Most current medical image segmentation models employ a unified feature modeling strategy for all target regions. However, they overlook the significant heterogeneity in lesion structure, boundary characteristics, and semantic texture, which frequently restricts their ability to accurately segment morphologically diverse lesions in complex imaging contexts, thereby reducing segmentation accuracy and robustness. To address this issue, we propose a brain-inspired segmentation framework named BrainNet, which adopts a tri-level backbone encoder-Brain Network-decoder architecture. Such an architecture enables globally guided, locally differentiated feature modeling. We further instantiate the framework with an attention-enhanced segmentation model, termed Att-BrainNet. In this model, a Thalamus Gating Module (TGM) dynamically selects and activates structurally identical but functionally diverse Encephalic Region Networks (ERNs) to collaboratively extract lesion-specific features. In addition, an S-F image enhancement module is incorporated to improve sensitivity to boundaries and fine structures. Meanwhile, multi-head self-attention is embedded in the encoder to strengthen global semantic modeling and regional coordination. Experiments conducted on two lung cancer CT segmentation datasets and the Synapse multi-organ dataset demonstrate that Att-BrainNet outperforms existing mainstream segmentation models in terms of both accuracy and generalization. Further ablation studies and mechanism visualizations confirm the effectiveness of the BrainNet architecture and the dynamic scheduling strategy. This research provides a novel structural paradigm for medical image segmentation and holds promise for extension to other complex segmentation scenarios.

To evaluate the effects of super-resolution deep learning-based reconstruction (SR-DLR) on thin-slice T2-weighted hippocampal MR image quality using 3 T MRI, in both human volunteers and phantoms. Thirteen healthy volunteers underwent hippocampal MRI at standard and high resolutions. Original (standard-resolution; StR) images were reconstructed with and without deep learning-based reconstruction (DLR) (Matrix = 320 × 320), and with SR-DLR (Matrix = 960 × 960). High-resolution (HR) images were also reconstructed with/without DLR (Matrix = 960 × 960). Contrast, contrast-to-noise ratio (CNR), and septum slope were analyzed. Two radiologists evaluated the images for noise, contrast, artifacts, sharpness, and overall quality. Quantitative and qualitative results are reported as medians and interquartile ranges (IQR). Comparisons used the Wilcoxon signed-rank test with Holm correction. We also scanned an American College of Radiology (ACR) phantom to evaluate the ability of our SR-DLR approach to reduce artifacts induced by zero-padding interpolation (ZIP). SR-DLR exhibited contrast comparable to original images and significantly higher than HR-images. Its slope was comparable to that of HR images but was significantly steeper than that of StR images (p < 0.01). Furthermore, the CNR of SR-DLR (10.53; IQR: 10.08, 11.69) was significantly superior to the StR-images without DLR (7.5; IQR: 6.4, 8.37), StR-images with DLR (8.73; IQR: 7.68, 9.0), HR-images without DLR (2.24; IQR: 1.43, 2.38), and HR-images with DLR (4.84; IQR: 2.99, 5.43) (p < 0.05). In the phantom study, artifacts induced by ZIP were scarcely observed when using SR-DLR. SR-DLR for hippocampal MRI potentially improves image quality beyond that of actual HR-images while reducing acquisition time.

Large Language Models (LLMs) show promise for generating patient-friendly radiology reports, but the performance of open-source versus proprietary LLMs needs assessment. To compare open-source and proprietary LLMs in generating patient-friendly radiology reports from chest CTs using quantitative readability metrics and qualitative assessments by radiologists. Fifty chest CT reports were processed by seven LLMs: three open-source models (Llama-3-70b, Mistral-7b, Mixtral-8x7b) and four proprietary models (GPT-4, GPT-3.5-Turbo, Claude-3-Opus, Gemini-Ultra). Simplification was evaluated using five quantitative readability metrics. Three radiologists rated patient-friendliness on a five-point Likert scale across five criteria. Content and coherence errors were counted. Inter-rater reliability and differences among models were statistically assessed. Inter-rater reliability was substantial to near perfect (κ = 0.76-0.86). Qualitatively, Llama-3-70b was non-inferior to leading proprietary models in 4/5 categories. GPT-3.5-Turbo showed the best overall readability, outperforming GPT-4 in two metrics. Llama-3-70b outperformed GPT-3.5-Turbo on the CLI (p = 0.006). Claude-3-Opus and Gemini-Ultra scored lower on readability but were rated highly in qualitative assessments. Claude-3-Opus maintained perfect factual accuracy. Claude-3-Opus and GPT-4 outperformed Llama-3-70b in emotional sensitivity (90.0 % vs 46.0 %, p < 0.001). Llama-3-70b shows strong potential in generating quality, patient-friendly radiology reports, challenging proprietary models. With further adaptation, open-source LLMs could advance patient-friendly reporting technology.

Harmonic Motion Imaging (HMI) is an ultrasound elasticity imaging method that measures the mechanical properties of tissue using amplitude-modulated acoustic radiation force (AM-ARF). Multi-frequency HMI (MF-HMI) excites tissue at various AM frequencies simultaneously, allowing for image optimization without prior knowledge of inclusion size and stiffness. However, challenges remain in size estimation as inconsistent boundary effects result in different perceived sizes across AM frequencies. Herein, we developed an automated assessment method for tumor and focused ultrasound surgery (FUS) induced lesions using a transformer-based multi-modality neural network, HMINet, and further automated neoadjuvant chemotherapy (NACT) response prediction. HMINet was trained on 380 pairs of MF-HMI and B-mode images of phantoms and in vivo orthotopic breast cancer mice (4T1). Test datasets included phantoms (n = 32), in vivo 4T1 mice (n = 24), breast cancer patients (n = 20), FUS-induced lesions in ex vivo animal tissue and in vivo clinical settings with real-time inference, with average segmentation accuracy (Dice) of 0.91, 0.83, 0.80, and 0.81, respectively. HMINet outperformed state-of-the-art models; we also demonstrated the enhanced robustness of the multi-modality strategy over B-mode-only, both quantitatively through Dice scores and in terms of interpretation using saliency analysis. The contribution of AM frequency based on the number of salient pixels showed that the most significant AM frequencies are 800 and 200 Hz across clinical cases. We developed an automated, multimodality ultrasound-based tumor and FUS lesion assessment method, which facilitates the clinical translation of stiffness-based breast cancer treatment response prediction and real-time image-guided FUS therapy.

Features of new bleeding on conventional imaging in cerebral cavernous malformations (CCMs) often disappear after several weeks, yet the risk of rebleeding persists long thereafter. Increases in mean lesional quantitative susceptibility mapping (QSM) ≥ 6% on MRI during 1 year of prospective surveillance have been associated with new symptomatic hemorrhage (SH) during that period. The authors hypothesized that QSM at a single time point reflects features of hemorrhage in the prior year or potential bleeding in the subsequent year. Twenty-eight features were extracted from 265 QSM acquisitions in 120 patients enrolled in a prospective trial readiness project, and machine learning methods examined associations with SH and biomarker bleed (QSM increase ≥ 6%) in prior and subsequent years. QSM features including sum variance, variance, and correlation had lower average values in lesions with SH in the prior year (p < 0.05, false discovery rate corrected). A support-vector machine classifier recurrently selected sum average, mean lesional QSM, sphericity, and margin sharpness features to distinguish biomarker bleeds in the prior year (area under the curve = 0.61, 95% CI 0.52-0.70; p = 0.02). No QSM features were associated with a subsequent bleed. These results provide proof of concept that machine learning may derive features of QSM reflecting prior hemorrhagic activity, meriting further investigation. Clinical trial registration no.: NCT03652181 (ClinicalTrials.gov).

Targeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strategy for managing pain and preventing neuroma formation after amputation. This shift in TMR's motivation has influenced surgical approaches, in ways that may challenge conventional electromyography (EMG)-based prosthetic control. The primary goal is often to simply reinnervate nerves to accessible muscles. This contrasts the earlier, more complex TMR surgeries that optimize EMG signal detection by carefully selecting target muscles near the skin's surface and manipulate residual anatomy to electrically isolate muscle activity. Consequently, modern TMR surgeries can involve less consideration for factors such as the depth of the reinnervated muscles or electrical crosstalk between closely located reinnervated muscles, all of which can impair the effectiveness of conventional prosthetic control systems. We recruited 4 participants with TMR, varying levels of upper limb loss, and diverse sets of reinnervated muscles. Participants attempted performing movements with their missing hands and we used a muscle activity measurement technique that employs ultrasound imaging and machine learning (sonomyography) to classify the resulting muscle movements. We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. Our findings suggest that despite the shifting motivations for performing TMR surgery this new generation of the surgical procedure not only offers prophylactic benefits but also retains promising opportunities for bionic prosthetic control.

Urinary tract dilation (UTD) is a frequent problem in infants. Automated and objective classification of UTD from renal ultrasounds would streamline their interpretations. To develop and evaluate the performance of different deep learning models in predicting UTD classifications from renal ultrasound images. We searched our image archive to identify renal ultrasounds performed in infants ≤ 3-months-old for the clinical indications of prenatal UTD and urinary tract infection (9/2023-8/2024). An expert pediatric uroradiologist provided the ground truth UTD labels for representative sagittal sonographic renal images. Three different deep learning models trained with cross-entropy loss were adapted with four-fold cross-validation experiments to determine the overall performance. Our curated database included 492 right and 487 left renal ultrasounds (mean age ± standard deviation = 1.2 ± 0.1 months for both cohorts, with 341 boys/151 girls and 339 boys/148 girls, respectively). The model prediction accuracies for the right and left kidneys were 88.7% (95% confidence interval [CI], [85.8%, 91.5%]) and 80.5% (95% CI, [77.6%, 82.9%]), with weighted kappa scores of 0.90 (95% CI, [0.88, 0.91]) and 0.87 (95% CI, [0.82, 0.92]), respectively. When predictions were binarized into mild (normal/P1) and severe (UTD P2/P3) dilation, accuracies of the right and left kidneys increased to 96.3% (95% CI, [94.9%, 97.8%]) and 91.3% (95% CI, [88.5%, 94.2%]), but agreements decreased to 0.78 (95% CI, [0.73, 0.82]) and 0.75 (95% CI, [0.68, 0.82]), respectively. Deep learning models demonstrated high accuracy and agreement in classifying UTD from infant renal ultrasounds, supporting their potential as decision-support tools in clinical workflows.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations. Efforts to quantify its various components using CT imaging have advanced, aiming for more precise, objective, and reproducible assessment and management. Beyond emphysema and small airway disease, the two major components of COPD, CT quantification enables the evaluation of pulmonary vascular alteration, ventilation-perfusion mismatches, fissure completeness, and extrapulmonary features such as altered body composition, osteoporosis, and atherosclerosis. Recent advancements, including the application of deep learning techniques, have facilitated fully automated segmentation and quantification of CT parameters, while innovations such as image standardization hold promise for enhancing clinical applicability. Numerous studies have reported associations between quantitative CT parameters and clinical or physiologic outcomes in patients with COPD. However, barriers remain to the routine implementation of these technologies in clinical practice. This review highlights recent research on COPD quantification, explores advances in technology, and also discusses current challenges and potential solutions for improving quantification methods.

Multiparametric MRI (mpMRI) has become an essential tool in the detection of prostate cancer (PCa) and can help many men avoid unnecessary biopsies. However, interpreting prostate mpMRI remains subjective, labor-intensive, and more complex compared to traditional transrectal ultrasound. These challenges will likely grow as MRI is increasingly adopted for PCa screening and diagnosis. This development has sparked interest in non-invasive artificial intelligence (AI) support, as larger and better-labeled datasets now enable deep-learning (DL) models to address important tasks in the prostate MRI workflow. Specifically, DL classification networks can be trained to differentiate between benign tissue and PCa, identify non-clinically significant disease versus clinically significant disease, and predict high-grade cancer at both the lesion and patient levels. This review focuses on the integration of DL classification networks with mpMRI for PCa assessment, examining key network architectures and strategies, the impact of different MRI sequence inputs on model performance, and the added value of incorporating domain knowledge and clinical information into MRI-based DL classifiers. It also highlights reported comparisons between DL models and the Prostate Imaging Reporting and Data System (PI-RADS) for PCa diagnosis and the potential of AI-assisted predictions, alongside ongoing efforts to improve model explainability and interpretability to support clinical trust and adoption. It further discusses the potential role of DL-based computer-aided diagnosis systems in improving the prostate MRI reporting workflow while addressing current limitations and future outlooks to facilitate better clinical integration of these systems. Evidence Level: N/A. Technical Efficacy: Stage 2.

Coronary artery calcification (CAC) provides robust prediction for major adverse cardiovascular events (MACE), but current techniques disregard plaque distribution and protective effects of high CAC density. We investigated whether a novel CAC-dispersion and density (CAC-DAD) score will exhibit superior prognostic value compared with the Agatston score (AS) for MACE prediction. We conducted a multicenter, retrospective, cross-sectional study of 961 patients (median age, 67 years; 61% male) who underwent cardiac computed tomography for cardiovascular or perioperative risk assessment. Blinded analyzers applied deep learning algorithms to noncontrast scans to calculate the CAC-DAD score, which adjusts for the spatial distribution of CAC and assigns a protective weight factor for lesions with ≥1000 Hounsfield units. Associations were assessed using frailty regression. Over a median follow-up of 30 (30-460) days, 61 patients experienced MACE (nonfatal myocardial infarction or cardiovascular mortality). An elevated CAC-DAD score (≥2050 based on optimal cutoff) captured more MACE than AS ≥400 (74% versus 57%; <i>P</i>=0.002). Univariable analysis revealed that an elevated CAC-DAD score, AS ≥400 and AS ≥100, age, diabetes, hypertension, and statin use predicted MACE. On multivariable analysis, only the CAC-DAD score (hazard ratio, 2.57 [95% CI, 1.43-4.61]; <i>P</i>=0.002), age, statins, and diabetes remained significant. The inclusion of the CAC-DAD score in a predictive model containing demographic factors and AS improved the C statistic from 0.61 to 0.66 (<i>P</i>=0.008). The fully automated CAC-DAD score improves MACE prediction compared with the AS. Patients with a high CAC-DAD score, including those with a low AS, may be at higher risk and warrant intensification of their preventative therapies.