can be applied across various computed tomography (CT) scanner models from different vendors, regardless of the iterative reconstruction availability. The purpose of this study was to propose a 3-dimensional cross-directional bilateral filter (3D-CDBF) and compare the edge-preserving noise reduction on low-dose CT images to a model-based iterative reconstruction (MBIR). MethodsThe 3D-CDBF comprises a bilateral filter and a smoothing filter applied in range filtering. The filtering process was applied with four iterations using empirically determined parameters that yielded the best tradeoff between noise reduction and edge preservation for a very low radiation dose of 2.5 mGy. In-plane and z-directional edge preservation performances for low-contrast rod phantoms (60 Hounsfield units) were compared to a clinically available MBIR and a conventional 3D bilateral filter (3D-BF), using task-based spatial resolution (task-based transfer function: TTF) and slice thickness. Moreover, the noise power spectra (NPS) were compared. Furthermore, performance was compared on abdominal CT images acquired from volunteers at 2.5 mGy (approved by our institutional review board). ResultsIn phantom tests, 3D-CDBF provided 28.5% higher spatial resolution at 50%TTF compared to MBIR. Moreover, total NPS was lower, while the slice thickness (z-axis resolution) was slightly broader than that achieved by MBIR (0.99 mm vs. 0.92 mm). 3D-BF was inferior to both 3D-CDBF and MBIR in all measurements. Consistent with phantom results, 3D-CDBF significantly reduced noise on abdominal images compared to MBIR (P < 0.001), exhibiting better preservation of organ edges. ConclusionThis 3D-CDBF may provide superior edge preserving noise reduction of low-dose CT images compared to currently available MBIR.