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BACKGROUND: Myocardial fibrosis is a common feature in various cardiac diseases. It causes adverse cardiac remodeling and is associated with poor clinical outcomes. Late gadolinium enhancement (LGE) and extracellular volume fraction (ECV) are the standard MRI techniques for detecting focal and diffuse myocardial fibrosis. However, these contrast-enhanced techniques require the administration of gadolinium contrast agents, which is not applicable to patients with gadolinium contraindications. To eliminate the need of contrast agents, we develop and apply an endogenous free-breathing T1ρ dispersion imaging technique (FB-MultiMap) for diagnosing diffuse myocardial fibrosis in a cohort with suspected cardiomyopathies. METHODS: The proposed FB-MultiMap technique, enabling T2, T1ρ and their difference (myocardial fibrosis index, mFI) quantification in a single scan was developed in phantoms and 15 healthy subjects. In the clinical study, 55 patients with suspected cardiomyopathies were imaged using FB-MultiMap, conventional native T1 mapping, LGE, and ECV imaging. The accuracy of the endogenous parameters for predicting increased ECV was evaluated using receiver operating characteristic (ROC) curve analysis. In addition, the correlation of native T1, T1ρ, and mFI with ECV was respectively assessed using Pearson correlation coefficients. RESULTS: FB-MultiMap showed a good agreement with conventional separate breath-hold mapping techniques in phantoms and healthy subjects. Considering all the patients, T1ρ was more accurate than mFI and native T1 for predicting increased ECV, with area under the curve (AUC) values of 0.91, 0.79 and 0.75, respectively, and showed stronger correlation with ECV (correlation coefficient r: 0.72 vs. 0.52 vs. 0.40). In the subset of 47 patients with normal T2 values, the diagnostic performance of mFI was significantly strengthened (AUC=0.90, r=0.83), outperforming T1ρ and native T1. CONCLUSION: The proposed free-breathing T1ρ dispersion imaging technique enabling simultaneous quantification of T2, T1ρ and mFI in a single scan has shown great potential for diagnosing diffuse myocardial fibrosis in patients with complex cardiomyopathies without contrast agents.

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Background: Colorectal cancer (CRC) is commonly assessed by computed tomography (CT), but the associated radiation exposure is a major concern. This study aimed to quantitatively and qualitatively compare the image quality of virtual non-contrast (VNC) images reconstructed from arterial and portal venous phases with that of true non-contrast (TNC) images in patients with CRC to assess the potential of TNC images to replace VNC images, thereby reducing the radiation dose. Methods: A total of 69 patients with postoperative pathologically confirmed CRC at the West China Hospital of Sichuan University between May 2022 and April 2023 were enrolled in this cross-sectional study. The CT protocol included the acquisition of TNC images, arterial and portal venous phase images; the VNC images were reconstructed from the two postcontrast phase images. Several parameters, including the CT attenuation value, absolute attenuation error, imaging noise [standard deviation (SD)], signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were measured in multiple abdominal structures for both the TNC and VNC images. Two blinded readers assessed the subjective image quality using a five-point scale. Interobserver agreement was evaluated using interclass correlation coefficients (ICCs). The paired t-test and Wilcoxon signed-rank test were used to compare the objective and subjective results between the TNC and VNC images. Individual measurements of radiation doses for the TNC scan and contrast scan protocols were recorded. Results: A total of 2,070 regions of interest (ROIs) of the 69 patients were analyzed. Overall, the VNC images exhibited significantly lower attenuation values and SD values than the TNC images in all tissues, except for the abdominal aorta, portal vein, and spleen. The mean absolute attenuation errors between the VNC and TNC images were all less than 10 Hounsfield units (HU). The percentages of absolute attenuation errors less than 5 and 10 HU in the VNC images from the arterial phase (VNCa) were 78.99% and 97.97%, respectively, while those from the portal venous phase (VNCp) were 81.59% and 96.96%, respectively. The absolute attenuation errors between the TNC and VNCa images were smaller than those between the TNC and VNCp images for tumors [VNCaerror: 2.77, interquartile range (IQR) 1.77-4.22; VNCperror: 3.27, IQR 2.68-4.30; P=0.002]. The SNR values and CNR values in the VNC images were significantly higher than those in the TNC images for all tissues, except for the portal vein and spleen. The image quality was rated as excellent (represented by a score of 5) in the majority of the TNC and VNC images; however, the VNC images scored lower than the TNC images. Eliminating the TNC phase resulted in a reduction of approximately 37.99% in the effective dose (ED). Conclusions: The VNC images provided accurate CT attenuation, good image quality, and lower radiation doses than the TNC images in CRC, and the VNCa images showed minimal differences in the CT attenuation of the tumors.

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Purpose: Machine learning (ML) models were constructed according to non-contrast computed tomography (NCCT) images as well as clinical and laboratory information to assess risk stratification for the occurrence of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. Methods: A retrospective cohort was constructed with 180 AIS patients who were diagnosed at two centers between January 2019 and October 2023 and were followed for HT outcomes. Patients were analyzed for clinical risk factors for developing HT, infarct texture features were extracted from NCCT images, and the radiomics score (Rad-score) was calculated. Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. Receiver operating characteristic (ROC) curves were used to compare the performance of the three models in predicting HT. Results: Based on the outcomes of the AIS patients, 104 developed HT, and the remaining 76 had no HT. The HT group consisted of 27 hemorrhagic infarction (HI) and 77 parenchymal-hemorrhage (PH). Patients with HT had a greater neutrophil-to-lymphocyte ratio (NLR), baseline National Institutes of Health Stroke Scale (NIHSS) score, infarct volume, and Rad-score and lower Alberta stroke program early CT score (ASPECTS) (all p < 0.01) than patients without HT. The best ML algorithm for building the model was logistic regression. In the training and validation cohorts, the AUC values for the clinical, radiomics, and clinical-radiomics models for predicting HT were 0.829 and 0.876, 0.813 and 0.898, and 0.876 and 0.957, respectively. In subgroup analyses with different treatment modalities, different infarct sizes, and different stroke time windows, the assessment accuracy of the clinical-radiomics model was not statistically meaningful (all p > 0.05), with an overall accuracy of 79.5%. Moreover, this model performed reliably in predicting the PH and HI subcategories, with accuracies of 82.9 and 92.9%, respectively. Conclusion: ML models based on clinical and NCCT radiomics characteristics can be used for early risk evaluation of HT development in AIS patients and show great potential for clinical precision in treatment and prognostic assessment.

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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.

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BACKGROUND: Spectral imaging of photon-counting detector CT (PCD-CT) scanners allows for generating virtual non-contrast (VNC) reconstruction. By analyzing 12 abdominal organs, we aimed to test the reliability of VNC reconstructions in preserving HU values compared to real unenhanced CT images. METHODS: Our study included 34 patients with pancreatic cystic neoplasm (PCN). The VNC reconstructions were generated from unenhanced, arterial, portal, and venous phase PCD-CT scans using the Liver-VNC algorithm. The observed 11 abdominal organs were segmented by the TotalSegmentator algorithm, the PCNs were segmented manually. Average densities were extracted from unenhanced scans (HUunenhanced), postcontrast (HUpostcontrast) scans, and VNC reconstructions (HUVNC). The error was calculated as HUerror=HUVNC-HUunenhanced. Pearson's or Spearman's correlation was used to assess the association. Reproducibility was evaluated by intraclass correlation coefficients (ICC). RESULTS: Significant differences between HUunenhanced and HUVNC[unenhanced] were found in vertebrae, paraspinal muscles, liver, and spleen. HUVNC[unenhanced] showed a strong correlation with HUunenhanced in all organs except spleen (r = 0.45) and kidneys (r = 0.78 and 0.73). In all postcontrast phases, the HUVNC had strong correlations with HUunenhanced in all organs except the spleen and kidneys. The HUerror had significant correlations with HUunenhanced in the muscles and vertebrae; and with HUpostcontrast in the spleen, vertebrae, and paraspinal muscles in all postcontrast phases. All organs had at least one postcontrast VNC reconstruction that showed good-to-excellent agreement with HUunenhanced during ICC analysis except the vertebrae (ICC: 0.17), paraspinal muscles (ICC: 0.64-0.79), spleen (ICC: 0.21-0.47), and kidneys (ICC: 0.10-0.31). CONCLUSIONS: VNC reconstructions are reliable in at least one postcontrast phase for most organs, but further improvement is needed before VNC can be utilized to examine the spleen, kidneys, and vertebrae.

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PURPOSE: Fresh blood imaging (FBI) utilizes physiological blood signal differences between diastole and systole, causing a long acquisition time. The purpose of this study is to develop a fast FBI technique using a centric ky - kz k-space trajectory (cFBI) and an exponential refocusing flip angle (eFA) scheme with fast longitudinal restoration. METHODS: This study was performed on 8 healthy subjects and 2 patients (peripheral artery disease and vascular disease) with informed consent, using a clinical 3-Tesla MRI scanner. A numeric simulation using extended phase graph (EPG) and phantom studies of eFA were carried out to investigate the restoration of longitudinal signal by lowering refocusing flip angles in later echoes. cFBI was then acquired on healthy subjects at the popliteal artery station to assess the effect of varying high/low flip ratios on the longitudinal restoration effects. In addition, trigger-delays of cFBI were optimized owing to the long acquisition window in zigzag centric ky - kz k-space trajectory. After optimizations, cFBI images were compared against standard FBI (sFBI) images in terms of scan time, motion artifacts, Nyquist N/2 artifacts, blurring, and overall image quality. We also performed two-way repeated measures analysis of variance. RESULTS: cFBI with eFA achieved nearly a 50% scan time reduction compared to sFBI. The high/low flip angle of 180/2 degrees with lower refocusing pulses shows fast longitudinal restoration with the highest blood signals, yet also more sensitive to the background signals. Overall, 180/30 degrees images show reasonable blood signal recovery while minimizing the background signal artifacts. After the trigger delay optimization, maximum intensity projection image of cFBI after systole-diastole subtraction demonstrates less motion and N/2 artifacts than that of sFBI. CONCLUSION: Together with eFA for fast longitudinal signal restoration, the proposed cFBI technique achieved a 2-fold reduction in scan time and improved image quality without major artifacts.

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BACKGROUND AND AIMS: With ultrasound sensitivity limited in hepatocellular carcinoma (HCC) surveillance and few prospective studies on non-contrast abbreviated MRI (NC-AMRI), this study aimed to assess its diagnostic performance in detecting HCC. METHODS: This prospective study involved cirrhotic patients with contrast-enhanced MRI (CE-MRI) Liver Imaging Reporting and Data System (LI-RADS) LR-3 and LR-4 observations detected during HCC surveillance. Patients underwent average 3 complete CE-MRI rounds at 3-6 months interval, with approximately 12-month follow-up. NC-AMRI included diffusion-weighted (DWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). NC-AMRI protocol images were analysed for diagnostic performance, with subgroup analyses. CE-MRI and NC-AMRI images were independently reviewed by 2 experienced radiologists, with inter-reader agreement assessed with Kappa coefficient. The reference standard was the American Association for the Study of Liver Diseases-defined presence of arterial hypervascularity and washout during the portal-venous or delayed phases on CE-MRI. RESULTS: In 166 CE-MRI follow-ups of 63 patients (median age: 63 years; 60.3% male, 39.7% female), 12 patients developed HCC, with average size of 19.6 mm. The NC-AMRI (DWI + T2WI + T1WI) showed 91.7% sensitivity (95%CI, 61.5-99.8) and 91.6% specificity (95%CI, 86.0-95.4), area under receiver operating characteristic 0.92 (95%CI, 0.83-1.00). Across different Body Mass Index categories, lesion size, Child-Turcotte-Pugh classes, Albumin-Bilirubin (ALBI) grades, and Model for End-Stage Liver Disease classes, sensitivity remained consistent. However, specificity differed significantly between ALBI grade 1 and 2 (86.7% vs. 98.4%, P = .010), and between viral and non-viral cirrhosis (93.8% vs. 80.8%, P = .010). CONCLUSIONS: NC-AMRI proved clinically feasible, and exhibits high diagnostic performance in HCC detection. ADVANCES IN KNOWLEDGE: This study highlights efficacy of NC-AMRI in detecting HCC among cirrhotic patients with LR-3 and LR-4 observations, representing significant progress in HCC surveillance.

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Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) method that enables the assessment and the quantification of perfusion without the need for an exogenous contrast agent. ASL was originally developed in the early 1990s to measure cerebral blood flow. The utility of ASL has since then broadened to encompass various organ systems, offering insights into physiological and pathological states. In this review article, we present a synopsis of ASL for quantitative non-contrast perfusion MRI, as a contribution to the special issue titled "Quantitative MRI-how to make it work in the body?" The article begins with an introduction to ASL principles, followed by different labeling strategies, such as pulsed, continuous, pseudo-continuous, and velocity-selective approaches, and their role in perfusion quantification. We proceed to address the technical challenges associated with ASL in the body and outline some of the innovative approaches devised to surmount these issues. Subsequently, we summarize potential clinical applications, challenges, and state-of-the-art ASL methods to quantify perfusion in some of the highly perfused organs in the thorax (lungs), abdomen (kidneys, liver, pancreas), and pelvis (placenta) of the human body. The article concludes by discussing future directions for successful translation of quantitative ASL in body imaging.

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BACKGROUND: Cardiovascular magnetic resonance (CMR) has demonstrated excellent performance in the diagnosis of cardiac amyloidosis (CA). However, misdiagnosis occasionally occurs because the morphological and functional features of CA are non-specific. This study was performed to determine the value of non-contrast CMR T1ρ in the diagnosis of CA. METHODS: This prospective study included 45 patients with CA, 30 patients with hypertrophic cardiomyopathy (HCM), and 10 healthy controls (HCs). All participants underwent cine (whole heart), T1ρ mapping, pre- and post-contrast T1 mapping imaging (three slices), and late gadolinium enhancement using a 3T whole-body magnetic resonance imaging system. All participants underwent T1ρ at two spin-locking frequencies: 0 and 298 Hz. Extracellular volume (ECV) maps were obtained using pre- and post-contrast T1 maps. The myocardial T1ρ dispersion map, termed myocardial dispersion index (MDI), was also calculated. All parameters were measured in the left ventricular myocardial wall. Participants in the HC group were scanned twice on different days to assess the reproducibility of T1ρ measurements. RESULTS: Excellent reproducibility was observed upon evaluation of the coefficient of variation between two scans (T1ρ [298 Hz]: 3.1%; T1ρ [0 Hz], 2.5%). The ECV (HC: 27.4 ± 2.8% vs HCM: 32.6 ± 5.8% vs CA: 46 ± 8.9%; p < 0.0001), T1ρ [0 Hz] (HC: 35.8 ± 1.7 ms vs HCM: 40.0 ± 4.5 ms vs CA: 51.4 ± 4.4 ms; p < 0.0001) and T1ρ [298 Hz] (HC: 41.9 ± 1.6 ms vs HCM: 48.8 ± 6.2 ms vs CA: 54.4 ± 5.2 ms; p < 0.0001) progressively increased from the HC group to the HCM group, and then the CA group. The MDI progressively decreased from the HCM group to the HC group, and then the CA group (HCM: 8.8 ± 2.8 ms vs HC: 6.1 ± 0.9 ms vs CA: 3.4 ± 2.1 ms; p < 0.0001). For differential diagnosis, the combination of MDI and T1ρ [298 Hz] showed the greatest sensitivity (98.3%) and specificity (95.5%) between CA and HCM, compared with the native T1 and ECV. CONCLUSION: The T1ρ and MDI approaches can be used as non-contrast CMR imaging biomarkers to improve the differential diagnosis of patients with CA.

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This study aims to assess the diagnostic accuracy of non-contrast-enhanced 4D MR angiography (NCE-4D-MRA) compared to contrast-enhanced 4D MR angiography (CE-4D-MRA) for the detection and angioarchitectural characterisation of brain arteriovenous malformations (bAVMs). Utilising a retrospective design, we examined 54 MRA pairs from 43 patients with bAVMs, using digital subtraction angiography (DSA) as the reference standard. Both NCE-4D-MRA and CE-4D-MRA were performed using a 3-T MR imaging system. The primary objectives were to evaluate the diagnostic performance of NCE-4D-MRA against CE-4D-MRA and DSA and to assess concordance between imaging modalities in grading bAVMs according to four main scales: Spetzler-Martin, Buffalo, AVM embocure score (AVMES), and R2eDAVM. Our results demonstrated that NCE-4D-MRA had a higher accuracy and specificity compared to CE-4D-MRA (0.85 vs. 0.83 and 95% vs. 85%, respectively) and similar agreement, with DSA detecting shunts in bAVMs or residuals. Concordance in grading bAVMs was substantial between NCE-4D-MRA and DSA, particularly for the Spetzler-Martin and Buffalo scales, with CE-4D-MRA showing slightly higher kappa values for interobserver agreement. The study highlights the potential of NCE-4D-MRA as a diagnostic tool for bAVMs, offering comparable accuracy to CE-4D-MRA while avoiding the risks associated with gadolinium-based contrast agents. The safety profile of imaging techniques is a significant concern in the long-term follow up of bAVMs, and further prospective research should focus on NCE-4D-MRA protocol improvement for clinical use.

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PURPOSE: To develop a novel framework to improve the visualization of distal arteries in arterial spin labeling (ASL) dynamic MRA. METHODS: The attenuation of ASL blood signal due to the repetitive application of excitation RF pulses was minimized by splitting the acquisition volume into multiple thin 2D (M2D) slices, thereby reducing the exposure of the arterial blood magnetization to RF pulses while it flows within the brain. To improve the degraded vessel visualization in the slice direction due to the limited minimum achievable 2D slice thickness, a super-resolution (SR) convolutional neural network (CNN) was trained by using 3D time-of-flight (TOF)-MRA images from a large public dataset. And then, we applied domain transfer from 3D TOF-MRA to M2D ASL-MRA, while avoiding acquiring a large number of ASL-MRA data required for CNN training. RESULTS: Compared to the conventional 3D ASL-MRA, far more distal arteries were visualized with higher signal intensity by using M2D ASL-MRA. In general, however, the vessel visualization with a conventional interpolation was prone to be blurry and unclear due to the limited spatial resolution in the slice direction, particularly in small vessels. Application of CNN-based SR transferred from 3D TOF-MRA to M2D ASL-MRA successfully addressed such a limitation and achieved clearer visualization of small vessels than conventional interpolation. CONCLUSION: This study demonstrated that the proposed framework provides improved visualization of distal arteries in later dynamic phases, which will particularly benefit the application of this approach in patients with cerebrovascular disease who have slow blood flow.

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Objectives: Three-tesla MRI with gadolinium-based contrast agents is important in diagnosing Ménière's disease. However, contrast agents cannot be used in some patients. By using the compositional difference between the inner ear endolymph and perilymph, we performed basic and clinical research focused on potassium ions and protein to find the optimal parameters for visualizing endolymphatic hydrops on MRI without contrast. We then examined the relationship between severity stage and visualization rate of endolymphatic hydrops. Methods: In phantom experiments simulating the endolymph and perilymph, we explored MRI parameters that could be used to separate endolymph from perilymph by gradually changing the inversion time. We then used these parameters to perform both new non-contrast MRI and contrast MRI on the same day in Ménière's disease patients, and we compared the visualization rates of endolymphatic hydrops under the two modalities. Fifty patients were selected from 478 patients with Ménière's disease of different severity stages; 12 patients had asthma and allergy to contrast agents. Results: The higher the disease stage, the higher the endolymphatic hydrops visualization rate. The new non-contrast MRI gave significantly higher (p < .01) visualization rates of endolymphatic hydrops on the affected side in patients at Stage 3 or above than in Stages 1 and 2 combined. Conclusion: New non-contrast MRI with parameters focusing on the endolymph-perilymph difference in the density of protons surrounding the potassium ions and protein can produce images consistent with endolymphatic hydrops. We believe that this groundbreaking method will be useful for diagnosing Ménière's disease in patients. Evidence Level: Clinical studies are at evidence level 3 in non-randomized controlled trials.

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Objective: This retrospective study aims to assess the role of pre-contrast sequences of an MRI-guided breast biopsy (MRIB) exam in confident and accurate lesion site localization based on tissue landmarks. Methods: The charts of all consecutives MRIB that were performed between January 2018 and December 2020 were reviewed. The images of the eligible exams were analyzed by 3 breast radiologists. Each radiologist independently attempted to identify lesion site on pre-contrast MRIB sequences, while blinded to the post-contrast MRIB images. Confidence levels (I-confident, II-not confident, and III-unknown) were assigned by each reviewer. A fourth radiologist assessed the accuracy (≤5 mm-accurate, >5 mm-inaccurate) in lesion site localization using the actual biopsied lesion site and the post-contrast MRIB images as reference. Descriptive statistics were used to calculate the percentage of confidence and accuracy categories for each reviewer, with Chi-square tests applied to analyze relationships between categorical variables. Results: There were 174 female patients with 181 lesions eligible for the trial. When the lesion site is confidently identified on the pre-contrast MRIB images (level 1 confidence), mean grade 1 accuracy was 93.8% (P < .001). Accuracy decreased with Level II and III confidence (55.3% and 34.2% respectively). Up to 61.4% improved accuracy was demonstrated when combining the performance of 2 reviewers. No correlation was found between breast density, lesion morphology, or biopsy positioning with confidence level or accuracy grade. Conclusion: Careful review of the pre-contrast MRIB images and familiarization with the surrounding tissue landmarks are important steps in confidently and accurately detecting lesion site.

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BACKGROUND: Photon-counting computed tomography (PCCT) enables new ways of image reconstruction, e.g. material decomposition and creation of virtual non-contrast (VNC) series with higher resolution and lower radiation dose than standard computed tomography (CT). Clinical experiences of this are limited. PURPOSE: To compare true non-contrast (TNC) series with VNC series derived from non-enhanced (VNCu), arterial phase (VNCa) and portal venous phase (VNCv) in clinically approved PCCT. MATERIAL AND METHODS: A total of 45 clinical, tri-phasic abdominal CT scans from the PCCT Naetom Alpha, between February 2022 and November 2022, were retrospectively assessed. Placing a region of interest in six different locations in each VNC series - right liver parenchyma, left liver parenchyma, spleen, aorta, erector spinae muscle, and in the subcutaneous fat - absolute Hounsfield values (HU) and standard deviations (SD) were collected. Differences in HU (ΔHU) were compared and statistically analyzed. RESULTS: Statistically significant differences between VNC and TNC were seen in all measurements, with the largest difference in the subcutaneous fat and the smallest difference in the erector spinae muscle. Only small differences were seen between VNCa and VNCv, where the largest differences were seen in the left and right liver lobes. CONCLUSION: VNC images from the first-generation clinically approved PCCT showed a significant difference between VNC and TNC images. The differences vary with the type of tissue. Only small differences were seen depending from which contrast phase the VNC was derived.

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This review article focuses on the advancements in non-contrast magnetic resonance angiography (NC-MRA) and its increasing importance in body imaging, especially for patients with renal complications, pregnant women, and children. It highlights the relevance of NC-MRA in chest, abdominal, and pelvis imaging and details various bright-blood NC-MRA techniques like cardiac-gated 3D Fast Spin Echo (FSE), balanced Steady-State Free Precession (bSSFP), Arterial Spin Labeling (ASL), and 4D flow methods. The article explains the operational principles of these techniques, their clinical applications, and their advantages over traditional contrast-enhanced methods. Special attention is given to the utility of these techniques in diverse imaging scenarios, including liver, renal, and pelvic imaging. The article underscores the growing importance of NC-MRA in medical diagnostics, offering insights into current practices and potential future developments. This comprehensive review is a valuable resource for radiologists and clinicians, emphasizing NC-MRA's role in enhancing patient care and diagnostic accuracy across various medical conditions.

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INTRODUCTION: This study aimed to evaluate the potential of dual-energy computed tomography (CT) to distinguish postoperative ascites, pancreatic fistula, and abscesses. MATERIALS AND METHOD: Patients who underwent biliary and pancreatic surgery performed at our institution between June 2021 and February 2022 were included in the study. Postoperative body fluid samples were collected through a drain or percutaneous drainage. These samples were set in a phantom, and imaging data were obtained using dual-energy CT. Image analysis was performed to obtain CT values at each energy in virtual monoenergetic images (VMIs), effective atomic number, iodine map, and virtual non-contrast (VNC) images. VMIs were calculated from 80 and 140 kVp tube data at 10 kV each from 40-140 kV. Additionally, the effective atomic number, iodine map, and VNC images were reconstructed from the material decomposition process using water and iodine as the base material pair. RESULTS: In this study, 25 patients (eight with abscess and 17 with ascites) were included. No significant association was observed between the presence or absence of abscess and malignancy or surgical procedure. The intervention was performed in six of the eight patients with abscesses. In contrast, five of the 17 patients with postoperative ascites required intervention. A significant relationship was observed between the intervention and the presence of an abscess. Significant differences in C-reactive protein values and the incidence of fever were observed between the groups. Only VNC showed a significant difference between the groups. CONCLUSIONS: VNC using dual-energy CT could differentiate abscesses from postoperative fluid.

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Intrinsic cerebrospinal fluid (CSF) dynamics in the brain have been extensively studied, particularly the egress sites of tagged intrinsic CSF in the meninges. Although spinal CSF recirculates within the central nervous system (CNS), we hypothesized that CSF outflows from the lumbar spinal canal. We aimed to visualize and semi-quantify the outflow using non-contrast MRI techniques. We utilized a 3 Tesla clinical MRI with a 16-channel spine coil, employing time-spatial labeling inversion (Time-SLIP) with tag-on and tag-off acquisitions, T2-weighted coronal 2D fluid-attenuated inversion recovery (FLAIR) and T2-weighted coronal 3D centric ky-kz single-shot FSE (cSSFSE). Images were acquired using time-spatial labeling inversion pulse (Time-SLIP) with tag-on and tag-off acquisitions with varying TI periods. Ten healthy volunteers with no known spinal diseases participated. Variations in tagged CSF outflow were observed across different thoracolumbar nerve root segments in all participants. We quantified CSF outflow at all lumbar levels and the psoas region. There was no significant difference among the ROIs for signal intensity. The tagged CSF outflow from the spinal canal is small but demonstrates egress to surrounding tissues. This finding may pave the way for exploring intrathecal drug delivery, understanding of CSF-related pathologies and its potential as a biomarker for peripheral neuropathy and radiculopathy.

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Velocity-selective (VS) magnetization preparation has shown great promise for non-contrast-enhanced (NCE) magnetic resonance angiography (MRA) with the ability to generate positive angiographic contrast directly using a single 3D acquisition. However, existing VS-MRA methods have an issue of aliased saturation around a certain velocity, known as velocity field-of-view (vFOV), which can cause undesired signal loss in arteries. This study aimed to develop a new version of the VS preparation pulse sequence that overcomes the aliased saturation problem in conventional VS preparation. Utilizing the fact that an excitation profile is the Fourier transform of excitation k-space sampling, we sampled the k-space in a non-uniform fashion by scaling gradient pulses accordingly to have aliased excitation diffused over velocity. The variable density sampling function was numerically optimized to maximize the average of the velocity passband signal while minimizing its variance. The optimized variable density VS magnetization was validated through Bloch simulations and applied to peripheral NCE MRA in healthy subjects. The in-vivo experiments showed that the proposed variable density VS-MRA significantly lowered arterial signal loss observed in conventional VS-MRA, as evidenced by a higher arterial signal-to-noise ratio (58.50 ± 14.29 vs. 55.54 ± 12.32; p < 0.05) and improved artery-to-background contrast-to-noise ratio (22.75 ± 7.57 vs. 20.60 ± 6.51; p < 0.05).

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Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial differences between single-energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

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OBJECTIVES: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL). METHODS: This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians' readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts. RESULTS: The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system's diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency. CONCLUSION: The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention. CLINICAL RELEVANCE STATEMENT: The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications. KEY POINTS: The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS. This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan. 94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.