Ultra-high resolution spectral photon-counting CT outperforms dual layer CT for lung imaging: Results of a phantom study.

PURPOSE: The purpose of this study was to compare lung image quality obtained with ultra-high resolution (UHR) spectral photon-counting CT (SPCCT) with that of dual-layer CT (DLCT), at standard and low dose levels using an image quality phantom and an anthropomorphic lung phantom. METHODS: An image quality phantom was scanned using a clinical SPCCT prototype and an 8 cm collimation DLCT from the same manufacturer at 10 mGy. Additional acquisitions at 6 mGy were performed with SPCCT only. Images were reconstructed with dedicated high-frequency reconstruction kernels, slice thickness between 0.58 and 0.67 mm, and matrix between 5122 and 10242 mm, using a hybrid iterative algorithm at level 6. Noise power spectrum (NPS), task-based transfer function (TTF) for iodine and air inserts, and detectability index (d') were assessed for ground-glass and solid nodules of 2 mm to simulate highly detailed lung lesions. Subjective analysis of an anthropomorphic lung phantom was performed by two radiologists using a five-point quality score. RESULTS: At 10 mGy, noise magnitude was reduced by 29.1 % with SPCCT images compared to DLCT images for all parameters (27.1 ± 11.0 [standard deviation (SD)] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 6 mGy with SPCCT images, noise magnitude was reduced by 8.9 % compared to DLCT images at 10 mGy (34.8 ± 14.1 [SD] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 10 mGy and 6 mGy, average NPS spatial frequency (fav) was greater for SPCCT images (0.75 ± 0.17 [SD] mm-1) compared to DLCT images at 10 mGy (0.55 ± 0.04 [SD] mm-1) while remaining constant from 10 to 6 mGy. At 10 mGy, TTF at 50 % (f50) was greater for SPCCT images (0.92 ± 0.08 [SD] mm-1) compared to DLCT images (0.67 ± 0.06 [SD] mm-1) for both inserts. At 6 mGy, f50 decreased by 1.1 % for SPCCT images, while remaining greater compared to DLCT images at 10 mGy (0.91 ± 0.06 [SD] mm-1 vs. 0.67 ± 0.06 [SD] mm-1, respectively). At both dose levels, d' were greater for SPCCT images compared to DLCT for all clinical tasks. Subjective analysis performed by two radiologists revealed a greater median image quality for SPCCT (5; Q1, 4; Q3, 5) compared to DLCT images (3; Q1, 3; Q3, 3). CONCLUSION: UHR SPCCT outperforms DLCT in terms of image quality for lung imaging. In addition, UHR SPCCT contributes to a 40 % reduction in radiation dose compared to DLCT.

Comparison of the spectral performance between two dual-source CT systems on low-energy virtual monoenergetic images: A phantom study.

PURPOSE: To compare the spectral performance of two different DSCT (DSCT-Pulse and DSCT-Force) on virtual monoenergetic images (VMIs) at low energy levels. METHODS: An image quality phantom was scanned on the two DSCTs at three dose levels: 11/6/1.8 mGy. Level 3 of an advanced modeled iterative reconstruction algorithm was used. Noise power spectrum and task-based transfer function were computed on VMIs from 40 to 70 keV to assess noise magnitude and noise texture (fav) and spatial resolution (f50). A detectability index (d') was computed to assess the detection of one contrast-enhanced abdominal lesion as a function of the keV level used. RESULTS: For all dose levels and all energy levels, noise magnitude was significantly higher (p < 0.05) with DSCT-Pulse than with DSCT-Force (12.6 ± 2.7 % at 1.8 mGy, 9.1 ± 2.9 % at 6 mGy and 4.0 ± 2.7 % at 11 mGy). For all energy levels, fav values were significantly higher (p < 0.05) with DSCT-Pulse than with DSCT-Force at 1.8 mGy (4.8 ± 3.9 %) and at 6 mGy (5.5 ± 2.5 %) but similar at 11 mGy (0.2 ± 3.6 %; p = 0.518). For all energy levels, f50 values were significantly higher with DSCT-Pulse than with DSCT-Force (12.7 ± 5.6 % at 1.8 mGy, 17.9 ± 4.5 % at 6 mGy and 13.1 ± 2.6 % at 11 mGy). For all keV, similar d' values were found with both DSCT-Force and DSCT-Pulse at 11 mGy (-1.0 ± 3.1 %; p = 0.084). For other dose levels, d' values were significantly lower with DSCT-Pulse than with DSCT-Force (9.1 ± 3.2 % at 1.8 mGy and -6.3 ± 3.9 % at 6 mGy). CONCLUSION: Compared with the DSCT-Force, the DSCT-Pulse improved noise texture and spatial resolution, but noise magnitude was slightly higher and detectability slightly lower, particularly when the dose level was reduced.

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF50%, d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d' were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.

Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study.

PURPOSE: The purpose of this study was to assess image-quality and dose reduction potential using a photon-counting computed tomography (PCCT) system by comparison with two different dual-source CT (DSCT) systems using two phantoms. MATERIALS AND METHODS: Acquisitions on phantoms were performed using two DSCT systems (DSCT1 [Somatom Force] and DSCT2 [Somatom Pro.Pulse]) and one PCCT system (Naeotom Alpha) at four dose levels (13/6/3.4/1.8 mGy). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed to assess noise magnitude and noise texture and spatial resolution (f50), respectively. Detectability indexes (d') were computed to model the detection of abdominal lesions: one unenhanced high-contrast task, one contrast-enhanced high-contrast task and one unenhanced low-contrast task. Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. RESULTS: For all dose levels, noise magnitude values were lower with PCCT than with DSCTs. For all CT systems, similar noise texture values were found at 13 and 6 mGy, but the greatest noise texture values were found for DSCT2 and the lowest for PCCT at 3.4 and 1.8 mGy. For high-contrast inserts, similar or lower f50 values were found with PCCT than with DSCT1 and the opposite pattern was found for the low-contrast insert. For the three simulated lesions, d' values were greater with PCCT than with DSCTs. Abdominal images were rated satisfactory for clinical use by the radiologists for all dose levels with PCCT and for 13 and 6 mGy with DSCTs. CONCLUSION: By comparison with DSCTs, PCCT reduces image-noise and improves detectability of simulated abdominal lesions without altering the spatial resolution and image texture. Image-quality obtained with PCCT seem to indicate greater potential for dose optimization than those obtained with DSCTs.

Comparison of low-energy virtual monoenergetic images between photon-counting CT and energy-integrating detectors CT: A phantom study.

PURPOSE: The purpose of this study was to assess image quality and dose level using a photon-counting CT (PCCT) scanner by comparison with a dual-source CT (DSCT) scanner on virtual monoenergetic images (VMIs) at low energy levels. MATERIALS AND METHODS: A phantom was scanned using a DSCT and a PCCT with a volume CT dose index of 11 mGy, and additionally at 6 mGy and 1.8 mGy for PCCT. Noise power spectrum and task-based transfer function were evaluated from 40 to 70 keV on VMIs to assess noise magnitude and noise texture (fav) and spatial resolution on two iodine inserts (f50), respectively. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions according to the energy level used. RESULTS: For all energy levels, noise magnitude values were lower with PCCT than with DSCT at 11 and 6 mGy, but greater at 1.8 mGy. fav values were higher with PCCT than with DSCT at 11 mGy (8.6 ± 1.5 [standard deviation [SD]%), similar at 6 mGy (1.6 ± 1.5 [SD]%) and lower at 1.8 mGy (-17.8 ± 2.2 [SD]%). For both inserts, f50 values were higher with PCCT than DSCT at 11- and 6 mGy for all keV levels, except at 6 mGy and 40 keV. d' values were higher with PCCT than with DSCT at 11- and 6 mGy for all keV and both simulated lesions. Similar d' values to those of the DSCT at 11 mGy, were obtained at 2.25 mGy for iodine insert at 2 mg/mL and at 0.96 mGy for iodine insert at 4 mg/mL at 40 keV. CONCLUSION: Compared to DSCT, PCCT reduces noise magnitude and improves noise texture, spatial resolution and detectability on VMIs for all low-keV levels.

Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study.

PURPOSE: The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol. MATERIALS AND METHODS: Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d') was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter). RESULTS: Noise magnitude values were lower with PIQE than with AiCE (-13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f50) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f50 values were found to be close. Whatever the dose and DLR level, d' values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d' values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels. CONCLUSION: Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.

Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.

Objective. Digital breast tomosynthesis (DBT) is a quasi-three-dimensional breast imaging modality that improves breast cancer screening and diagnosis because it reduces fibroglandular tissue overlap compared with 2D mammography. However, DBT suffers from noise and blur problems that can lower the detectability of subtle signs of cancers such as microcalcifications (MCs). Our goal is to improve the image quality of DBT in terms of image noise and MC conspicuity.Approach. We proposed a model-based deep convolutional neural network (deep CNN or DCNN) regularized reconstruction (MDR) for DBT. It combined a model-based iterative reconstruction (MBIR) method that models the detector blur and correlated noise of the DBT system and the learning-based DCNN denoiser using the regularization-by-denoising framework. To facilitate the task-based image quality assessment, we also proposed two DCNN tools for image evaluation: a noise estimator (CNN-NE) trained to estimate the root-mean-square (RMS) noise of the images, and an MC classifier (CNN-MC) as a DCNN model observer to evaluate the detectability of clustered MCs in human subject DBTs.Main results. We demonstrated the efficacies of CNN-NE and CNN-MC on a set of physical phantom DBTs. The MDR method achieved low RMS noise and the highest detection area under the receiver operating characteristic curve (AUC) rankings evaluated by CNN-NE and CNN-MC among the reconstruction methods studied on an independent test set of human subject DBTs.Significance. The CNN-NE and CNN-MC may serve as a cost-effective surrogate for human observers to provide task-specific metrics for image quality comparisons. The proposed reconstruction method shows the promise of combining physics-based MBIR and learning-based DCNNs for DBT image reconstruction, which may potentially lead to lower dose and higher sensitivity and specificity for MC detection in breast cancer screening and diagnosis.

Impact of Phantom Size on Low-Energy Virtual Monoenergetic Images of Three Dual-Energy CT Platforms.

The purpose of this study was to compare the quality of low-energy virtual monoenergetic images (VMIs) obtained with three Dual-Energy CT (DECT) platforms according to the phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs (SFCT-1st and SFCT-2nd) and on one Dual-source CT (DSCT). The noise power spectrum (NPS), task-based transfer function (TTF), and detectability index (d') were assessed on VMIs from 40 to 70 keV. The highest noise magnitude values were found with SFCT-1st and noise magnitude was higher with DSCT than with SFCT-2nd for 26 cm (10.2% ± 1.3%) and 31 cm (7.0% ± 2.5%), and the opposite for 36 cm (-4.2% ± 2.5%). The highest average NPS spatial frequencies and TTF values at 50% (f50) values were found with DSCT. For all energy levels, the f50 values were higher with SFCT-2nd than SFCT-1st for 26 cm (3.2% ± 0.4%) and the opposite for 31 cm (-6.9% ± 0.5%) and 36 cm (-5.6% ± 0.7%). The lowest d' values were found with SFCT-1st. For all energy levels, the d' values were lower with DSCT than with SFCT-2nd for 26 cm (-6.2% ± 0.7%), similar for 31 cm (-0.3% ± 1.9%) and higher for 36 cm (5.4% ± 2.7%). In conclusion, compared to SFCT-1st, SFCT-2nd exhibited a lower noise magnitude and higher detectability values. Compared with DSCT, SFCT-2nd had a lower noise magnitude and higher detectability for the 26 cm, but the opposite was true for the 36 cm.

Spectral performance of two split-filter dual-energy CT systems: A phantom study.

BACKGROUND: Recently, a second generation of split filter dual-energy CT (SFCT) platform has been developed. The thicknesses of the gold and tin filters used to obtain both low- and high-energy spectra have been changed. These differences in filter thickness may affect the spectral separation between the two spectra and thus the quality of spectral images. PURPOSE: To compare the spectral performance of two Split-Filter Dual-Energy CT systems (SFCT-1st and SFCT-2nd ) on virtual monoenergetic images (VMIs) and iodine map. METHODS: A Multi-Energy CT phantom was scanned on two SFCT with a tube voltage of 120 kVp for both systems (SFCT-1st -120 and SFCT-2nd -120) and 140 kVp only for the second generation (SFCT-2nd -140). Acquisitions were performed on the phantom with a CTDIvol close to 11 mGy. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated on VMIs from 40 to 70 keV. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions on VMIs. Hounsfield Unit (HU) accuracy was assessed on VMIs and the accuracy of iodine concentration was assessed on iodine maps. RESULTS: For all keV, noise magnitude values were lower with the SFCT-2nd -120 than with the SFCT-1st -120 (on average: -22.5 ± 2.9%) and higher with the SFCT-2nd -140 than with the SFCT-2nd -120 (on average: 25.0 ± 6.2%). Average NPS spatial frequencies (fav ) were lower with the SFCT-1st -120 than with the SFCT-2nd -120 (-6.0 ± 0.5%) and the SFCT-2nd -140 (-3.6 ± 1.6%). Similar TTF50% values were found for both systems and both kVp for blood and iodine inserts at 2 mg/mL (0.29 ± 0.01 mm-1 ) and at 4 mg/mL (0.31 ± 0.01 mm-1 ). d' values peaked at 40 keV for the SFCT-2nd and at 70 keV for the SFCT-1st . Highest d' values were found for the SFCT-2nd -120 for both simulated lesions. Accuracy of HU values and iodine concentration was higher with the SFCT-2nd than with the SFCT 1st . CONCLUSION: Compared to the SFCT-1st , with similar spatial resolution and noise texture values, the SFCT-2nd -120 exhibited the lowest values for noise magnitude, the highest detectability index values, and more accurate HU values and iodine concentrations.

Impact of tin filter on the image quality of ultra-low dose chest CT: A phantom study on three CT systems.

PURPOSE: The purpose of this study was to assess the impact of a tin filter on the image quality of ultra-low dose (ULD) chest computed tomography (CT) on three different CT systems. MATERIALS AND METHODS: An image quality phantom was scanned on three CT systems including two split-filter dual-energy CT (SFCT-1 and SFCT-2) scanners and one dual-source CT scanner (DSCT). Acquisitions were performed with a volume CT dose index (CTDIvol) of 0.4 mGy, first at 100 kVp without tin filter (Sn), and second, at Sn100/Sn140 kVp, Sn100/Sn110/Sn120/Sn130/Sn140/Sn150 kVp and Sn100/Sn150 kVp for SFCT-1, SFCT-2 and DSCT respectively. Noise-power-spectrum and task-based transfer function were computed. The detectability index (d') was computed to model the detection of two chest lesions. RESULTS: For DSCT and SFCT-1, noise magnitude values were higher with 100kVp than with Sn100 kVp and with Sn140 kVp or Sn150 kVp than with Sn100 kVp. For SFCT-2, noise magnitude increased from Sn110 kVp to Sn150 kVp and was higher at Sn100 kVp than at Sn110 kVp. For most kVp with the tin filter, the noise amplitude values were lower than those obtained at 100 kVp. For each CT system, noise texture and spatial resolution values were similar with 100 kVp and with all kVp used with a tin filter. For all simulated chest lesions, the highest d' values were obtained at Sn100 kVp for SFCT-1 and DSCT and at Sn110 kVp for SFCT-2. CONCLUSION: For ULD chest CT protocols, the lowest noise magnitude and highest detectability values for simulated chest lesions are obtained with Sn100 kVp for the SFCT-1 and DSCT CT systems and at Sn110 kVp for SFCT-2.

Implementation of xSPECT, xSPECT bone and Broadquant from literature, clinical survey and innovative phantom study with task-based image quality assessment.

OBJECTIVE: From patient and phantom studies, we aimed to highlight an original implementation process and share a two-years experience clinical feedback on xSPECT (xS), xSPECT Bone (xB) and Broadquant quantification (Siemens) for 99mTc-bone and 177Lu-NET (neuroendocrine tumors) imaging. METHODS: Firstly, we checked the relevance of implemented protocols and Broadquant module on the basis of literature and with a homogeneous phantom study respectively. Then, we described xS and xB behaviours with reconstruction parameters (10i-0mm to 40i-20mm) and optimized the protocols through a blinded survey (7 physicians). Finally, the preferred 99mTc-bone reconstruction was assessed through an IEC NEMA phantom including liquid bone spheres. Conventional SNR, CNR, spatial resolution, Q.%error, and recovery curves; and innovative NPS, TTF and detectability score d' were performed (ImQuest software). We also sought to review the adoption of these tools in clinical routine and showed the potential of quantitative xB in the context of theranostics (Xofigo®). RESULTS: We showed the need of optimization of implemented reconstruction algorithms and pointed out a decay correction particularity with Broadquant. Preferred parameters were 1s-25i-8mm and 1s-25i-5mm for xS/xB-bone and xS-NET imaging respectively. The phantom study highlighted the different image quality especially for the enhanced spatial resolution xB algorithm (1/TTF10%=2.1 mm) and showed F3D and xB shared the best performances in terms of image quality and quantification. xS was generally less efficient. CONCLUSIONS: Qualitative F3D still remains the clinical standard, xB and Broadquant offer challenging perspectives in theranostics. We introduced the potential of innovative metrics for image quality analysis and showed how CT tools should be adapted to fit nuclear medicine imaging.

Dose-efficiency quantification of computed tomography systems using a model-observer.

BACKGROUND: Recent advances in computed tomography (CT) technology have considerably improved the quality of CT images and reduced radiation exposure in patients. At present, however, there is no generally accepted figure of merit (FOM) for comparing the dose efficiencies of CT systems. PURPOSE: (i) To establish an FOM that characterizes the quality of CT images in relation to the radiation dose by means of a mathematical model observer and (ii) to evaluate the new FOM on different CT systems and image reconstruction algorithms. METHODS: Images of a homogeneous phantom with four low-contrast inserts were acquired using three different CT systems at three dose levels and a representative protocol for CT imaging of low-contrast objects in the abdomen. The images were reconstructed using filtered-back projection and iterative algorithms. A channelized hotelling observer with difference-of-Gaussian channels was applied to compute the detectability ( d ' $d^{\prime}$ ). This was done for each insert and each of the considered imaging conditions from square regions of interest (ROIs) that were (semi-)automatically centered on the inserts. The estimated detectabilities ( d ' $d^{\prime}$ ) were averaged in the first step over the three dose levels ( ⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ ), and subsequently over the four contrast inserts ( ⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ ). All calculation steps included a dedicated assessment of the related uncertainties following accepted metrological guidelines. RESULTS: The determined detectabilities ( d ' $d^{\prime}$ ) varied considerably with the contrast and diameter of the four inserts, as well as with the radiation doses and reconstruction algorithms used for image generation ( d ' $d^{\prime}\;$ = 1.3-5.5). Thus, the specification of a single detectability as an FOM is not well suited for comprehensively characterizing the dose efficiency of a CT system. A more comprehensive and robust characterization was provided by the averaged detectabilities ⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ and, in particular, ⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ . Our analysis reveals that the model observer analysis is very sensitive to the exact position of the ROIs. CONCLUSIONS: The presented automatable software approach yielded with the weighted detectability ⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ an objective FOM to benchmark different CT systems and reconstruction algorithms in a robust and reliable manner. An essential advantage of the proposed model-observer approach is that uncertainties in the FOM can be provided, which is an indispensable prerequisite for type testing.

Task-Based Image Quality Assessment Comparing Classical and Iterative Cone Beam CT Images on Halcyon®.

BACKGROUND: Despite the development of iterative reconstruction (IR) in diagnostic imaging, CBCT are generally reconstructed with filtered back projection (FBP) in radiotherapy. Varian medical systems, recently released with their latest Halcyon® V2.0 accelerator, a new IR algorithm for CBCT reconstruction. PURPOSE: To assess the image quality of radiotherapy CBCT images reconstructed with FBP and an IR algorithm. METHODS: Three CBCT acquisition modes (head, thorax and pelvis large) available on a Halcyon® were assessed. Five acquisitions were performed for all modes on an image quality phantom and reconstructed with FBP and IR. Task-based image quality assessment was performed with noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d'). To illustrate the image quality obtained with both reconstruction types, CBCT acquisitions were made on 6 patients. RESULTS: The noise magnitude and the spatial frequency of the NPS peak was lower with IR than with FBP for all modes. For all low and high-contrast inserts, the values for TTF at 50% were higher with IR than with FBP. For all inserts and all modes, the contrast values were similar with FBP and IR. For all low and high-contrast simulated lesions, d' values were higher with IR than with FBP for all modes. These results were also found on the 6 patients where the images were less noisy but smoother with IR-CBCT. CONCLUSIONS: Using the IR algorithm for CBCT images in radiotherapy improve image quality and thus could increase the accuracy of online registration and limit positioning errors during processing.

Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study.

PURPOSE: The purpose of this study was to assess the impact of the new artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and radiation dose compared with iterative reconstruction algorithm in lumbar spine computed tomography (CT) examination. MATERIALS AND METHODS: Acquisitions on phantoms were performed using a tube current modulation system for four DoseRight Indexes (DRI) (i.e., 26/23/20/15). Raw data were reconstructed using the Level 4 of iDose4 (i4) and three levels of AI-DLR (Smoother/Smooth/Standard) with a bone reconstruction kernel. The Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed (d' modeled detection of a lytic and a sclerotic bone lesions). Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. RESULTS: The Noise magnitude was lower with AI-DLR than i4 and decreased from Standard to Smooth (-31 ± 0.1 [SD]%) and Smooth to Smoother (-48 ± 0.1 [SD]%). The average NPS spatial frequency was similar with i4 (0.43 ± 0.01 [SD] mm-1) and Standard (0.42 ± 0.01 [SD] mm-1) but decreased from Standard to Smoother (0.36 ± 0.01 [SD] mm-1). TTF values at 50% decreased as the dose decreased but were similar with i4 and all AI-DLR levels. For both simulated lesions, d' values increased from Standard to Smoother levels. Higher detectabilities were found with a DRI at 15 and Smooth and Smoother levels than with a DRI at 26 and i4. The images obtained with these dose and AI-DLR levels were rated satisfactory for clinical use by the radiologists. CONCLUSION: Using Smooth and Smoother levels with CT allows a significant dose reduction (up to 72%) with a high detectability of lytic and sclerotic bone lesions and a clinical overall image quality.

iQMetrix-CT: New software for task-based image quality assessment of phantom CT images.

The purpose of this article was to explain how the new iQMetrix-CT software works, as well as to describe its current potential and discuss its future applications. iQMetrix-CT was developed by a working group from the French Society of Medical Physicists (SFPM). This software calculates three advanced metrics, which have been adapted to take into account the non-linear and non-stationary properties of iterative reconstruction algorithms. Noise power spectrum is calculated to assess noise texture and noise magnitude in the frequency domain. The task-based transfer function is computed to assess the spatial resolution under conditions of contrast and noise similar to the lesions encountered in clinical practice. Finally, the detectability index is used to estimate the radiologist's ability to perform a given task such as detecting a simulated lesion. These metrics are very useful to evaluate the performance of reconstruction algorithms in term of image quality and optimize the doses of a given protocol or to evaluate and compare a new technology.

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

BACKGROUND: Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels. PURPOSE: To assess the impact of an artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications. METHODS: Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU-41 phantoms were performed at five dose levels (CTDIvol : 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose4 levels 4 (i4) and 7 (i7)), and five levels of AI-DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task-based transfer function, and detectability index (d') were computed: d'-modeled detection of a soft tissue mediastinal nodule (low-contrast soft tissue chest nodule within the mediastinum [LCN]), ground-glass opacity (GGO), or high-contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four- or five-point scale. RESULTS: From Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: -66.3% ± 0.5% for mediastinal images and -63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: -35.3% ± 2.2% for mediastinal images and -13.3% ± 2.2% for parenchymal images), and the detectability (d') of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low-contrast polyethylene insert and the opposite for the high-contrast air insert. Compared to the i4 used in clinical practice, d' values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required. CONCLUSION: With AI-DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction.

BACKGROUND: To assess the spectral performance of rapid kV switching dual-energy CT (KVSCT-Canon) equipped with a Deep-Learning spectral reconstruction algorithm on virtual-monoenergetic images at low-energy levels and to compare its performances with four other dual-energy CT (DECT) platforms equipped with iterative reconstruction algorithms. METHODS: Two CT phantoms were scanned on five DECT platforms: KVSCT-Canon, fast kV-switching CT (KVSCT-GE), split filter CT, dual-source CT (DSCT), and dual-layer CT (DLCT). The classical parameters of abdomen-pelvic examinations were used for all phantom acquisitions, and a CTDIvol close to 10 mGy. For KVSCT-Canon, virtual-monoenergetic images were reconstructed with a clinical slice thickness of 0.5 and 1.5 mm to be close to other platforms. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 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. RESULTS: For KVSCT-Canon, the noise magnitude and average NPS spatial frequency (fav) decreased from 40 to 70 keV and increased thereafter. Similar noise magnitude outcomes were found for KVSCT-GE but the opposite for fav. For the other DECT platforms, the noise magnitude decreased as the keV increased. For split filter CT, DSCT and DLCT, the fav values increased from 40 to 80 keV. For all DECT platforms, TTF at 50% (f50) decreased as the keV increased, decreasing spatial resolution. For KVSCT-Canon, d' values peaked at 60 and 70 keV for both simulated lesions and from 50 to 70 keV for KVSCT-GE. d' decreased between 40 and 70 keV for DSCT, DLCT and split filter CT. For KVSCT-Canon, the increase in slice thickness decreases noise magnitude, fav and f50 and increases d' values. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV. CONCLUSIONS: For KVSCT-Canon, the detectability of contrast-enhanced lesions was highest at 60 keV. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV.