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Purpose: To investigate the chronological relationship between scan delay and liver enhancement for the hepatobiliary phase on Gd-EOB-DTPA-enhanced MRI and evaluate the effects of liver function on liver enhancement.Materials and Methods: Hepatobiliary-phase images were retrospectively evaluated in 125 patients with chronic liver disease. Hepatobiliary phase images were obtained at 5, 10, 15, and 20 min after injection. We calculated relative liver enhancement (RLE) at t min after injection by dividing the signal intensity (SI) of the liver at t min by precontrast SI. We compared RLE values at 5, 10, 15, and 20 min and evaluated the detectability of focal hepatic lesions. We analyzed the effect of liver function on RLE with the generalized linear model.Results: There was not significant difference in RLE and lesion detectability at 15 and 20 min. RLE in the Child-Pugh C group was significantly lower than in the Child-Pugh A and B groups. The serum albumin level and prothrombin time were significantly correlated with the liver enhancement.Conclusion: A delay time of 15 min for the hepatobiliary phase was thought to be adequate in patients with mild liver dysfunction. The serum albumin level and prothrombin time would be predictive of liver enhancement in the hepatobiliary phase. [ABSTRACT FROM AUTHOR]

Purpose: We evaluated low-contrast injection protocols for coronary computed tomography angiography (CTA) using a 64-detector scanner and the test bolus technique.Materials and Methods: We randomly assigned 60 patients undergoing coronary CTA to one of two contrast material (CM) injection protocols. For the low-contrast dose protocol (P(low)), the patients received injections of iohexol-350 [0.7 ml/kg body weight (BW)] during 9 s, and the test-bolus technique was used. Under the conventional protocol (P(conv)), they received iohexol-350 (1.0 ml/kg BW) during 15 s, and bolus tracking was used. We compared the protocols for attenuation values in the ascending aorta and coronary arteries and for the amount of CM required.Results: There was no significant difference in the mean CT attenuation of the ascending aorta and coronary arteries between the P(low) and P(conv) groups. The amount of CM was significantly less with P(low) than with P(conv) [49.7 ± 6.4 ml (main bolus: 39.7 ± 6.4 ml) vs. 57.0 ± 10.1 ml, P < 0.01].Conclusion: With 64-detector CTA of the heart, the low-dose and short-injection-duration protocol with the test-injection technique provides vessel attenuation comparable to that obtained with the standard-dose protocol with the bolus-tracking technique. [ABSTRACT FROM AUTHOR]

Objective: We investigated the effect of low-tube-voltage CT angiography with a reduced volume of contrast agent on qualitative and quantitative parameters and the radiation dose in patients with peripheral arterial disease.Methods: Eighty consecutive patients were divided into two groups; the protocol was 120 kVp and 1.8 ml/kg contrast agent (300 mgI/ml) in one group and 80 kVp and 1.2 ml/kg in the other. For quantitative analysis we calculated the mean arterial attenuation, the mean difference between maximum and minimum attenuation values, contrast-to-noise ratio (CNR), and figure of merit (FOM). For qualitative evaluation, we used visual scores.Results: There were no significant intergroup differences in mean arterial attenuation (120 vs. 80 kVp: 331.6 ± 61.6 vs. 354.9 ± 61.9 HU) and in the mean difference between maximum and minimum attenuation (120 vs. 80 kVp: 52.2 ± 25.5 vs. 61.5 ± 27.5 HU). While the mean CNR was significantly higher at 120 than 80 kVp (38.4 ± 18.8 vs. 31.1 ± 15.3), the mean FOM was not significantly different (120 vs. 80 kVp: 1.3 ± 1.5 vs. 1.2 ± 1.2), and there was no significant intergroup difference in visual scores. The mean dose-length product was significantly lower at 80 than 120 kVp (1,024.3 ± 151.3 vs. 1,464.7 ± 208.7 mGy·cm).Conclusion: The 80-kVp protocol allows for reduction of the radiation dose by approximately 30% and the volume of contrast agent by more than 30% without deterioration of vascular enhancement and image quality. [ABSTRACT FROM AUTHOR]

The objective of our study was to compare diffusion-weighted imaging (DWI) alone and DWI combined with T2-weighted MRI for the differentiation of uterine sarcomas from benign leiomyomas. T2-weighted imaging and DWI were performed in 103 patients with 103 myometrial tumours, including 8 uterine sarcomas and 95 benign leiomyomas on 3-T MR imaging. The signal intensity (SI) of the tumour on T2-weighted images was quantified as the tumour-myometrium contrast ratio (TCR) by using the following formula: (SI(tumour)-SI(myometrium))/SI(myometrium). The TCR or apparent diffusion coefficient (ADC) value alone and then the ADC value combined with T2-weighted imaging were evaluated for differentiation between sarcomas and leiomyomas. The mean ADC value of sarcomas was 0.86 +/- 0.11 x 10(-3) m(2)/s, which was significantly lower than that of leiomyomas 1.18 +/- 0.24 x 10(-3) m(2)/s; however, there was a substantial overlap. The mean TCR of sarcomas was 0.66 +/- 0.71, which was significantly higher than that of the leiomyomas, -0.37 +/- 0.34; however, again, there was a considerable overlap. When ADC was less than 1.05 x 10(-3) mm(2)/s and TCR was greater than 0 this condition was considered to confirm a sarcoma; a combination of ADC and TCR achieved a significant improvement without any overlap between sarcomas and leiomyomas (sensitivity 100%, specificity 100%). Our preliminary results indicate that combined DWI and T2-weighted MR imaging is better than DWI alone in the differentiation of uterine sarcomas from benign leiomyomas. [ABSTRACT FROM AUTHOR]

Recent technical advances in diffusion-weighted imaging (DWI) greatly enhanced the clinical value of magnetic resonance imaging (MRI) of the body. DWI can provide excellent tissue contrast based on molecular diffusion and may be able to demonstrate malignant tumors. Quantitative measurement of the apparent diffusion coefficient (ADC) may be valuable in distinguishing between malignant and benign lesions. We reviewed DWI and conventional MRI of the female pelvis to study the utility of DWI in patients with gynecological diseases. Although the ADC can help to differentiate between normal and cancerous tissue in the uterine cervix and endometrium, its utility may be limited by the large overlap of the uterine myometrium and ovaries. On the other hand, the ADC may be useful for monitoring the therapeutic outcome after uterine arterial embolizati (UAE), chemotherapy and/or radiation therapy. In patients with ovarian cancer, DWI demonstrates high intensity not only at the primary cancer site but also in disseminated peritoneal implants. When added to conventional MRI findings, DWI and ADC values provide additional information and DWI may play an important role in the diagnosis of patients with gynecological diseases. [ABSTRACT FROM AUTHOR]

Aim: To compare the performance of machine learning using multiparametric magnetic resonance imaging (mp-MRI) and positron-emission tomography (PET) to distinguish between uterine sarcoma and leiomyoma.Materials and Methods: This retrospective study was approved by the institutional review board and informed consent was waived. Sixty-seven consecutive patients with uterine sarcoma or leiomyoma who underwent pelvic 3 T MRI and PET were included. Of 67 patients, 11 had uterine sarcomas and 56 had leiomyomas. Seven different parameters were measured in the tumours, from T2-weighted, T1-weighted, contrast-enhanced, and diffusion-weighted MRI, and PET. The areas under the receiver operating characteristic curves (AUC) with a leave-one-out cross-validation were used to compare the diagnostic performances of the univariate and multivariate logistic regression (LR) model with those of two board-certified radiologists.Results: The AUCs of the univariate models using MRI parameters (0.68-0.8) were inferior to that of the maximum standardised uptake value (SUVmax) of PET (0.85); however, the AUC of the multivariate LR model (0.92) was superior to that of SUVmax, and comparable to that of the board-certified radiologists (0.97 and 0.89).Conclusion: The diagnostic performance of the machine learning using mp-MRI was superior to PET and comparable to that of experienced radiologists. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and Patient Care Follow-up System (APCFS) for high-risk ICTFs. This study aimed to analyze the ICTFs detected by this system and the factors associated with them., Materials and Methods: This retrospective study was approved by the institutional review board. We analyzed 52,331 CT examinations conducted between 2019 and 2021. In cases where high-risk ICTFs were identified, radiologists utilized APCFS to prompt ordering clinicians for further patient care. We assessed the frequency, affected body organs, presence or absence of therapeutic interventions, and diagnoses of high-risk ICTFs. An automated machine learning platform was employed to analyze the factors associated with high-risk ICTFs., Results: Among the 52,331 CT examinations, 507 (0.96%) revealed high-risk ICTFs, primarily affecting the lung (18.0%). Of these 507 high-risk ICTFs, 117 (23.1%) underwent therapeutic interventions, while 362 (71.4%) required only follow-up. Of the 117 cases undergoing interventions, 61 (52.1%) required surgery. Of the 219 high-risk ICTFs leading to a confirmed diagnosis, 146 (66.7%) were neoplastic lesions, including 88 (60.3%) malignancies, and 73 (33.3%) were non-neoplastic lesions. The top three risk factors associated with high-risk ICTFs in the regularized logistic regression model were the imaging protocol (especially aortic valve implantation planning protocol), imaging area (especially whole-body imaging), and clinical department (especially cardiology)., Conclusion: Utilizing APCFS, high-risk ICTFs were detected in approximately 1% of all CT examinations, likely associated with specific imaging protocols, areas, and clinical departments., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future., (© 2024. The Author(s).)

Rationale and Objectives: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image quality in computed tomography (CT) images across various field of view (FOV) sizes, radiation doses, and noise reduction strengths., Materials and Methods: A Catphan phantom equipped with an external body ring was used. CT images were reconstructed using filtered back-projection (FBP), HIR, NR-DLR, and SR-DLR across three noise reduction strengths: mild, standard, and strong. The noise power spectrum (NPS) was obtained from the FBP, HIR, NR-DLR, and SR-DLR images at various FOVs, radiation doses, and noise reduction strengths. The noise magnitude ratio (NMR) and central frequency ratio (CFR) were calculated from the HIR, NR-DLR, and SR-DLR images relative to the FBP images using NPS. The high-contrast value was obtained from the amplitude values of the peaks and valleys of profile curve and the task-based transfer function were also analyzed., Results: SR-DLR consistently demonstrated superior noise reduction capabilities, with NMR of 0.29-0.36 at reduced dose and 0.35-0.45 at standard dose, outperforming HIR and showing comparable efficiency to NR-DLR. The high-contrast values for SR-DLR were highest at mild and standard levels for both low and standard doses (0.610 and 0.726 at mild and 0.725 and 0.603 at standard levels). At the standard dose, the spatial resolution of SR-DLR was significantly improved, regardless of the noise reduction strength and FOV., Conclusion: SR-DLR images achieved more substantial noise reduction than HIR and similar noise reduction as NR-DLR reconstructions while also improving spatial resolution., Competing Interests: Declaration of Competing Interest Toshinori Hirai is a recipient of research grant from Canon Medical Systems Corporation. Yuya Ito, Yutaka Chiba are employees of Canon Medical Systems Corporation., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: This study assessed whether patient-specific contrast enhancement optimizer simulation software (p-COP) can reduce the contrast material (CM) dose compared with the conventional body weight (BW)-tailored scan protocol during transcatheter aortic valve implantation-computed tomography angiography (TAVI-CTA) in patients with aortic stenosis., Methods: We used the CM injection protocol selected by the p-COP in group A (n = 30). p-COP uses an algorithm that concerns data on an individual patient's cardiac output. Group B (n = 30) was assigned to the conventional BW-tailored CM injection protocol group. We compared the CM dose, CM amount, injection rate, and computed tomography (CT) values in the abdominal aorta between the 2 groups and classified them as acceptable (>280 Hounsfield units (HU)) or unacceptable (<279 HU) based on the optimal CT value and visualization scores for TAVI-CTA. We used the Mann-Whitney U test to compare patient characteristics and assess the interpatient variability of subjects in both groups., Results: Group A received 56.2 mL CM and 2.6 mL/s of injection, whereas group B received 76.9 mL CM and 3.4 mL/s of injection ( P < 0.01). The CT value for the abdominal aorta at the celiac level was 287.0 HU in group A and 301.7HU in group B ( P = 0.46). The acceptable (>280 HU) and unacceptable (<280 HU) CT value rates were 22 and 8 patients in group A and 24 and 6 patients in group B, respectively ( P = 0.76). We observed no significant differences in the visualization scores between groups A and B (visualization score = 3, P = 0.71)., Conclusion: The utilization of p-COP may decrease the CM dosage and injection rate by approximately 30% in individuals with aortic stenosis compared with the body-weight-tailored scan protocol during TAVI-CTA., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI., (© 2024. Italian Society of Medical Radiology.)

Objective: The aim of this study was to assess the utility of the combined use of 3D wheel sampling and deep learning-based reconstruction (DLR) for intracranial high-resolution (HR)-time-of-flight (TOF)-magnetic resonance angiography (MRA) at 3 T., Methods: This prospective study enrolled 20 patients who underwent head MRI at 3 T, including TOF-MRA. We used 3D wheel sampling called "fast 3D" and DLR for HR-TOF-MRA (spatial resolution, 0.39 × 0.59 × 0.5 mm 3 ) in addition to conventional MRA (spatial resolution, 0.39 × 0.89 × 1 mm 3 ). We compared contrast and contrast-to-noise ratio between the blood vessels (basilar artery and anterior cerebral artery) and brain parenchyma, full width at half maximum in the P3 segment of the posterior cerebral artery among 3 protocols. Two board-certified radiologists evaluated noise, contrast, sharpness, artifact, and overall image quality of 3 protocols., Results: The contrast and contrast-to-noise ratio of fast 3D-HR-MRA with DLR are comparable or higher than those of conventional MRA and fast 3D-HR-MRA without DLR. The full width at half maximum was significantly lower in fast 3D-MRA with and without DLR than in conventional MRA ( P = 0.006, P < 0.001). In qualitative evaluation, fast 3D-MRA with DLR had significantly higher sharpness and overall image quality than conventional MRA and fast 3D-MRA without DLR (sharpness: P = 0.021, P = 0.001; overall image quality: P = 0.029, P < 0.001)., Conclusions: The combination of 3D wheel sampling and DLR can improve visualization of arteries in intracranial TOF-MRA., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF 50% , d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d' were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

Objectives: This study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-phase sequence., Materials and Methods: In this retrospective study, 29 patients who underwent lumbar spine MRI, including an MR bone imaging sequence between January and April 2023, were analyzed. Images were reconstructed with and without SR-DLR (Matrix sizes: 960 × 960 and 320 × 320, respectively). The signal-to-noise ratio (SNR) of the vertebral body and spinal canal and the contrast and contrast-to-noise ratio (CNR) between the vertebral body and spinal canal were quantitatively evaluated. Furthermore, the slope at half-peak points of the profile curve drawn across the posterior border of the vertebral body was calculated. Two radiologists independently assessed image noise, contrast, artifacts, sharpness, and overall image quality of both image types using a 4-point scale. Interobserver agreement was evaluated using weighted kappa coefficients, and quantitative and qualitative scores were compared via the Wilcoxon signed-rank test., Results: SNRs of the vertebral body and spinal canal were notably improved in images with SR-DLR (p < 0.001). Contrast and CNR were significantly enhanced with SR-DLR compared to those without SR-DLR (p = 0.023 and p = 0.022, respectively). The slope of the profile curve at half-peak points across the posterior border of the vertebral body and spinal canal was markedly higher with SR-DLR (p < 0.001). Qualitative scores (noise: p < 0.001, contrast: p < 0.001, artifact p = 0.042, sharpness: p < 0.001, overall image quality: p < 0.001) were superior in images with SR-DLR compared to those without. Kappa analysis indicated moderate to good agreement (noise: κ = 0.56, contrast: κ = 0.51, artifact: κ = 0.46, sharpness: κ = 0.76, overall image quality: κ = 0.44)., Conclusion: SR-DLR, which is based on k-space data, has the potential to enhance the image quality of lumbar spine MR bone imaging utilizing a 3D gradient echo in-phase sequence., Clinical Relevance Statement: The application of SR-DLR can lead to improvements in lumbar spine MR bone imaging quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent TEVAR for the presence or absence of an endoleak. We evaluated the effect of machine learning of the patient age, sex, weight, and height, plus 22 vascular features on the ability to predict post-TEVAR endoleaks. The extreme Gradient Boosting (XGBoost) for ML system was trained on 14 patients with- and 131 without endoleaks. We calculated their importance by applying XGBoost to machine learning and compared our findings between with those of conventional vessel measurement-based methods such as the 22 vascular features by using the Pearson correlation coefficients. Pearson correlation coefficient and 95% confidence interval (CI) were r = 0.86 and 0.75 to 0.92 for the machine learning, r = - 0.44 and - 0.56 to - 0.29 for the vascular angle, and r = - 0.19 and - 0.34 to - 0.02 for the diameter between the subclavian artery and the aneurysm (Fig. 3a-c, all: p < 0.05). With machine-learning, the univariate analysis was significant higher compared with the vascular angle and in the diameter between the subclavian artery and the aneurysm such as the conventional methods (p < 0.05). To predict the risk for post-TEVAR endoleaks, machine learning was superior to the conventional vessel measurement method when factors such as patient characteristics, and vascular features (vessel length, diameter, and angle) were evaluated on pre-TEVAR thoracic CTA images., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development., (© 2024. The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.)