To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI. A neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling. A saturation-contrast-specific loss function was added to preserve amide proton transfer (APT) contrast, as well as enforce image alignment between motion-corrected and ground-truth images. The proposed neural network was evaluated on simulation data and demonstrated in healthy volunteers and brain tumor patients. The experimental results showed the effectiveness of motion artifact correction in the Z-spectrum frequency domain (MOCO_Ω) compared to in the image spatial domain. In addition, a temporal convolution applied to a dynamic saturation image series was able to leverage motion artifacts to improve reconstruction results as a denoising process. The MOCO_Ω outperformed existing techniques for motion correction in terms of image quality and computational efficiency. At 3 T, human experiments showed that the root mean squared error (RMSE) of APT images decreased from 4.7% to 2.1% at 1 μT and from 6.2% to 3.5% at 1.5 μT in case of "moderate" motion and from 8.7% to 2.8% at 1 μT and from 12.7% to 4.5% at 1.5 μT in case of "severe" motion, after motion artifact correction. The MOCO_Ω could effectively correct motion artifacts in CEST MRI without compromising saturation transfer contrast.

Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is crucial for early proactive management and limit growing disease burden. The more recent advanced quantitative imaging techniques and deep learning (DL) algorithms in musculoskeletal imaging have shown great potential for visualizing "pre-OA." In this review, we first focus on ultrashort echo time-based magnetic resonance imaging (MRI) techniques for direct visualization as well as quantitative morphological and compositional assessment of both short- and long-T2 musculoskeletal tissues, and second explore how DL revolutionize the way of MRI analysis (eg, automatic tissue segmentation and extraction of quantitative image biomarkers) and the classification, prediction, and management of OA. PLAIN LANGUAGE SUMMARY: Detecting osteoarthritis (OA) before the onset of irreversible changes is crucial for early proactive management. OA is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Ultrashort echo time-based magnetic resonance imaging (MRI), in particular, enables direct visualization and quantitative compositional assessment of short-T2 tissues. Deep learning is revolutionizing the way of MRI analysis (eg, automatic tissue segmentation and extraction of quantitative image biomarkers) and the detection, classification, and prediction of disease. They together have made further advances toward identification of imaging biomarkers/features for pre-OA. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Pancreatic ductal adenocarcinoma (PDAC) is the deadliest malignant tumor, with a grim 5-year overall survival rate of about 12%. As its incidence and mortality rates rise, it is likely to become the second-leading cause of cancer-related death. The radiological assessment determined the stage and management of PDAC. However, it is a highly heterogeneous disease with the complexity of the tumor microenvironment, and it is challenging to adequately reflect the biological aggressiveness and prognosis accurately through morphological evaluation alone. With the dramatic development of artificial intelligence (AI), multiparametric magnetic resonance imaging (mpMRI) using specific contrast media and special techniques can provide morphological and functional information with high image quality and become a powerful tool in quantifying intratumor characteristics. Besides, AI has been widespread in the field of medical imaging analysis. Radiomics is the high-throughput mining of quantitative image features from medical imaging that enables data to be extracted and applied for better decision support. Deep learning is a subset of artificial neural network algorithms that can automatically learn feature representations from data. AI-enabled imaging biomarkers of mpMRI have enormous promise to bridge the gap between medical imaging and personalized medicine and demonstrate huge advantages in predicting biological characteristics and the prognosis of PDAC. However, current AI-based models of PDAC operate mainly in the realm of a single modality with a relatively small sample size, and the technical reproducibility and biological interpretation present a barrage of new potential challenges. In the future, the integration of multi-omics data, such as radiomics and genomics, alongside the establishment of standardized analytical frameworks will provide opportunities to increase the robustness and interpretability of AI-enabled image biomarkers and bring these biomarkers closer to clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers on low-dose chest CT. We included participants of the population-based Imaging in Lifelines (ImaLife) study with low-dose chest CT at baseline and 3-4 month follow-up. An AI system (AI-Rad Companion chest CT prototype) performed automatic segmentation and quantification of vertebral height and density, aortic diameters, heart volume (cardiac chambers plus pericardial fat), and coronary artery calcium volume (CACV). A trained researcher visually checked segmentation accuracy. We evaluated the repeatability of adequate AI-based measurements at baseline and repeat scan using Intraclass Correlation Coefficient (ICC), relative differences, and change in CACV risk categorization, assuming no physiological change. Overall, 632 participants (63 ± 11 years; 56.6% men) underwent short-term repeat CT (mean interval, 3.9 ± 1.8 months). Visual assessment showed adequate segmentation in both baseline and repeat scan for 98.7% of vertebral measurements, 80.1-99.4% of aortic measurements (except for the sinotubular junction (65.2%)), and 86.0% of CACV. For heart volume, 53.5% of segmentations were adequate at baseline and repeat scans. ICC for adequately segmented cases showed excellent agreement for all biomarkers (ICC > 0.9). Relative difference between baseline and repeat measurements was < 4% for vertebral and aortic measurements, 7.5% for heart volume, and 28.5% for CACV. There was high concordance in CACV risk categorization (81.2%). In low-dose chest CT, segmentation accuracy of AI-based software was high for vertebral, aortic, and CACV evaluation and relatively low for heart volume. There was excellent repeatability of vertebral and aortic measurements and high concordance in overall CACV risk categorization. Question Can AI algorithms for opportunistic screening in chest CT obtain an accurate and repeatable result when applied to multiple CT scans of the same participant? Findings Vertebral and aortic analysis showed accurate segmentation and excellent repeatability; coronary calcium segmentation was generally accurate but showed modest repeatability due to a non-electrocardiogram-triggered protocol. Clinical relevance Opportunistic screening for diseases outside the primary purpose of the CT scan is time-consuming. AI allows automated vertebral, aortic, and coronary artery calcium (CAC) assessment, with highly repeatable outcomes of vertebral and aortic biomarkers and high concordance in overall CAC categorization.

Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study aims to enhance preoperative discrimination of absence or presence of MI by developing and validating a multimodal deep learning radiomics (MDLR) model based on MRI. During March 2010 and February 2023, 1139 EC patients (age 54.771 ± 8.465 years; range 24-89 years) from five independent centers were enrolled retrospectively. We utilized ResNet18 to extract multi-scale deep learning features from T2-weighted imaging followed by feature selection via Mann-Whitney U test. Subsequently, a Deep Learning Signature (DLS) was formulated using Integrated Sparse Bayesian Extreme Learning Machine. Furthermore, we developed Clinical Model (CM) based on clinical characteristics and MDLR model by integrating clinical characteristics with DLS. The area under the curve (AUC) was used for evaluating diagnostic performance of the models. Decision curve analysis (DCA) and integrated discrimination index (IDI) were used to assess the clinical benefit and compare the predictive performance of models. The MDLR model comprised of age, histopathologic grade, subjective MR findings (TMD and Reading for MI status) and DLS demonstrated the best predictive performance. The AUC values for MDLR in training set, internal validation set, external validation set 1, and external validation set 2 were 0.899 (95% CI, 0.866-0.926), 0.874 (95% CI, 0.829-0.912), 0.862 (95% CI, 0.817-0.899) and 0.867 (95% CI, 0.806-0.914) respectively. The IDI and DCA showed higher diagnostic performance and clinical net benefits for the MDLR than for CM or DLS, which revealed MDLR may enhance decision-making support. The MDLR which incorporated clinical characteristics and DLS could improve preoperative accuracy in discriminating absence or presence of MI. This improvement may facilitate individualized treatment decision-making for EC.

To develop a deep subspace learning network that can function across different pulse sequences. A contrast-invariant component-by-component (CBC) network structure was developed and compared against previously reported spatiotemporal multicomponent (MC) structure for reconstructing MR Multitasking images. A total of 130, 167, and 16 subjects were imaged using T₁, T₁-T₂, and T₁-T₂- <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> -fat fraction (FF) mapping sequences, respectively. We compared CBC and MC networks in matched-sequence experiments (same sequence for training and testing), then examined their cross-sequence performance and generalizability by unmatched-sequence experiments (different sequences for training and testing). A "universal" CBC network was also evaluated using mixed-sequence training (combining data from all three sequences). Evaluation metrics included image normalized root mean squared error and Bland-Altman analyses of end-diastolic maps, both versus iteratively reconstructed references. The proposed CBC showed significantly better normalized root mean squared error than MC in both matched-sequence and unmatched-sequence experiments (p < 0.001), fewer structural details in quantitative error maps, and tighter limits of agreement. CBC was more generalizable than MC (smaller performance loss; p = 0.006 in T₁ and p < 0.001 in T₁-T₂ from matched-sequence testing to unmatched-sequence testing) and additionally allowed training of a single universal network to reconstruct images from any of the three pulse sequences. The mixed-sequence CBC network performed similarly to matched-sequence CBC in T₁ (p = 0.178) and T₁-T₂ (p = 0121), where training data were plentiful, and performed better in T₁-T₂- <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> -FF (p < 0.001) where training data were scarce. Contrast-invariant learning of spatial features rather than spatiotemporal features improves performance and generalizability, addresses data scarcity, and offers a pathway to universal supervised deep subspace learning.

Growth discordance in twin pregnancies is associated with increased perinatal morbidity and mortality, yet the patterns of discordance progression and the utility of Doppler assessments remain underinvestigated. The objective of this study was to conduct a longitudinal assessment of intertwin growth and Doppler discordance to identify possible distinct patterns and to investigate the predictive value of longitudinal discordance patterns for adverse perinatal outcomes in twin pregnancies. This retrospective cohort study included twin pregnancies followed and delivered at a tertiary hospital in London (United Kingdom) between 2010 and 2023. We included pregnancies with at least 3 ultrasound assessments after 18 weeks and delivery beyond 34 weeks' gestation. Monoamniotic twin pregnancies, pregnancies with twin-to-twin transfusion syndrome, genetic or structural abnormalities, or incomplete data were excluded. Data on chorionicity, biometry, Doppler indices, maternal characteristics and obstetrics, and neonatal outcomes were extracted from electronic records. Doppler assessment included velocimetry of the umbilical artery, middle cerebral artery, and cerebroplacental ratio. Intertwin growth discordance was calculated for each scan. The primary outcome was a composite of perinatal mortality and neonatal morbidity. Statistical analysis involved multilevel mixed effects regression models and unsupervised machine learning algorithms, specifically k-means clustering, to identify distinct patterns of intertwin discordance and their predictive value. Predictive models were compared using the area under the receiver operating characteristic curve, calibration intercept, and slope, validated with repeated cross-validation. Analyses were performed using R, with significance set at P<.05. Data from 823 twin pregnancies (647 dichorionic, 176 monochorionic) were analyzed. Five distinct patterns of intertwin growth discordance were identified using an unsupervised learning algorithm that clustered twin pairs based on the progression and patterns of discordance over gestation: low-stable (n=204, 24.8%), mild-decreasing (n=171, 20.8%), low-increasing (n=173, 21.0%), mild-increasing (n=189, 23.0%), and high-stable (n=86, 10.4%). In the high-stable cluster, the rates of perinatal morbidity (46.5%, 40/86) and mortality (9.3%, 8/86) were significantly higher compared to the low-stable (reference) cluster (P<.001). High-stable growth pattern was also associated with a significantly higher risk of composite adverse perinatal outcomes (odds ratio: 70.19, 95% confidence interval: 24.18-299.03, P<.001; adjusted odds ratio: 76.44, 95% confidence interval: 25.39-333.02, P<.001). The model integrating discordance pattern with cerebroplacental ratio discordance at the last ultrasound before delivery demonstrated superior predictive accuracy, evidenced by the highest area under the receiver operating characteristic curve of 0.802 (95% confidence interval: 0.712-0.892, P<.001), compared to only discordance patterns (area under the receiver operating characteristic curve: 0.785, 95% confidence interval: 0.697-0.873), intertwin weight discordance at the last ultrasound prior to delivery (area under the receiver operating characteristic curve: 0.677, 95% confidence interval: 0.545-0.809), combination of single measurements of estimated fetal weight and cardiopulmonary resuscitation discordance at the last ultrasound prior to delivery (area under the receiver operating characteristic curve: 0.702, 95% confidence interval: 0.586-0.818), and single measurement of cardiopulmonary resuscitation discordance only at the last ultrasound (area under the receiver operating characteristic curve: 0.633, 95% confidence interval: 0.515-0.751). Using an unsupervised machine learning algorithm, we identified 5 distinct trajectories of intertwin fetal growth discordance. Consistent high discordance is associated with increased rates of adverse perinatal outcomes, with a dose-response relationship. Moreover, a predictive model integrating discordance trajectory and cardiopulmonary resuscitation discordance at the last visit demonstrated superior predictive accuracy for the prediction of composite adverse perinatal outcomes, compared to either of these measurements alone or a single value of estimated fetal weight discordance at the last ultrasound prior to delivery.

Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis. To develop and validate deep learning (DL) models for predicting MRE-derived liver stiffness using routine clinical non-contrast abdominal T1-weighted (T1w) and T2-weighted (T2w) data from multiple institutions/system manufacturers in pediatric and adult patients. We identified pediatric and adult patients with known or suspected CLD from four institutions, who underwent clinical MRI with MRE from 2011 to 2022. We used T1w and T2w data to train DL models for liver stiffness classification. Patients were categorized into two groups for binary classification using liver stiffness thresholds (≥ 2.5 kPa, ≥ 3.0 kPa, ≥ 3.5 kPa, ≥ 4 kPa, or ≥ 5 kPa), reflecting various degrees of liver stiffening. We identified 4695 MRI examinations from 4295 patients (mean ± SD age, 47.6 ± 18.7 years; 428 (10.0%) pediatric; 2159 males [50.2%]). With a primary liver stiffness threshold of 3.0 kPa, our model correctly classified patients into no/minimal (< 3.0 kPa) vs moderate/severe (≥ 3.0 kPa) liver stiffness with AUROCs of 0.83 (95% CI: 0.82, 0.84) in our internal multi-site cross-validation (CV) experiment, 0.82 (95% CI: 0.80, 0.84) in our temporal hold-out validation experiment, and 0.79 (95% CI: 0.75, 0.81) in our external leave-one-site-out CV experiment. The developed model is publicly available ( https://github.com/almahdir1/Multi-channel-DeepLiverNet2.0.git ). Our DL models exhibited reasonable diagnostic performance for categorical classification of liver stiffness on a large diverse dataset using T1w and T2w MRI data. Question Can DL models accurately predict liver stiffness using routine clinical biparametric MRI in pediatric and adult patients with CLD? Findings DeepLiverNet2.0 used biparametric MRI data to classify liver stiffness, achieving AUROCs of 0.83, 0.82, and 0.79 for multi-site CV, hold-out validation, and external CV. Clinical relevance Our DeepLiverNet2.0 AI model can categorically classify the severity of liver stiffening using anatomic biparametric MR images in children and young adults. Model refinements and incorporation of clinical features may decrease the need for MRE.

This study compared the characteristics of lesions suspicious for hepatocellular carcinoma (HCC) and their LI-RADS classifications in adaptive statistical iterative reconstruction (ASIR) and deep learning reconstruction (DLR) to those of MR images, along with radiologist confidence. This prospective single-center trial included patients who underwent four-phase liver CT and multiphasic contrast-enhanced MRI within 7 days from February to August 2023. The lesion characteristics, LI-RADS classifications and confidence scores according to two radiologists on the ASIR, DLR and MRI techniques were compared. If the patient had at least one lesion, he was included in the HCC group, otherwise in the non-HCC group. MRI being the technique with the best sensitivity, concordance of lesions characteristics and LI-RADS classifications were calculated by weighted kappa between the ASIR and MRI and between the DLR and MRI. The confidence scores are expressed as the means and standard deviations. Eighty-nine patients were enrolled, 52 in the HCC group (67 years ± 9 [mean ± SD], 46 men) and 37 in the non-HCC group (68 years ± 9, 33 men). The concordance coefficient between the LI-RADS classification by ASIR and MRI was 0.64 [0.52; 0.76], showing good agreement, that by DLR and MRI was 0.83 [0.73; 0.92], showing excellent agreement. The diagnostic confidence in ASIR was 3.31 ± 0.95 (mean ± SD) and 3.0 ± 1.11, that in the DLR was 3.9 ± 0.88 and 4.11 ± 0.75, that in the MRI was 4.46 ± 0.80 and 4.57 ± 0.80. DLR provided excellent LI-RADS classification concordance with MRI, whereas ASIR provided good concordance. The radiologists' confidence was greater in the DLR than in the ASIR but remained highest in the MR group. Question Does the use of deep learning reconstructions (DLR) improve LI-RADS classification of suspicious hepatocellular carcinoma lesions compared to adaptive statistical iterative reconstructions (ASIR)? Findings DLR demonstrated superior concordance of LI-RADS classification with MRI compared to ASIR. It also provided greater diagnostic confidence than ASIR. Clinical relevance The use of DLR enhances radiologists' ability to visualize and characterize lesions suspected of being HCC, as well as their LI-RADS classification. Moreover, it also boosts their confidence in interpreting these images.

Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-network (LCP-CNN), a deep learning-based approach, in comparison to multiparametric statistical methods (Brock model and Lung-RADS®) for risk classification of nodules in cohorts with different risk profiles and underlying pulmonary diseases. Retrospective analysis was conducted on non-contrast and contrast-enhanced CT scans containing pulmonary nodules measuring 5-30 mm. Ground truth was defined by histology or follow-up stability. The final analysis was performed on 297 patients with 422 eligible nodules, of which 105 nodules were malignant. Classification performance of the LCP-CNN, Brock model, and Lung-RADS® was evaluated in terms of diagnostic accuracy measurements including ROC-analysis for different subcohorts (total, screening, emphysema, and interstitial lung disease). LCP-CNN demonstrated superior performance compared to the Brock model in total and screening cohorts (AUC 0.92 (95% CI: 0.89-0.94) and 0.93 (95% CI: 0.89-0.96)). Superior sensitivity of LCP-CNN was demonstrated compared to the Brock model and Lung-RADS® in total, screening, and emphysema cohorts for a risk threshold of 5%. Superior sensitivity of LCP-CNN was also shown across all disease groups compared to the Brock model at a threshold of 65%, compared to Lung-RADS® sensitivity was better or equal. No significant differences in the performance of LCP-CNN were found between subcohorts. This study offers further evidence of the potential to integrate deep learning-based decision support systems into pulmonary nodule classification workflows, irrespective of the individual patient risk profile and underlying pulmonary disease. Question Is a deep-learning approach (LCP-CNN) superior to multiparametric models (Brock model, Lung-RADS®) in classifying pulmonary nodule risk across varied patient profiles? Findings LCP-CNN shows superior performance in risk classification of pulmonary nodules compared to multiparametric models with no significant impact on risk profiles and structural pulmonary diseases. Clinical relevance LCP-CNN offers efficiency and accuracy, addressing limitations of traditional models, such as variations in manual measurements or lack of patient data, while producing robust results. Such approaches may therefore impact clinical work by complementing or even replacing current approaches.