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

Optimizing CT Imaging Parameters: Implications for Diagnostic Accuracy in Nuclear Medicine

X-ray computed tomography (CT) is an important companion modality in molecular imaging, offering attenuation correction (AC) of single-photon emission computed tomography (SPECT) - and positron emission tomography (PET)-data, topographic information in scans as well as changes in morphology in serial follow-up studies. Image quality plays a critical role in delivering an acceptable diagnosis and in medical treatment planning. Variability in protocols can present a considerable challenge in achieving consistent image quality within departments. The differences in CT scanning protocol metrics established by various manufacturers and across different generations of scanners can contribute to this issue, making the standardization of image quality a complex task. This review aims to present relevant literature herein and provide an introduction of the CT imaging parameters, including acquisition factors, reconstruction algorithms, and relevant image quality metrics, and discuss possible ways to implement a robust CT protocol review process in a nuclear medicine department. We also evaluate the potential of iterative reconstruction (IR) and deep learning (DL) for enhancing image quality and minimizing exposure doses. This article points to the need for periodic audit of image quality to guarantee that CT protocols are suited for the intended purpose. Through the creation of local diagnostic reference levels and monitoring performance through protocol management, physicians may aim at delivering high quality imaging services consistently adhering to the principles of ALARA and reduction of dose for both patients and workers.

Gaussian Function Model for Task-Specific Evaluation in Medical Imaging: A Theoretical Investigation

In medical image diagnosis, understanding image characteristics is crucial for selecting and optimizing imaging systems and advancing their development. Objective image quality assessments, based on specific diagnostic tasks, have become a standard in medical image analysis, bridging the gap between experimental observations and clinical applications. However, conventional task-based assessments often rely on ideal observer models that assume target signals have circular shapes with well-defined edges. This simplification rarely reflects the true complexity of lesion morphology, where edges exhibit variability. This study proposes a more practical approach by employing a Gaussian distribution to represent target signal shapes. This study explicitly derives the task function for Gaussian signals and evaluates the detectability index through simulations based on head computed tomography (CT) images with low-contrast lesions. Detectability indices were calculated for both circular and Gaussian signals using non-prewhitening and Hotelling observer models. The results demonstrate that Gaussian signals consistently exhibit lower detectability indices compared to circular signals, with differences becoming more pronounced for larger signal sizes. Simulated images closely resembling actual CT images confirm the validity of these calculations. These findings quantitatively clarify the influence of signal shape on detection performance, highlighting the limitations of conventional circular models. Thus, it provides a theoretical framework for task-based assessments in medical imaging, offering improved accuracy and clinical relevance for future advancements in the field.

Image quality and dose reduction with photon counting detector CT: Comparison between ultra-high resolution mode and standard mode using a phantom study

PURPOSE

The purpose of this study was to assess the image quality and dose reduction potential of ultra-high resolution (UHR) mode compared with standard mode, both available on a commercial photon-counting detector computed tomography (PCCT) scanner.

MATERIALS AND METHODS

Images were acquired on a PCCT with a phantom using UHR and standard modes at three dose levels (3/6/12 mGy). Raw data were reconstructed using soft tissue (Br36) and bone (Br68) reconstruction kernels and 0.4-mm slice thickness. Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated to assess noise magnitude, noise texture (fav), and spatial resolution (f50), respectively. Detectability indexes (d') were calculated to model the detection of two abdominal lesions for a Br36 soft tissue reconstruction kernel and three bone lesions for a Br68 bone reconstruction kernel.

RESULTS

At all dose levels, noise magnitude values were lower with UHR than with standard mode (mean difference, -18.0 ± 2.6 [standard deviation (SD)] % for Br36 and -33.9 ± 2.3 [SD] % for Br68). Noise texture was lower with UHR than with standard mode (mean difference, -4.2 ± 0.9 [SD] % for Br36 and -16.0 ± 1.8 [SD] % for Br68). For the solid water insert and Br36, f50 values were similar for both UHR (0.34 ± [SD] 0.04 mm-1) and standard (0.33 ± [SD] 0.04 mm-1) modes. For Br68, f50 values were greater with UHR than with standard for iodine (mean difference, 18.5 ± 1.9 [SD] %) and bone (11.7 ± 5.7 [SD] %) inserts. For all simulated lesions, d' values were greater with UHR than with standard and, compared to standard, the dose reduction potential with UHR was -32.9 ± 0.0 (SD) % for abdominal lesions and -68.7 ± 3.2 (SD) % for bone lesions.

CONCLUSION

Compared to the standard mode, the UHR mode offers lower noise levels and better detectability of abdominal and bone lesions, paving the way for potential dose reduction with PCCT in clinical applications.

Model observers and detectability index in x-ray imaging: historical review, applications and future trends

The detectability index, originally developed in psychophysics, has been applied in medical imaging to integrate objective metrics with subjective assessments. This index accounts for both image processing properties and the limitations of the human visual system, thus enhancing the clinical efficacy of imaging technologies. By providing a single metric that captures multiple aspects of image quality, the detectability index offers a comprehensive evaluation of clinical images. Numerous applications of this index across various areas of medical imaging are documented in the literature, along with recommendations for its use in periodic performance evaluations of imaging devices. However, since different modalities of images may require different detectability indices, it is crucial to assess the adequacy of the properties of the image being analyzed and those from the adopted index. A thorough understanding of this metric, including its statistical nature and complex relationship with model observers, is essential to ensure its proper application and interpretation, and to prevent misuse. Medical physicists face the challenge of a lack of organized guidance on the detectability index, necessitating a comprehensive review of its merits and drawbacks. This paper aims to trace the origins, concepts, and clinical applications of the detectability index, offering insight into its strengths, limitations, and future potential. To achieve this, an extensive literature review was conducted, covering the evolution of the index from its early use in radar interpretation to its current applications in modern imaging techniques and future trends. The paper includes supplementary materials such as a compendium of fundamental concepts, ancillary information, and mathematical deductions to help readers less experienced in the subject.

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.

Statistical phase alignment of edge spread function for modulation transfer function measurement on computed tomography images

PURPOSE

This study aimed to develop a statistical approach for edge spread function (ESF) phase alignment to improve the accuracy of modulation transfer function (MTF) measurements at the edges of computed tomography (CT) images.

METHODS

A statistical approach to ESF phase alignment was initiated by collecting ESF samples with poor phase alignment. One ESF sample was selected as the reference ESF and the other as the treated ESF. The treated ESF was then shifted by 10-pixels in the right and left directions with a 1-pixel increment at each shift. The mean squared error (MSE) for each shift was calculated between the shifted and reference ESF, and the position with the minimum MSE indicated the best phase alignment between the two ESFs. All shifted ESFs were averaged and differentiated to obtain a single-line spread function (LSF). The MTF was generated by Fourier transformation of the LSF. The MTFs from the shifted ESF and the non-shifted MTF from images of the ACR CT, point-computational, CTDI, and anthropomorphic phantoms were compared.

RESULTS

The MTF curves obtained after the phase alignment showed higher and more consistent results than those obtained before the alignment. The MTF curves obtained after phase alignment were comparable to those obtained from a point computational phantom. Our method showed improved accuracy in measuring spatial resolution compared to those without the edge-shifting method.

CONCLUSIONS

The results showed that a statistical approach for ESF phase alignment can overcome poor phase alignment and produce a more accurate MTF curve.

A statistical-based automatic detection of a low-contrast object in the ACR CT phantom for measuring contrast-to-noise ratio of CT images

Purpose. This study aimed to develop a new method for automated contrast-to-noise ratio (CNR) measurement using the low-contrast object in the ACR computed tomography (CT) phantom.Methods. The proposed method for CNR measurement was based on statistical criteria. A region of interest (ROI) was placed in a specific radial location and was then rotated around 360° in increments of 2°. At each position, the average CT number within the ROI was calculated. After one complete rotation, a profile of the average CT number around the full rotation was obtained. The center coordinate of the low-contrast object was determined from the maximum value of the profile. The CNR was calculated based on the average CT number and noise within the ROI in the low-contrast object and the ROI in the background, i.e., at the center of the phantom. The proposed method was used to evaluate CNR from images scanned with various phantom rotations, images with various noise levels (tube currents), and images from 25 CT scanners. The results were compared to a previous method based on a threshold approach.Results. The proposed method successfully placed the ROI properly in the center of a low-contrast object for variations of phantom rotation and tube current, whereas was not properly located in the center of the low-contrast object using the previous method. In addition, from 325 image samples of the 25 CT scanners, the proposed method successfully (100%) located the ROI within the low-contrast objects of all images used. The success rate of the previous method was only 58%.Conclusion. A new method for measuring CNR in the ACR CT phantom has been proposed and implemented. It is more powerful than a previous method based on a threshold approach.

Cross-Domain Denoising for Low-Dose Multi-Frame Spiral Computed Tomography

Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing issue in conventional cascaded frameworks caused by aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70% of noise without compromised spatial resolution, while subjective evaluations by two experienced radiologists further supported its superior performance against state-of-the-art methods in clinical practice. Code is available at https://github.com/YCL92/TMD-LDCT.

Image Quality Comparison of Three 3D Mobile X-Ray Imaging Guidance Devices Used in Spine Surgery: A Phantom Study

An image-quality CT phantom was scanned with three different 3D X-ray imaging guidance devices in the operating theatre: O-Arm, Loop-X, and Airo TruCT. Default acquisition and reconstruction parameters for lumbar spine procedures were used on each device. The tube current was set to a dose level of around 27 mGy. A task-based image quality assessment was performed by calculating the noise power spectrum (NPS) and task transfer function (TTF). A detectability index (d') was calculated for three simulated bone lesions. The noise magnitude of the O-Arm was higher than the Airo TruCT, and the Loop-X had higher noise than the Airo TruCT. The highest average NPS frequency was for the O-Arm images, and the lowest was for the Loop-X. The TTFs at 50% values were similar for the Airo TruCT and Loop-X devices. Compared to Airo TruCT, the TTF at 50% value increased with the O-Arm by 53.12% and 41.20% for the Teflon and Delrin inserts, respectively. Compared to Airo TruCT, the d' value was lower with Loop-X by -26.73%, -27.02%, and -23.95% for lytic lesions, sclerotic lesions, and high-density bone, respectively. Each 3D-imaging spine surgery guidance device has its own strengths and weaknesses in terms of image quality. Cone-beam CT systems apparently offer the best compromise between noise and spatial resolution for spine surgery.

360° CZT-SPECT/CT cameras: 99mTc- and 177Lu-phantom-based evaluation under clinical conditions

PURPOSE

For the first time, three currently available 360° CZT-SPECT/CT cameras were compared under clinical conditions using phantom-based measurements.

METHODS

A 99mTc- and a 177Lu-customized NEMA IEC body phantom were imaged with three different cameras, StarGuide (GE Healthcare), VERITON-CT versions 200 (V200) and 400 (V400) (Spectrum Dynamics Medical) under the same clinical conditions. Energy resolution and volumetric sensitivity were evaluated from energy spectra. Vendors provided the best reconstruction parameters dedicated to visualization and/or quantification, based on their respective software developments. For both 99mTc- and 177Lu-phantoms, noise level, quantification accuracy, and recovery coefficient (RC) were performed with 3DSlicer. Image quality metrics from an approach called "task-based" were computed with iQMetrix-CT on 99mTc visual reconstructions to assess, through spatial frequencies, noise texture in the background (NPS) and contrast restitution of a hot insert (TTF). Spatial resolution indices were calculated from frequencies corresponding to TTF10% and TTF50%.

RESULTS

Despite the higher sensitivity of VERITON cameras and the enhanced energy resolution of the V400 (3.2% at 140 keV, 5.2% at 113 keV, and 3.6% at 208 keV), StarGuide presents comparable image quality. This highlights the need to differentiate sensitivity from count quality, which is influenced by hardware design (collimator, detector block) and conditions image quality as well as the reconstruction process (algorithms, scatter correction, noise regulation). For 99mTc imaging, the quantitative image optimization approach based on RCmean for StarGuide versus RCmax for V200 and V400 systems (RCmean/RCmax: 0.9/1.8; 0.5/0.9; 0.5/0.9 respectively-Ø37 mm). SRTB10/50 showed nearly equivalent spatial resolution performances across the different reconstructed images. For 177Lu imaging, the 113 keV imaging of the V200 and V400 systems demonstrated strong performances in both image quality and quantification, while StarGuide and V400 systems offer even better potential due to their ability to exploit signals from both the 113 and 208 keV peaks. 177Lu quantification was optimized according to RCmax for all cameras and reconstructions (1.07 ± 0.09-Ø37 mm).

CONCLUSIONS

The three cameras have equivalent potential for 99mTc imaging, while StarGuide and V400 have demonstrated higher potential for 177Lu. Dedicated visual or quantitative reconstructions offer better specific performances compared to the unified visual/quantitative reconstruction. The task-based approach appears to be promising for in-depth comparison of images in the context of system characterization/comparison and protocol optimization.

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.

Standardization of scan protocols for RT CT simulator from different vendors using quantitative image quality technique

OBJECTIVE

To investigate the feasibility of standardizing RT simulation CT scanner protocols between vendors using target-based image quality (IQ) metrics.

METHOD AND MATERIALS

A systematic assessment process in phantom was developed to standardize clinical scan protocols for scanners from different vendors following these steps: (a) images were acquired by varying CTDIvol and using an iterative reconstruction (IR) method (IR: iDose and model-based iterative reconstruction [IMR] of CTp-Philips Big Bore scanner, SAFIRE of CTs-Siemens biograph PETCT scanner), (b) CT exams were classified into body and brain protocols, (c) the rescaled noise power spectrum (NPS) was calculated, (d) quantified the IQ change due to varied CTDIvol and IR, and (e) matched the IR strength level. IQ metrics included noise and texture from NPS, contrast, and contrast-to-noise ratio (CNR), low contrast detectability (d'). Area under curve (AUC) of the receiver operation characteristic curve of d' was calculated and compared.

RESULTS

The level of change in the IQ ratio was significant (>0.6) when using IMR. The IQ ratio change was relatively low to moderate when using either iDose in CTp (0.1-0.5) or SAFIRE in CTs (0.1-0.6). SAFIRE-2 in CTs showed a closer match to the reference body protocol when compared to iDose-3 in CTp. In the brain protocol, iDose-3 in CTp could be matched to the low to moderate level of SAFIRE in CTs. The AUC of d' was highest when using IMR in CTp with lower CTDIvol, and SAFIRE in CTs performed better than iDose in CTp CONCLUSION: It is possible to use target-based IQ metrics to evaluate the performance of the system and operations across various scanners in a phantom. This can serve as an initial reference to convert clinical scanned protocols from one CT simulation scanner to another.

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.

Multicentric characterization of organ-based tube current modulation in head computed tomography: A dosimetric and image quality study

PURPOSE

To evaluate the efficiency of organ-based tube current modulation (OBTCM) in head Computed Tomography (CT) for different radiology departments and manufacturers.

MATERIALS AND METHODS

Five CT scanners from four radiology departments were evaluated in this study. All scans were performed using a standard and a routine head protocol. A scintillating fiber optic detector was placed directly on the gantry to measure the tube exit kerma. Image quality was quantified on a 16-cm HEAD phantom by measuring the signal-to-noise ratio (SNR) and the standard deviation of the Hounsfield units (HU) of circular regions of interest placed in the phantom. The Noise Power Spectrum (NPS) was also studied. Measured values were compared on images with and without OBTCM.

RESULTS

The reduction rates in tube exit kerma, on the anterior part, vary between 11 % and 74 % depending on the CT scanner and the protocol used. The tube exit kerma on the posterior part remains unchanged in GE and Canon CT scanners. On the contrary, the tube exit kerma to the posterior part increases by up to 39 % in Siemens CT scanner. Image noise and SNR increase by up to 10 % in the five CT scanners. Nonetheless, the differences in noise and SNR are statistically significant (p-value < 0.05).The analysis of the NPS indicates that the noise texture remains unchanged.

CONCLUSION

OBTCM reduces the tube exit kerma to the anterior part of the gantry without reducing substantially image quality for head protocols.

Ultra-high-resolution CT of the temporal bone: Comparison between deep learning reconstruction and hybrid and model-based iterative reconstruction

PURPOSE

The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT.

MATERIALS AND METHODS

CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms.

RESULTS

UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (P < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (P < 0.032).

CONCLUSION

UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.

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.

Method for measuring noise-power spectrum independent of the effect of extracting the region of interest from a noise image

This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPSR=P. NPSsR=256, 128, 64, 32, 16, and 8 were obtained and compared to assess their dependency on ROI size. In the proposed method, the true NPS was numerically modeled as an NPSmodel, with adjustable parameters, and a noise image with the property of the NPSmodel was generated. From the generated noise image, the NPS was measured using the conventional method with a P × P pixel ROI size; the obtained NPS was referred to as NPS'R=P. The adjustable parameters of the NPSmodel were optimized such that NPS'R=P was most similar to NPSR=P. When NPS'R=P was almost equivalent to NPSR=P, the NPSmodel was considered the true NPS. NPSsR=256, 128, 64, 32, 16, and 8 obtained using the conventional method were dependent on the ROI size. Conversely, the NPSs (optimized NPSsmodel) measured using the proposed method were not dependent on the ROI size, even when a much smaller ROI (P = 16 or 8) was used. The proposed method for NPS measurement was confirmed to be precise, independent of the ROI size, and useful for measuring local NPSs using a small ROI.

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.

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.

Virtual monochromatic images for coronary artery imaging with a spectral photon-counting CT in comparison to dual-layer CT systems: a phantom and a preliminary human study

OBJECTIVES

To evaluate the quality of virtual monochromatic images (VMIs) from spectral photon-counting CT (SPCCT) and two energy-integrating detector dual-energy CT (EID-DECT) scanners from the same manufacturer, for the coronary lumen.

METHODS

A 21-cm section of the Mercury v4.0 phantom was scanned using a cardiac CT protocol. VMIs from 40 to 90 keV were reconstructed using high-resolution (HR) parameters for EID-DECT and SPCCT (CB and HRB kernels at 0.67 mm slice thickness, respectively). Ultra-high-resolution (UHR) parameters were used in addition to SPCCT (detailed-2 kernel, 0.43 mm slice thickness). Noise-power-spectrum (NPS), task-based transfer function (TTF), and detectability index (d') were computed for 2-mm-diameter lumen detection. In consensus, two radiologists analyzed the quality of the images from 8 patients who underwent coronary CTA on both CT systems.

RESULTS

For all keV images, fpeak, f50, and d' were higher with SPCCT. The fpeak and f50 were higher with UHR-SPCCT with greater noise and lower d' compared to those of the HR-SPCCT images. Noise magnitude was constant for all energy levels (keV) with both systems, and lower with HR images, and d' decreased as keV decreased. Subjective analysis showed greater lumen sharpness and overall quality for HR and UHR-SPCCT images using all keV, with a greater difference at low keV compared to HR-EID-DECT images.

CONCLUSION

HR and UHR-SPCCT images gave greater detectability of the coronary lumen for 40 to 90 keV VMIs compared to two EID-DECT systems, with benefits of higher lumen sharpness and overall quality.

KEY POINTS

• Compared with 2 dual-energy CT systems, spectral photon-counting CT (SPCCT) improved spatial resolution, noise texture, noise magnitude, and detectability of the coronary lumen. • Use of ultra-high-resolution parameters with SPCCT improved spatial resolution and noise texture and provided high detectability of the coronary lumen, despite an increase in noise magnitude. • In eight patients, radiologists found greater overall image quality with SPCCT for all virtual monochromatic images with a greater difference at low keV, compared with dual-energy CT systems.

Impact of the automatic tube current modulation (ATCM) system on virtual monoenergetic image quality for dual-source CT: A phantom study

PURPOSE

To assess the impact of the automatic tube current modulation (ATCM) on virtual monoenergetic images (VMIs) quality in dual-source CT(DSCT).

MATERIALS AND METHODS

Acquisitions were performed on DSCT using the Mercury phantom. The acquisition parameters for an abdomen-pelvic examination with single-energy CT(SECT) and dual-energy CT(DECT) imaging were used. Acquisitions were performed for each imaging mode using fixed mAs and ATCM. The mAs value was set to obtain a volume CT dose index of 11 mGy in fixed mAs acquisitions. This value was used as the reference mAs in ATCM acquisitions. The noise power spectrum and task-based transfer function at 40,50,60 and 70 keV levels were computed on VMIs and SECT images. The detectability index (d') was calculated for a lesion with an iodine concentration of 10 mg/mL.

RESULTS

The noise magnitude on VMIs was higher with the ATCM system than with fixed mAs for all energy levels and section diameters of 21,26 and 31 cm. The noise texture and spatial resolution were similar between the fixed mAs and ATCM acquisitions for both imaging modes. The d' values were lower for all energy levels with ATCM than with fixed mAs acquisitions for 21 and 26 cm diameters by -39.82 ± 9.32%, similar at 31 cm diameter -4.13 ± 0.24% and higher at 36 cm diameter 10.40 ± 6.69%. It was higher on VMIs at all energy levels compared to SECT images.

CONCLUSIONS

The ATCM system could be used with DECT imaging to optimize patient exposure without changing the noise texture and spatial resolution of VMIs compared to fixed mAs and SECT.

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.

Spectral CT imaging: Technical principles of dual-energy CT and multi-energy photon-counting CT

Spectral computed tomography (CT) imaging encompasses a unique generation of CT systems based on a simple principle that makes use of the energy-dependent information present in CT images. Over the past two decades this principle has been expanded with the introduction of dual-energy CT systems. The first generation of spectral CT systems, represented either by dual-source or dual-layer technology, opened up a new imaging approach in the radiology community with their ability to overcome the limitations of tissue characterization encountered with conventional CT. Its expansion worldwide can also be considered as an important leverage for the recent groundbreaking technology based on a new chain of detection available on photon counting CT systems, which holds great promise for extending CT towards multi-energy CT imaging. The purpose of this article was to detail the basic principles and techniques of spectral CT with a particular emphasis on the newest technical developments of dual-energy and multi-energy CT systems.

Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study

OBJECTIVES

To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications.

METHODS

Acquisitions on phantoms were performed at 5 dose levels (CTDI_vol: 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose⁴ (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed: d' modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists.

RESULTS

From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d') of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d' values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level.

CONCLUSION

Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality.

KEY POINTS

• Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.