Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions

RATIONALE AND OBJECTIVES

To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).

MATERIALS AND METHODS

A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction. Quantitative assessment was performed using region of interest (ROI) analysis of eleven different anatomical areas, measuring absolute attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). In addition, CNR of clot compared to normally opacified lumen was calculated in cases that were positive for PE. For qualitative analysis, four different keV levels (40-60-80-100) were evaluated.

RESULTS

The image noise was significantly lower, and the cardiovascular SNR (24.9 ± 5.85 vs. 21.98 ± 5.49) and CNR (23.72 ± 8.00 vs. 20.31 ± 6.44) were significantly higher, on strong Deep Learning Spectral reconstruction (DLSR) than standard DLSR (p < 0.0001). PE-specific CNR (8.58 ± 4.47 vs. 6.25 ± 3.19) was significantly higher on strong DLSR than standard (p < 0.0001). The subjective image quality scores were diagnostically acceptable at four different keV levels (40-60-80-100 keV) evaluated using both standard and strong DLSR, with no qualitative differences observed at those energies.

CONCLUSION

Strong DLSR improves image quality with an increase of the SNR and CNR in DECT-PA compared to standard DLSR.

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.

Radiation dose reduction and image quality improvement with ultra-high resolution temporal bone CT using deep learning-based reconstruction: An anatomical study

PURPOSE

The purpose of this study was to evaluate the achievable radiation dose reduction of an ultra-high resolution computed tomography (UHR-CT) scanner using deep learning reconstruction (DLR) while maintaining temporal bone image quality equal to or better than high-resolution CT (HR-CT).

MATERIALS AND METHODS

UHR-CT acquisitions were performed with variable tube voltages and currents at eight different dose levels (volumic CT dose index [CTDIvol] range: 4.6-79 mGy), 10242 matrix, and 0.25 mm slice thickness and reconstructed using DLR and hybrid iterative reconstruction (HIR) algorithms. HR-CT images were acquired using a standard protocol (120 kV/220 mAs; CTDI vol, 54.2 mGy, 5122 matrix, and 0.5 mm slice thickness). Two radiologists rated the image quality of seven structures using a five point confidence scale on six cadaveric temporal bone CTs. A global image quality score was obtained for each CT protocol by summing the image quality scores of all structures.

RESULTS

With DLR, UHR-CT at 120 kV/220 mAs (CTDIvol, 50.9 mGy) and 140 kV/220 mAs (CTDIvol, 79 mGy) received the highest global image quality scores (4.88 ± 0.32 [standard deviation (SD)] [range: 4-5] and 4.85 ± 0.35 [range: 4-5], respectively; P = 0.31), while HR-CT at 120 kV/220 mAs and UHR-CT at 120 kV/20 mAs received the lowest (i.e., 3.14 ± 0.75 [SD] [range: 2-5] and 2.97 ± 0.86 [SD] [range: 1-5], respectively; P = 0.14). All the DLR protocols had better image quality scores than HR-CT with HIR.

CONCLUSION

UHR-CT with DLR can be performed with up to a tenfold reduction in radiation dose compared to HR-CT with HIR while maintaining or improving image quality.

Influence of Deep Learning Based Image Reconstruction on Quantitative Results of Coronary Artery Calcium Scoring

RATIONALE AND OBJECTIVES

To assess the impact of deep learning-based imaging reconstruction (DLIR) on quantitative results of coronary artery calcium scoring (CACS) and to evaluate the potential of DLIR for radiation dose reduction in CACS.

METHODS

For a retrospective cohort of 100 consecutive patients (mean age 62 ±10 years, 40% female), CACS scans were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR-V in 30%, 60% and 90% strength) and DLIR in low, medium and high strength. CACS was quantified semi-automatically and compared between image reconstructions. In a phantom study, a cardiac calcification insert was scanned inside an anthropomorphic thorax phantom at standard dose, 50% dose and 25% dose. FBP reconstructions at standard dose served as the reference standard.

RESULTS

In the patient study, DLIR led to a mean underestimation of Agatston score by 3.5, 6.4 and 11.6 points at low, medium and high strength, respectively. This underestimation of Agatston score was less pronounced for DLIR than for ASiR-V. In the phantom study, quantitative CACS results increased with reduced radiation dose and decreased with increasing strength of DLIR. Medium strength DLIR reconstruction at 50% dose reduction and high strength DLIR reconstruction at 75% dose reduction resulted in quantitative CACS results that were comparable to FBP reconstructions at standard dose.

CONCLUSION

Compared to FBP as the historical reference standard, DLIR leads to an underestimation of CACS but this underestimation is more moderate than with ASiR-V. DLIR can offset the increase in image noise and calcium score at reduced dose and may thus allow for substantial radiation dose reductions in CACS studies.

A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms

PURPOSE

To characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images.

METHODS

Catphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index.

RESULTS

The five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values.

CONCLUSIONS

The improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.