Deep learning-based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis

Virtual monochromatic image-based automatic segmentation strategy using deep learning method

BACKGROUND AND PURPOSE

The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thoroughly. Based on DECT-generated virtual monochromatic images (VMIs), this study proposed a novel deep learning model (MIAU-Net) and evaluated the segmentation performance on the head organs-at-risk (OARs).

METHODS AND MATERIALS

The VMIs from 40 keV to 190 keV were retrospectively generated at intervals of 10 keV using the DECT of 46 patients. Images with expert delineation were used for training, validation, and testing MIAU-Net for automatic segmentation. Theperformance of MIAU-Net was compared with the existingU-Net, Attention-UNet, nnU-Net and TransFuse methods based on Dice Similarity Coefficient (DSC). Correlationanalysis was performed to evaluate and optimize the impact of different virtual energies on the accuracy of segmentation.

RESULTS

Using MIAU-Net, average DSCs across all virtual energy levels were 93.78 %, 81.75 %, 84.46 %, 92.85 %, 94.40 %, and 84.75 % for the brain stem, optic chiasm, lens, mandible, eyes, and optic nerves, respectively, higher than the previous publications using SECT. MIAU-Net achieved the highest average DSC (88.84 %) and the lowest parameters (14.54 M) in all tested models. The results suggested that 60 keV-80 keV is the optimal VMI energy level for soft tissue delineation, while 100 keV is optimal for skeleton segmentation.

CONCLUSIONS

This work proposed and validated a novel deep learning model for automatic segmentation based on DECT, suggesting potential advantages and OAR-specific optimal energy of using VMIs for automatic delineation.

Single-Energy, Dual-Energy, and Photon-Counting Computed Tomography of the Liver: Current Development and Clinical Utility for the Assessment of Focal Liver Lesions

ABSTRACT

Advancements in computed tomography (CT) technology, particularly the emergence of dual-energy CT (DE-CT) and photon-counting detector CT (PCD-CT), can improve detection, characterization, and treatment monitoring of focal liver lesions. DE-CT, through its ability to differentiate tissues with similar densities and produce diverse datasets, has enhanced lesion visibility and diagnostic precision. PCD-CT further advances imaging with superior spatial resolution and material decomposition capabilities, offering potential for complex diagnostic scenarios. This review aimed to highlight the role of CT in hepatic imaging and its application to focal liver lesions.DE-CT improves lesion detectability using low-energy virtual monochromatic images, which enhance iodine contrast and reduce radiation and contrast agent doses. It also facilitates treatment response evaluation after locoregional therapies for hepatocellular carcinoma by quantifying biomarkers, such as the extracellular volume fraction. This review underscores the transformative impact of DE-CT and PCD-CT on liver imaging, emphasizing their complementary roles alongside magnetic resonance imaging. These innovations have paved the way for more precise diagnostics, improved treatment planning, and enhanced patient outcomes in the management of liver diseases.

Evaluation of colorectal liver metastases using virtual monoenergetic images obtained from dual-layer spectral computed tomography

PURPOSE

To assess the potential of virtual monoenergetic images in assessing colorectal liver metastasis (CRLM) compared with conventional CT images.

METHODS

This single-center, retrospective study included 173 consecutive patients (mean age, 65.5 ± 10.6 years; 106 men) who underwent dual-layer spectral CT (DLSCT) between November 2016 and April 2021. Portal venous phase images were reconstructed using hybrid iterative reconstruction (iDose) and virtual monoenergetic imaging at 50 keV. Four radiologists independently and randomly reviewed the de-identified iDose and 50 keV images. Lesion detection, CRLM conspicuity, and CRLM diagnosis were compared between these images using a generalized estimating equation analysis. The reference standards used were histopathology and follow-up imaging findings.

RESULTS

The study included 797 focal liver lesions, including 463 CRLMs (median size, 18.1 mm [interquartile range, 10.9-37.7 mm]). Lesion detection was better with 50 keV images than with iDose images (45.0% [95% confidence interval [CI]: 39-50] vs 40.0% [95% CI: 34-46], P = 0.003). CRLM conspicuity was higher in the 50 keV images than in the iDose images (3.27 [95% CI: 3.09-3.46] vs 3.09 [95% CI: 2.90-3.28], P < 0.001). However, the specificity for diagnosing CRLM was lower with 50 keV images than with iDose images (94.5% [95% CI: 91.6-96.4] vs 96.0% [95% CI: 93.2-98.1], P = 0.022), whereas sensitivity did not differ significantly (77.6% [95% CI: 70.3-83.5] vs 76.9% [95% CI: 70.0-82.7], P = 0.736). Indeterminate lesions were more frequently noted in 50 keV images than in iDose images (13% [445/3188] vs 9% [313/3188], P = 0.005), and 56% (247/445) of the indeterminate lesions at 50 keV were not CRLMs.

CONCLUSION

The 50 keV images obtained from DLSCT were better than the iDose images in terms of CRLM conspicuity and lesion detection. However, 50 keV images did not improve CRLM diagnosis but slightly increased the reporting of indeterminate focal liver lesions associated with CRLMs.

Radiomics based on dual-energy CT virtual monoenergetic images to identify symptomatic carotid plaques: a multicenter study

This study aims to create a radiomics nomogram using dual-energy computed tomography (DECT) virtual monoenergetic images (VMI) to accurately identify symptomatic carotid plaques. Between January 2018 and May 2023, data from 416 patients were collected from two centers for retrospective analysis. Center 1 provided data for the training (n = 213) and internal validation (n = 93) sets, and center 2 supplied the external validation set (n = 110). Plaques imaged at 40 keV, 70 keV, and 100 keV were outlined, and the selected radiomics features were used to establish the radiomics model. The classifier with the highest area under the curve (AUC) in the training set generated the radiomics score (Rad-Score). Logistic regression was used to identify risk factors and establish a clinical model. A radiomics nomogram integrating the Rad-score and clinical risk factors was constructed. The predictive performance was evaluated using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Plaque ulceration and plaque burden are independent risk factors for symptomatic carotid plaques. The 40 + 70 keV radiomics model achieved excellent diagnostic performance, with an average AUC of 0.805 across all validation sets. Furthermore, the radiomics nomogram, integrating the Rad-score with clinical predictors, demonstrated robust diagnostic accuracy, with AUCs of 0.909, 0.850, and 0.804 in the training, internal validation, and external validation sets, respectively. DCA results suggested that the nomogram was clinically valuable. Our study developed and validated a DECT VMI-based radiomics nomogram for early identification of symptomatic carotid plaques, which can be used to assist clinical diagnosis and treatment decisions. The study introduces an innovative radiomics nomogram utilizing DECT VMI to discern symptomatic carotid plaques with high precision.

Contrast Volume Reduction in Oncologic Body Imaging Using Dual-Energy CT: A Comparison with Single-Energy CT

Background/Objectives: To evaluate the feasibility of reducing contrast volume in oncologic body imaging using dual-energy CT (DECT) by (1) identifying the optimal virtual monochromatic imaging (VMI) reconstruction using DECT and (2) comparing DECT performed with reduced iodinated contrast media (ICM) volume to single-energy CT (SECT) performed with standard ICM volume. Methods: In this retrospective study, we quantitatively and qualitatively compared the image quality of 35 thoracoabdominopelvic DECT across 9 different virtual monoenergetic image (VMI) levels (from 40 to 80 keV) using a reduced volume of ICM (0.3 gI/kg of body weight) to determine the optimal keV reconstruction level. Out of these 35 patients, 20 had previously performed SECT with standard ICM volume (0.3 gI/kg of body weight + 9 gI), enabling protocol comparison. The qualitative analysis included overall image quality, noise, and contrast enhancement by two radiologists. Quantitative analysis included contrast enhancement measurements, contrast-to-noise ratio, and signal-to-noise ratio of the liver parenchyma and the portal vein. ANOVA was used to identify the optimal VMI level reconstruction, while t-tests and paired t-tests were used to compare both protocols. Results: VMI60 keV provided the highest overall image quality score. DECT with reduced ICM volume demonstrated higher contrast enhancement and lower noise than SECT with standard ICM volume (p < 0.001). No statistical difference was found in the overall image quality between the two protocols (p = 0.290). Conclusions: VMI60 keV with reduced contrast volume provides higher contrast and lower noise than SECT at a standard contrast volume. DECT using a reduced ICM volume is the technique of choice for oncologic body CT.

Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan

OBJECTIVE

The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based iterative reconstruction (MBIR) from a single CT using dual-split scan in patients with suspected liver metastasis via a noninferiority design.

MATERIALS AND METHODS

This prospective study enrolled participants who met the eligibility criteria at 2 tertiary hospitals in South Korea from June 2022 to January 2023. The criteria included (a) being aged between 20 and 85 years and (b) having suspected or known liver metastases. Dual-source CT scans were conducted, with the standard radiation dose divided in a 2:1 ratio between tubes A and B (67% and 33%, respectively). The voltage settings of 100/120 kVp were selected based on the participant's body mass index (<30 vs ≥30 kg/m2). For image reconstruction, MBIR was utilized for standard-dose (100%) images, whereas DLR was employed for both low-dose (67%) and ultra-low-dose (33%) images. Three radiologists independently evaluated FLL conspicuity, the probability of metastasis, and subjective image quality using a 5-point Likert scale, in addition to quantitative signal-to-noise and contrast-to-noise ratios. The noninferiority margins were set at -0.5 for conspicuity and -0.1 for detection.

RESULTS

One hundred thirty-three participants (male = 58, mean body mass index = 23.0 ± 3.4 kg/m2) were included in the analysis. The low- and ultra-low- dose had a lower radiation dose than the standard-dose (median CT dose index volume: 3.75, 1.87 vs 5.62 mGy, respectively, in the arterial phase; 3.89, 1.95 vs 5.84 in the portal venous phase, P < 0.001 for all). Median FLL conspicuity was lower in the low- and ultra-low-dose scans compared with the standard-dose (3.0 [interquartile range, IQR: 2.0, 4.0], 3.0 [IQR: 1.0, 4.0] vs 3.0 [IQR: 2.0, 4.0] in the arterial phase; 4.0 [IQR: 1.0, 5.0], 3.0 [IQR: 1.0, 4.0] vs 4.0 [IQR: 2.0, 5.0] in the portal venous phases), yet within the noninferiority margin (P < 0.001 for all). FLL detection was also lower but remained within the margin (lesion detection rate: 0.772 [95% confidence interval, CI: 0.727, 0.812], 0.754 [0.708, 0.795], respectively) compared with the standard-dose (0.810 [95% CI: 0.770, 0.844]). Sensitivity for liver metastasis differed between the standard- (80.6% [95% CI: 76.0, 84.5]), low-, and ultra-low-doses (75.7% [95% CI: 70.2, 80.5], 73.7 [95% CI: 68.3, 78.5], respectively, P < 0.001 for both), whereas specificity was similar (P > 0.05).

CONCLUSIONS

Low- and ultra-low-dose CT with DLR showed noninferior FLL conspicuity and detection compared with standard-dose CT with MBIR. Caution is needed due to a potential decrease in sensitivity for metastasis (clinicaltrials.gov/ NCT05324046).

The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones

Purpose

To compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU).

Methods

A retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility.

Results

DH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05).

Conclusion

The DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.

Impact of deep Learning-enhanced contrast on diagnostic accuracy in stroke CT angiography

PURPOSE

To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.

METHODS

This retrospective single-centre study included 102 consecutive patients who underwent CT imaging for suspected stroke between 01/2021 and 12/2022, including whole brain volume perfusion CT (VPCT) and, specifically, a poorly contrasted CT angiography (defined as < 350HU in the proximal MCA). CT angiography imaging data was reconstructed using i.) an iterative reconstruction kernel (conventional CTA, c-CTA) as well as ii.) an iodine-based contrast boosting deep learning model (Deep Learning-enhanced CTA, DLe-CTA). For quantitative analysis, the slope, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were determined. Qualitative image analysis was conducted by three readers, rating image quality and vessel-specific parameters on a 4-point Likert scale. Readers evaluated both datasets for cerebral vessel occlusion presence. VPCT served as the reference standard for calculating sensitivity and specificity.

RESULTS

102 patients were evaluated (mean age 69 ± 13 years; 70 men). DLe-CTA outperformed c-CTA in quantitative (all items p < 0.001) and qualitative image analysis (all items p < 0.05). VPCT revealed 58/102 patients with vascular occlusion. DLe-CTA resulted in significantly higher sensitivity compared to c-CTA (p < 0.001); (all readers put together: c-CTA: 142/174 [81.6 %; 95 % CI: 75.0 %-87.1 %] vs. DLe-CTA 163/174 [94 %; 95 % CI: 89.0 %-96.8 %]). One false positive finding occurred on DLe-CTA (specificity 1/132) [99.2 %; 95 % CI: 95.9 %-100 %].

CONCLUSIONS

Deep learning-augmented contrast enhancement improves the image quality and increases the sensitivity of detection vessel occlusions in poorly contrasted CTA.

Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

BACKGROUND

Prostate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.

METHODS

We conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People's Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40-100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.

RESULTS

Virtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).

CONCLUSIONS

Virtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

Pancreatic cancer detection with dual-energy CT: diagnostic performance of 40 keV and 70 keV virtual monoenergetic images

PURPOSE

To compare the diagnostic performance of 40 keV and 70 keV virtual monoenergetic images (VMIs) generated from dual-energy CT in the detection of pancreatic cancer.

METHODS

This retrospective study included patients who underwent pancreatic protocol dual-energy CT from January 2019 to August 2022. Four radiologists (1-11 years of experience), who were blinded to the final diagnosis, independently and randomly interpreted 40 keV and 70 keV VMIs and graded the presence or absence of pancreatic cancer. For each image set (40 keV and 70 keV VMIs), the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. The diagnostic performance of each image set was compared using generalized estimating equations.

RESULTS

Overall, 137 patients (median age, 71 years; interquartile range, 63-78 years; 77 men) were included. Among them, 62 patients (45%) had pathologically proven pancreatic cancer. The 40 keV VMIs had higher specificity (75% vs. 67%; P < .001), PPV (76% vs. 71%; P < .001), and accuracy (85% vs. 81%; P = .001) than the 70 keV VMIs. On the contrary, 40 keV VMIs had lower sensitivity (96% vs. 98%; P = .02) and NPV (96% vs. 98%; P = .004) than 70 keV VMIs. However, the diagnostic confidence in patients with (P < .001) and without (P = .001) pancreatic cancer was improved in 40 keV VMIs than in 70 keV VMIs.

CONCLUSIONS

The 40 keV VMIs showed better diagnostic performance in diagnosing pancreatic cancer than the 70 keV VMIs, along with higher reader confidence.

Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT

Purpose To compare the image quality and diagnostic capability in detecting malignant liver tumors of low-dose CT (LDCT, 33% dose) with deep learning-based denoising (DLD) and standard-dose CT (SDCT, 100% dose) with model-based iterative reconstruction (MBIR). Materials and Methods In this prospective, multicenter, noninferiority study, individuals referred for liver CT scans were enrolled from three tertiary referral hospitals between February 2021 and August 2022. All liver CT scans were conducted using a dual-source scanner with the dose split into tubes A (67% dose) and B (33% dose). Blended images from tubes A and B were created using MBIR to produce SDCT images, whereas LDCT images used data from tube B and were reconstructed with DLD. The noise in liver images was measured and compared between imaging techniques. The diagnostic performance of each technique in detecting malignant liver tumors was evaluated by three independent radiologists using jackknife alternative free-response receiver operating characteristic analysis. Noninferiority of LDCT compared with SDCT was declared when the lower limit of the 95% CI for the difference in figure of merit (FOM) was greater than -0.10. Results A total of 296 participants (196 men, 100 women; mean age, 60.5 years ± 13.3 [SD]) were included. The mean noise level in the liver was significantly lower for LDCT (10.1) compared with SDCT (10.7) (P < .001). Diagnostic performance was assessed in 246 participants (108 malignant tumors in 90 participants). The reader-averaged FOM was 0.880 for SDCT and 0.875 for LDCT (P = .35). The difference fell within the noninferiority margin (difference, -0.005 [95% CI: -0.024, 0.012]). Conclusion Compared with SDCT with MBIR, LDCT using 33% of the standard radiation dose had reduced image noise and comparable diagnostic performance in detecting malignant liver tumors. Keywords: CT, Abdomen/GI, Liver, Comparative Studies, Diagnosis, Reconstruction Algorithms Clinical trial registration no. NCT05804799 © RSNA, 2024 Supplemental material is available for this article.

Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions

To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity - 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy

OBJECTIVES

To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR).

METHODS

In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images.

RESULTS

Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates.

CONCLUSION

DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT.

CLINICAL RELEVANCE STATEMENT

Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose.

KEY POINTS

• The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases

OBJECTIVE

To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM).

METHODS

Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images.

RESULTS

All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P＜0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P＞0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01).

CONCLUSION

Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis

PURPOSE

To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases.

METHODS

This retrospective study included 128 patients who underwent contrast-enhanced dual-energy CT for hepatic metastasis surveillance between July 2019 and June 2022 using a 30% reduced iodine contrast dose in the portal phase. Three image types were reconstructed: 50-keV virtual monoenergetic images (50-keV VMI); linearly blended images simulating 120-kVp images (120-kVp); and post-processed 120-kVp images using DLICA (DLICA 120-kVp). Three reviewers evaluated lesion conspicuity, image contrast, and subjective image noise. We also measured image noise, contrast-to-noise ratios (CNRs), and signal-to-noise ratios (SNRs). The diagnostic performance for hepatic metastases was evaluated using a jackknife alternative free-response receiver operating characteristic method with the consensus of two independent radiologists as the reference standard.

RESULTS

DLICA 120-kVp demonstrated significantly higher CNR of lesions to liver (5.7 ± 3.1 vs. 3.8 ± 2.1 vs. 3.8 ± 2.1) and higher SNR compared with 50-keV VMI and 120-kVp (p < 0.001 for all). DLICA 120-kVp had significantly lower image noise than 50-kVp VMI for all regions (p < 0.001 for all). DLICA 120-kVp also exhibited superior lesion conspicuity (4.0 [3.3-4.3] vs. 3.7 [3.0-4.0] vs. 3.7 [3.0-4.0]), higher image contrast, and lower subjective image noise compared with 50-keV VMI and 120-kVp (p < 0.001 for all). Although there was no significant difference in the figure of merit for lesion diagnosis among the three methods (p = 0.11), DLICA 120-kVp had a significantly higher figure of merit for lesions with a diameter < 20 mm than 50-keV VMI (0.677 vs. 0.648, p = 0.007). On a per-lesion basis, DLICA 120-kVp also demonstrated higher sensitivity than the 50-keV VMI (81.2% vs. 72.9%, p < 0.001). The specificities per lesion were not significantly different among the three algorithms (p = 0.15).

CONCLUSION

DLICA at 120-kVp provided superior lesion conspicuity and image quality and similar diagnostic performance for hypovascular hepatic metastases compared with 50-keV VMI.

Renal protection CT protocol using low-dose and low-concentration iodine contrast medium in at-risk patients of HCC and with chronic kidney disease: a randomized controlled non-inferiority trial

BACKGROUND

Although efforts have been made to reduce the dose of Contrast Medium (CM) to improve patient safety, there are ongoing concerns regarding its potential effects on image quality and diagnostic performance. Moreover, research is lacking to establish a lower limit for safe and effective CM dose reduction. To determine whether the image quality of contrast-enhanced liver computed tomography (CT) using a reduced amount of iodinated CM was similar to that of standard liver CT.

METHODS

We enrolled participants at risk for hepatocellular carcinoma with decreased estimated glomerular filtration rates (< 60 mL/min/1.73m2). Participants were randomly assigned to the standard group or the renal protection protocol (RPP) group. In the standard group, images were reconstructed using hybrid iterative reconstruction (iDose), while in the RPP group, low monoenergetic (50-keV) images and deep learning (DL)-based iodine-boosting reconstruction were used. Four radiologists independently assessed image quality and lesion conspicuity.

RESULTS

Fifty-two participants were assigned to the standard (n = 25) or RPP (n = 27) groups. The iodine load was significantly lower in the RPP group than in the standard group (301.5 ± 1.71 vs. 524 ± 7.37 mgI/kg, P < 0.001). The 50-keV and DL-based iodine-boosting images from the RPP group exhibited higher image contrast than those from the standard group during arterial (3.60 ± 0.65, 3.75 ± 0.60, and 3.09 ± 0.43, respectively) and portal venous phases (4.01 ± 0.49, 3.86 ± 0.42, and 3.21 ± 0.31, respectively) (P < 0.05 for all). Overall image quality was superior in the RPP group (P < 0.05 for all). No significant difference in lesion conspicuity was observed (P > 0.017).

CONCLUSIONS

The reduction in image contrast and overall image quality caused by decreased CM can be restored using either low monoenergetic imaging or DL-based iodine-boosting reconstruction.

TRIAL REGISTRATION

clinicaltrials.gov, NCT04024514, Registered July 18, 2019, prospectively registered, https://classic.

CLINICALTRIALS

gov/ct2/show/NCT04024514 .

Clinical applications of artificial intelligence in liver imaging

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Spectral CT: Current Liver Applications

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.