Effectiveness of fat suppression methods and influence on proton-resonance frequency shift (PRFS) MR thermometry

PURPOSE

To evaluate the effectiveness of fat suppression techniques experimentally and illustrate their influence on the accuracy of PRFS MR-thermometry.

METHODS

The residual magnitudes of the main fat peaks are measured using a water-fat decomposition algorithm in an oil phantom and in vivo in swine bone marrow, either with spectral fat saturation (FS), water excitation (WE) or fast water excitation (FWE), as implemented on 1.5 T whole-body clinical MRIs. Thermometry experiments in tissue-mimicking oil-water phantoms (10 and 30 % fat) allow determining temperature errors in PRFS MR-thermometry with no fat suppression, FS and WE, compared against reference fiber optic thermometry.

RESULTS

WE attenuates the signal of the main methylene fat peak more than FS (2 % and 22 % amplitude attenuation in the oil phantom, respectively), while the olefinic and glycerol peaks surrounding the water peak remain unaltered with both FS and WE. Within the 37 °C to 60 °C temperature range explored, FS and WE strongly attenuate temperature errors compared to PRFS without fat suppression. The residual fat signal after FS and WE leads to errors in PRFS thermometry, that increase with the fat content and oscillate with TE and temperature. In our tests limited to a single MR provider, fat suppression with WE appears to suppress fat signal more effectively.

CONCLUSIONS

We propose a protocol to quantify the remaining fraction of each spectral fat peak after fat suppression. In PRFS thermometry, despite spectral fat suppression, the remnant fat signal leads to temperature underestimation or overestimation depending on TE, fat fraction and temperature range. Fat suppression techniques should be evaluated specifically for quantitative MRI methods such as PRFS thermometry.

Accelerator based-BNCT facilities worldwide and an update of the Buenos Aires project

Globally, there are several AB-BNCT facilities, either operational and already treating patients and others still under development. These facilities range from high-energy 30 MeV cyclotrons using the 9Be(p,n) reaction, medium-energy RFQ-DTL accelerators operating at 8-10 MeV with the same reaction, low-energy electrostatic machines (Tandem and single-ended) using the 7Li(p,n) reaction at approximately 2.5 MeV. Additionally, the low-energy ESQ accelerator developed in Argentina, employs the 9Be(d,n) or the 13C(d,n) reactions at 1.45 MeV. This paper presents updated insights into Argentina's AB-BNCT project while also providing a global overview of AB-BNCT developments.

Effect of FLASH proton therapy on primary bronchial epithelial cell organoids

Purpose

The effects of conventional (CONV) and FLASH proton therapy on primary bronchial epithelial cell (PBEC) organoids from individuals with chronic obstructive pulmonary disease (COPD) were investigated. The primary objective was to compare the effect of FLASH and CONV on COPD PBEC organoids with a focus on DNA damage, organoid formation, and gene expression.

Methods

PBECs were obtained from six COPD donors, cultured as three-dimensional (3D) organoids and exposed to 2 and 8 Gy CONV and FLASH proton radiation at the Holland Proton Therapy Center. DNA damage was assessed by γH2AX staining. Organoid formation capacity was assessed by counting the organoids formed after reseeding irradiated cells at 24 h and 7 days. Bulk RNA sequencing (RNAseq) and qPCR analyses were performed to identify pathways and differences in the radiation response.

Results

γH2AX foci analysis showed a significant dose-dependent increase in DNA damage at 1 h for both CONV and FLASH treatments, without differences between the two modalities. Organoid formation assays revealed a dose-dependent decrease in organoid formation capacity at 24 h for both treatments. At 7 days, 2 Gy FLASH-treated samples showed significantly reduced organoid formation compared to 2 Gy CONV (p = 0.008). RNAseq identified CONV and FLASH-induced changes in expression of DNA-damage response and apoptosis pathway genes. A dose-dependent upregulation of MDM2, GDF15, DDB2, BAX, P21, AEN and a decrease in MKi67 expression was confirmed by qPCR analysis.

Conclusion

No significant differences were found in DNA damage or gene expression profiles between CONV and FLASH. The organoid formation assay showed a prolonged detrimental effect in the FLASH-treated organoids, suggesting a more complex interaction of FLASH with lung epithelial cells. The results of this study contribute to the advancement of robust in vitro human lung models for investigating the mechanisms of action of FLASH, potentially facilitating the treatment of NSCLC patients with proton FLASH therapy.

Large blood vessel segmentation in quantitative DCE-MRI of brain tumors: A Swin UNETR approach

Brain tumor growth is associated with angiogenesis, wherein the density of newly developed blood vessels indicates tumor progression and correlates with the tumor grade. Quantitative dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has shown potential in brain tumor grading and treatment response assessment. Segmentation of large-blood-vessels is crucial for automatic and accurate tumor grading using quantitative DCE-MRI. Traditional manual and semi-manual rule-based large-blood-vessel segmentation methods are time-intensive and prone to errors. This study proposes a novel deep learning-based technique for automatic large-blood-vessel segmentation using Swin UNETR architectures and comparing it with U-Net and Attention U-Net architectures. The study employed MRI data from 187 brain tumor patients, with training, validation, and testing datasets sourced from two centers, two vendors, and two field-strength magnetic resonance scanners. To test the generalizability of the developed model, testing was also carried out on different brain tumor types, including lymphoma and metastasis. Performance evaluation demonstrated that Swin UNETR outperformed other models in segmenting large-blood-vessel regions (achieving Dice scores of 0.979, and 0.973 on training and validation sets, respectively, with test set performance ranging from 0.835 to 0.982). Moreover, most quantitative parameters showed significant differences (p < 0.05) between with and without large-blood-vessel. After large-blood-vessel removal, using both ground truth and predicted masks, the values of parameters in non-vascular tumoral regions were statistically similar (p > 0.05). The proposed approach has potential applications in improving the accuracy of automatic grading of tumors as well as in treatment planning.

The effect of radiation dose to the brain on early self-reported cognitive function in brain and head-and-neck cancer patients

Purpose

Assess cognitive changes after radiotherapy (RT) in brain and head-and-neck (HN) cancer patients using patient-reported outcome measures (PROMs) and evaluate a dose-effect relationship for brain structures.

Materials and methods

Primary brain and HN cancer patients treated with RT between 2012-2021 were included. Patient characteristics, clinical parameters, and PROMs at baseline and 1-year follow-up were collected. Cognitive functioning (CF) from the EORTC QLQ-C30, communication deficit (CD) from the QLQ-BN20, and one cognition-related questions from the EQ6D questionnaire were used, the latter two only for brain patients. Missing data were imputed and the four-point scale scores were transformed to a 100-point scale. Change in scores from baseline to 1-year were categorized into improvement/constant or deterioration. Organs-at-risk (OARs) were contoured either clinically or retrospectively using autocontouring and dose to the OARs were calculated.

Results

A total of 110 brain and 356 HN cancer patients were included. Median age was 56 (brain) and 67.5 (HN) years. Baseline and 1-year CF was significantly lower for brain patients (p < 0.001). Univariate analysis for ΔCF showed that age at start RT ≤ 65 years, receiving chemotherapy, higher CF Baseline score, brain mean dose > 3 Gy, and multiple dose levels to left and right hippocampus were statistically associated with cognitive deterioration. Multivariate analysis for ΔCF identified age at RT ≤ 65 years, higher CF Baseline score, and brain mean dose > 3 Gy as significant predictors.

Conclusion

This study identified risk factors for subjective cognitive decline and suggests that patients' self-perceived cognitive deterioration may be related to age, CF baseline score and brain radiation dose above 3 Gy.

Low dose computed tomography reconstruction with momentum-based frequency adjustment network

BACKGROUND AND OBJECTIVE

Recent investigations into Low-Dose Computed Tomography (LDCT) reconstruction methods have brought Model-Based Data-Driven (MBDD) approaches to the forefront. One prominent architecture within MBDD entails the integration of Model-Based Iterative Reconstruction (MBIR) with Deep Learning (DL). While this approach offers the advantage of harnessing information from sinogram and image domains, it also reveals several deficiencies. First and foremost, the efficacy of DL methods within the realm of MBDD necessitates meticulous enhancement, as it directly impacts the computational cost and the quality of reconstructed images. Next, high computational costs and a high number of iterations limit the development of MBDD methods. Last but not least, CT reconstruction is sensitive to pixel accuracy, and the role of loss functions within DL methods is crucial for meeting this requirement.

METHODS

This paper advances MBDD methods through three principal contributions. Firstly, we introduce an innovative Frequency Adjustment Network (FAN) that effectively adjusts both high and low-frequency components during the inference phase, resulting in substantial enhancements in reconstruction performance. Second, we develop the Momentum-based Frequency Adjustment Network (MFAN), which leverages momentum terms as an extrapolation strategy to facilitate the amplification of changes throughout successive iterations, culminating in a rapid convergence framework. Lastly, we delve into the visual properties of CT images and present a unique loss function named Focal Detail Loss (FDL). The FDL function preserves fine details throughout the training phase, significantly improving reconstruction quality.

RESULTS

Through a series of experiments validation on the AAPM-Mayo public dataset and real-world piglet datasets, the aforementioned three contributions demonstrated superior performance. MFAN achieved convergence in 10 iterations as an iteration method, faster than other methods. Ablation studies further highlight the advanced performance of each contribution.

CONCLUSIONS

This paper presents an MBDD-based LDCT reconstruction method using a momentum-based frequency adjustment network with a focal detail loss function. This approach significantly reduces the number of iterations required for convergence while achieving superior reconstruction results in visual and numerical analyses.

Comparison of dose distribution with and without reflecting heterogeneous boron distribution using 18F-BPA positron emission tomography in boron neutron capture therapy

This study investigated the dosimetric impact of implementing heterogeneous boron distribution into dose evaluations on tumors in BNCT. The study involved 27 patients who underwent 18F-BPA PET/CT scans. Dose evaluations were performed using various methods reflecting boron concentrations on CT images. The conventional dose evaluations, which reflected uniform boron concentration in blood of 25 ppm and a constant tumor-to-blood (T/B) ratio of 3.5, were compared with individual dose evaluations, which reflected the blood boron concentration and T/B ratio calculated from 18F-BPA in each patient. The heterogeneous tumoral dose distribution was also compared, revealing the dosimetric impact of the boron distribution calculated from each voxel of 18F-BPA. The spatial correspondence between 18F-BPA and dose distribution was compared using metabolic tumor volume (MTV) from 18F-BPA and isodose volume from the heterogeneous dose distribution. Results showed that the median blood boron concentration and T/B ratio calculated from 18F-BPA were 25.57 (23.90-27.84) ppm and 3.75 (2.54-4.59), respectively, comparable to those in the conventional dose evaluations. All dose indices in the heterogeneous tumoral dose evaluations were significantly lower than those in the conventional dose evaluations (p < 0.01). However, the spatial correspondence between the 18F-BPA and the dose distribution was not observed in the dice similarity coefficients of both MTV40-40% isodose volume and MTV50-50% isodose volume. In conclusion, the study confirmed the validity of applying the boron concentration calculated from 18F-BPA to the dose evaluation for a patient in BNCT. The differences might be associated with non-inter-patient variations of 18F-BPA, but the conventional dose evaluations mainly focused on the high boron concentration area within the tumor. Furthermore, the discrepancies in the patients were also observed between the 18F-BPA distribution and the heterogeneous dose distribution. Therefore, this study suggested that the indications for BNCT should consider not only 18F-BPA but also the dose distributions, which could reflect the heterogeneous tumoral boron distribution.

Produced radioactive isotopes, ambient dose equivalent in TrueBeam room with flattening filter (FF) and Flattening Filter Free (FFF) modes: Monte Carlo simulation

BACKGROUND

High-energy linear accelerators used in radiotherapy produce unexpected neutrons that can activate materials in the treatment room.

MATERIALS AND METHODS

The research group used PHITS code to simulate TrueBeam Linac head with 10 MV photons. Then, PHITS codes connected Dchain code to calculate the variation of the induced radioactivity, the ambient dose equivalent during TrueBeam radiation 4 Gy with dose rate 400 MU (Monitor Unit)/minute in FF mode, 24 Gy with dose rate 2400 MU/minute in FFF mode, and cooling time (14 min).

RESULTS

One minute of beam-on TrueBeam with 10 MV photon energy produced six mainly produced nuclides (183 mW, 24mNa, 185 mW, 28Al, 24mNa, and 187W). FFF mode had more activity of the produced isotopes than those in FF mode (1.60 × 1014 Bq compared with 1.57 × 1014 Bq-corresponding to ∼1.71% higher). The differences in the activity of isotopes and ambient dose between FF and FFF mode were mostly less than a few percent; however, considering that the MU per beam current is six times higher in the FFF mode than in the FF mode, the induced activities per MU can be reduced by roughly six times when using the FFF mode instead of the FF mode.

CONCLUSION

The isotope activities of the produced isotopes in the FF and FFF modes are essentially equivalent when normalized per beam current.

Lucerne milestone approach for benchmarking and education: Towards ultra-low dose endovascular aortic repair

Objective

The aim of this single-center case series is to demonstrate that an ultra-low dose (ULD) can be routinely achieved in the hybrid operating room in standard endovascular aortic repair (EVAR) for infrarenal abdominal aortic aneurysm by adjusting the manufacturer's predefined imaging parameters, hardware configurations and user protocols (including benchmarking).

Methods

The hybrid operating room manufacturer predefined EVAR software setup of the dose exposure control software (OPTIQ, Siemens Healthineers, Forchheim, Germany) at our university medical center was screened for possible improvements regarding radiation dose application. Tests on a water-equivalent as well as polymethyl methacrylate phantom model to assess the impact of technical settings were performed, including comparison of settings for exposure control software, different magnification, collimation configurations and detector distance. All results were transferred into modified setups for the exposure control software and a new ULD procedure protocol for EVAR. Additionally, to standardize the clinical pathway, the Lucerne EVAR Milestone Approach (LEMA) was introduced including preoperative, perioperative, and postoperative milestones for technical procedure content and dose benchmarking during EVAR. A validation of the new settings including revised software setup, procedure protocol, and applicability of LEMA on a consecutive EVAR case series was conducted. Ten consecutive patients undergoing EVAR for low and medium complexity infrarenal abdominal aortic aneurysm were included. The primary outcome parameter was intraoperative dose area product (DAP, measured in Gy·cm2). Secondary outcomes were median fluoroscopy time (in minutes:seconds), cumulative air kerma (in mGy), clinical success, and occurrence of endoleaks.

Results

New ULD settings compared with previous manufacturers standard settings of dose exposure control software reduced DAP for both fluoroscopy (0.0382 Gy·cm2/min vs 0.3 Gy·cm2/min) and angiography (2.36 Gy·cm2/min vs 2.48 Gy·cm2/min). Digital magnification and collimation decreased DAP. Application of the new ULD standard EVAR protocol resulted in a median DAP of 5.6 Gy·cm2 (range, 3.54-12.1 Gy·cm2). Median fluoroscopy time was 16 minutes and 32 seconds. Type I endoleaks occurred in no patients (0%), type II in five patients (50%), and type III in no patients (0%). No patient had to undergo reintervention owing to endoleak or absence of diameter shrinkage during the first postoperative year.

Conclusions

Revision of the manufacturer-predefined EVAR setup by testing and ensuring optimal imaging parameters and hardware configurations in combination with LEMA enabled performance of ULD standard EVAR procedures routinely without compromising imaging quality.

FLASH Radiotherapy: From In Vivo Data to Clinical Translation

Delivery of radiotherapy (RT) at ultra-high dose rates or FLASH radiotherapy (FLASH-RT) is an emerging treatment option for patients with cancer that could increase survival outcomes and quality of life. In vivo data across a multitude of normal tissues and associated tumors have been published demonstrating the FLASH effect while bringing attention to the need for additional research. Combination of FLASH-RT with other treatment options including spatially fractionated RT, immunotherapy, and usage in the setting of reirradiation could also provide additional benefit. Phase I clinical trials have shown promising results, yet research is warranted before routine clinical use of FLASH-RT.

Recovered water - H218O from the 18F[FDG] production as liquid radioactive waste

In this study, ten recovered water samples were analysed using gamma spectrometry and Liquid Scintillation Counting techniques for identification of radioactive impurities (quality and quantity) and for radioactive waste qualifications. The presence of several radioactive isotopes of 3H, 56,57Co 52Mn in the recovered [18O] water irradiated with 11 MeV protons used to produce [18F] fluoride by the 18O(p,n)18F reaction has been confirmed. Radioactive impurities were generated directly in enriched water or washed out from activated Havar foil, or tantalum body target material. The highest impact on the qualification of the recovered water remains after the production as a radioactive waste has 56Co. The highest activity concentration of about 0.1 GBq/ml has been detected in the case of tritium 3H. All ten samples were qualified as transitional, low-level radioactive wastes.

C-UQ: Conflict-based uncertainty quantification-A case study in lung cancer classification

Uncertainty quantification is crucial in deep learning, especially in medical diagnostics, to measure model prediction confidence and ensure reliable clinical decisions. This study introduces a novel conflict-based uncertainty quantification approach, applied as a case study in lung cancer classification, leveraging Dempster-Shafer Theory in conjunction with Deep Ensemble methods. The proposed method aggregates predictions from multiple neural network models using conflict as an uncertainty measure. By converting softmax outputs into Basic Belief Assignments and applying the rule of combination, this conflict-based method effectively quantifies uncertainty: high conflict values indicate predictions requiring expert review, and low values are considered reliable. Evaluations on the LIDC-IDRI dataset and additional 3D biomedical datasets show that the proposed method achieved high accuracy (0.957) and URecall (0.819) for lung classification. The sensitivity analysis further revealed that increasing the ensemble size enhanced performance even though the computational demands may challenge real-time applications. In contrast, the entropy-based smoothing effect limits the accuracy improvement of traditional Deep Ensemble methods. In addition, Out-of-Distribution detection with the proposed method achieved AUC scores up to 0.864 across various datasets. Future work will focus on optimising efficiency and exploring alternative Dempster-Shafer Theory combination rules and hybrid models.

Systematic Review of 3D-Printed Ultrasound-able Models in Graduate Medical Education

This systematic review aimed to identify studies that have created ultrasound-able models for resident procedural training by means of 3D-printing techniques and examine their tissue specific properties. There were 456 articles identified from 3 databases, of which, 35 studies were assessed for eligibility, and 11 total studies were included. All qualitative studies showed improvements in procedural skills and 89% of the quantitative studies showed significant results. Studies that documented modeling price showed a 90% reduction in fabrication cost compared to commercial models. Three-dimensional-printed, ultrasound-able models have the potential to provide trainees with low-cost, high-fidelity training opportunities.

Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound

RATIONALE

Lung ultrasound, the most precise diagnostic tool for pleural effusions, is underutilized due to healthcare providers' limited proficiency. To address this, deep learning models can be trained to recognize pleural effusions. However, current models lack the ability to diagnose effusions in diverse clinical contexts, which presents significant challenges.

OBJECTIVE

To develop and validate a deep learning model for detecting pleural effusions in lung ultrasound images, with adaptable performance characteristics tailored to specific clinical scenarios.

METHODS

A retrospective study was conducted at two Canadian tertiary hospitals to evaluate the detection of pleural effusions of varying sizes and complexities using lung ultrasound. A deep learning model incorporating a frame-level convolutional neural network and a clip-level prediction algorithm was developed and validated against expert annotations.

RESULTS

The model was evaluated using a holdout dataset of 103 lung ultrasound clips from 46 patients with pleural effusion and 136 clips from 83 patients without effusion. The general model achieved a sensitivity of 0.90 for small-to-large effusions, with a specificity of 0.89. The large effusion model demonstrated a sensitivity of 0.97 for large effusions while maintaining a specificity of 0.90. The trauma model showed high sensitivity to all effusions, including trace (0.91) and small (0.97) effusions.

CONCLUSION

Our research highlights the development of a deep learning model that effectively detects pleural effusions of varying sizes and complexities on lung ultrasound in different clinical settings. This tool has the potential to enhance emergency physicians' ability to quickly and accurately diagnose effusions, particularly in time-sensitive situations.

Assessment of Emphysema on X-ray Equivalent Dose Photon-Counting Detector CT: Evaluation of Visual Scoring and Automated Quantification Algorithms

OBJECTIVES

The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials.

METHODS

One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read. In the second step, automated emphysema quantification was performed using an established LAV algorithm with a threshold of -950 HU and a commercially available deep learning model for automated emphysema quantification. Automated estimations of emphysema extent were converted and compared with visual scoring ratings.

RESULTS

X-ray dose scans exhibited a significantly lower computed tomography dose index than low-dose scans (low-dose: 0.66 ± 0.16 mGy, x-ray dose: 0.11 ± 0.03 mGy, P < 0.001). Interreader agreement between low- and x-ray dose for visual emphysema scoring was excellent (κ = 0.83). Visual emphysema scoring consensus showed good agreement between low-dose and x-ray dose scans (κ = 0.70), with significant and strong correlation (Spearman ρ = 0.79). Although trace emphysema was underestimated in x-ray dose scans, there was no significant difference in the detection of higher-grade (mild to advanced destructive) emphysema ( P = 0.125) between the 2 scan doses. Although predicted emphysema volumes on x-ray dose scans for the LAV method showed strong and the deep learning model excellent significant correlations with predictions on low-dose scans, both methods significantly overestimated emphysema volumes on lower quality scans ( P < 0.001), with the deep learning model being more robust. Further, deep learning emphysema severity estimations showed higher agreement (κ = 0.65) and correlation (Spearman ρ = 0.64) with visual scoring for low-dose scans than LAV predictions (κ = 0.48, Spearman ρ = 0.45).

CONCLUSIONS

The severity of emphysema can be reliably estimated using visual scoring on CT scans performed with x-ray equivalent doses on a PCD-CT. A deep learning algorithm demonstrated good agreement and strong correlation with the visual scoring method on low-dose scans. However, both the deep learning and LAV algorithms overestimated emphysema extent on x-ray dose scans. Nonetheless, x-ray equivalent radiation dose scans may revolutionize the detection and monitoring of disease in chronic obstructive pulmonary disease patients.

FDuDoCLNet: Fully dual-domain contrastive learning network for parallel MRI reconstruction

Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that is widely used for high-resolution imaging of soft tissues and organs. However, the slow speed of MRI imaging, especially in high-resolution or dynamic scans, makes MRI reconstruction an important research topic. Currently, MRI reconstruction methods based on deep learning (DL) have garnered significant attention, and they improve the reconstruction quality by learning complex image features. However, DL-based MR image reconstruction methods exhibit certain limitations. First, the existing reconstruction networks seldom account for the diverse frequency features in the wavelet domain. Second, existing dual-domain reconstruction methods may pay too much attention to the features of a single domain (such as the global information in the image domain or the local details in the wavelet domain), resulting in the loss of either critical global structures or fine details in certain regions of the reconstructed image. In this work, inspired by the lifting scheme in wavelet theory, we propose a novel Fully Dual-Domain Contrastive Learning Network (FDuDoCLNet) based on variational networks (VarNet) for accelerating PI in both the image and wavelet domains. It is composed of several cascaded dual-domain regularization units and data consistency (DC) layers, in which a novel dual-domain contrastive loss is introduced to optimize the reconstruction performance effectively. The proposed FDuDoCLNet was evaluated on the publicly available fastMRI multi-coil knee dataset under a 6× acceleration factor, achieving a PSNR of 34.439 dB and a SSIM of 0.895.

Evaluation of 18F-FDG absorbed dose ratios in percent in adult and pediatric reference phantoms using DoseCalcs Monte Carlo platform

This study investigates the field of radiation exposure in nuclear medicine, which might have implications for exposure to ionizing radiation in pediatric cases. To demonstrate the difference in radiosensitivity between younger patients and adults and to highlight the need for individualized radiation protection procedures when investigating medical imaging and therapy, this study examines the absorbed dose ratios in percent (ADR%) for 18F-FDG. This parameter is an important indicator, illustrating the percentage of radiation dose absorbed by specific organs/tissues concerning the emitted radiation from different body regions. The methodology involves calculating ADR% in twelve voxel-based models for adults, children, and newborns, as referenced by International Commission on Radiological Protection (ICRP) Publications 110 and 143. The simulations used the 18F positron spectrum from ICRP Publication 107 and Livermore models. These simulations were performed using the DoseCalcs Monte Carlo platform. We have calculated the S-values and ADR% using the DoseCalcs simulations of the 18F positrons and provided a comprehensive dataset of ADR% results. This dataset evaluates the impact of anatomical variation on absorbed dose in target regions. It consists of 141 target regions and 8 different source regions. Significant differences in radiosensitivity were observed in ADR% values among various source-target combinations for each age and sex group. The self-irradiation ADR% reaches up to 95%, while the cross-irradiation ADR% varies, ranging approximately from 0.1% to 12%, depending on the mass of the target organ, the distance between it and the source organ, and the chemical composition of these organs. Also, the variations observed across different age and sex phantoms highlight the importance of personalized internal dosimetry, especially for pediatric cases with heightened radiosensitivity. Healthcare practitioners can use the dataset of ADR% values as the first stage to illustrate variability and optimize nuclear medicine imaging with 18F-FDG while reducing radiation risks.

Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping

Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular subtypes at the same time and provide reliable imaging biomarkers is still challenging. To achieve this, we propose a novel cooperative multi-task learning network (CMTLNet) which consists of a task-common feature extraction (CFE) module, a task-specific unique feature extraction (UFE) module and a unique-common feature collaborative classification (UCFC) module. In CFE, a segmentation-free tumor feature perception (SFTFP) module is first designed to extract the tumor-aware features in a classification manner rather than a segmentation manner. Following that, based on the multi-scale tumor-aware features extracted by SFTFP module, CFE uses convolutional layers to further refine these features, from which the task-common features are learned. In UFE, based on orthogonal projection and conditional classification strategies, the task-specific unique features are extracted. In UCFC, the unique and common features are fused with an attention mechanism to make them adaptive to different glioma prediction tasks. Finally, deep features-guided interpretable radiomic biomarkers for each glioma prediction task are explored by combining SHAP values and correlation analysis. Through the comparisons with recent reported methods on a large multi-center dataset comprising over 1800 cases, we demonstrated the superiority of the proposed CMTLNet, with the mean Matthews correlation coefficient in validation and test sets improved by (4.1%, 10.7%), (3.6%, 23.4%), and (2.7%, 22.7%) respectively for glioma grading, 1p/19q and IDH status prediction tasks. In addition, we found that some radiomic features are highly related to uninterpretable deep features and that their variation trends are consistent in multi-center datasets, which can be taken as reliable imaging biomarkers for glioma diagnosis. The proposed CMTLNet provides an interpretable tool for glioma multi-task prediction, which is beneficial for glioma precise diagnosis and personalized treatment.

Dose response relation for optic nerve atrophy at low-dose rate brachytherapy of uveal melanoma

BACKGROUND

Dose-response relationships for optic neuropathy and optic nerve atrophy after brachytherapy for posterior uveal melanoma were poorly defined from previous studies. Here, the outcome differences were analyzed in dependence on dosimetric factors, the applicator type, and tumor dependent variables.

PURPOSE

Primary objective was to evaluate the association of applied dose and on-set of optic nerve atrophy after brachytherapy for posterior uveal melanoma in order to allow risk estimation for new patients.

MATERIALS AND METHODS

This retrospective study was performed at a single high volume centre for ocular oncology. Patients receiving brachytherapy with Ruthenium-106 applicators for posterior uveal melanoma with a maximum distance between optic nerve and the nearest tumor margin of 4 optical disc diameters and follow-up with fundus photographs were included. The dose distribution at the optic nerve was reconstructed from the fundus photographs at latest follow-up and the dose-distribution of the applicator using a dedicated software. A first mask with important structural elements such as optic nerve, macula, tumor and vessels was first superimposed on the fundus photograph and adapted to the real contours. In a second step, an applicator contour mask was adapted to the radiation scar in order to calculate the dose distribution in all structures. Dose-response relations were obtained by weighted logistic regression.

RESULTS

The maximum dose at the optic disc (ODmax) in this group of 109 patients ranged from 5.8 Gy - 242.2 Gy, median 48.7 Gy. Optic nerve atrophy was observed in 29patients. Median time to radiation induced optic nerve atrophy was 18months. Using weighted logistic regression, the dependence of optic nerve atrophy on ODmax was significant (p = 0.0001, chi2-test). There was a considerable interobserver variability in ODmax values (p < 0.02, signed rank test). An additional factor influencing the dose-response was the applicator type (p = 0.0315, chi2-test). The ODmax for a probability of optic nerve atrophy of 50 % (ED50) were 77.6 Gy ± 7.0 Gy for patients treated with notched COB applicators and 53.2 Gy ± 8.2 Gy for patients with other applicators. Including the applicator type, the area under ROC curve reached a value of 0.857 (95 %-CI: 0.793-0.921) for the logistic model with ODmax. The ED50 for optic nerve neuropathy, classified as grade ≥ 1 toxicity, was estimated to be 46.9 Gy ± 4.1 Gy for the maximum dose at the optic disc.

CONCLUSIONS

Significant dose-response curves were found for optic nerve atrophy at low dose rate brachytherapy. A standard position of COB applicators was identified that allows estimation of the dose-response relation from the scleral dose of the applicator for risk estimation without fundus photographs. This larger data set enhances the knowledge of dose-response relationships for irradiation near the optic nerve.

Assessment of Photon-Counting Computed Tomography for Quantitative Imaging in Radiation Therapy

PURPOSE

To test a first-generation clinical photon-counting computed tomography (PCCT) scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes.

METHODS AND MATERIALS

A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear) was scanned on a NAEOTOM Alpha PCCT and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LACs) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via "Rho/Z"-algorithm was also used. Proton stopping-power ratios (SPRs) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations within the material regions were evaluated for LAC, RED/EAN, and SPR, respectively. Two example ray projections were also examined for LAC, SPR, and water-equivalent thickness, for illustrations of a more treatment-like scenario.

RESULTS

There was a tendency toward narrower distributions for PCCT compared with both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored root-mean-square deviations showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium, the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile, whereas the more standard projection showed similar results between the modalities.

CONCLUSIONS

This study shows promising results for tissue characterization in a human-like geometry for radiation therapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

Effect of head size, head material, and radiation dose on the repeatability of CT-RSA measurements of femoral head penetration

BACKGROUND

The risk of early revision of total hip arthroplasty (THA) for polyethylene wear is now low, but there remains a need to perform wear measurements in patients for clinical surveillance. The gold standard of wear measurements has been radiostereometric analysis (RSA), which has limited availability. The use of computed tomography (CT) to perform THA wear measurement was described a decade ago and found to have acceptable accuracy and precision, but high radiation dose was a concern. Additionally, the use of larger femoral head sizes and ceramic femoral heads has risen in the past decade. The objectives of the study were to determine the effect of femoral head size, femoral head material, and lowered radiation dose on femoral head penetration measurement repeatability.

METHODS

A cadaveric hip was implanted with a cementless THA implant system. CT scans were acquired at a conventional radiation dose and at a reduced dose and repeated for a 32 mm and 36 mm cobalt-chromium femoral heads and a 32 mm ceramic femoral head. Apparent translation of the femoral head versus the acetabular cup was measured between the repeated scans using a CT-RSA software, where deviations from zero indicated measurement precision.

RESULTS

The mean and standard deviation of translations in all planes was <0.200 mm. There was no effect for 3D translation of increasing cobalt-chromium head size (p = 0.2252). Cobalt-chromium heads had superior repeatability compared to ceramic heads at reduced dose (p = 0.022), but not at conventional dose (p = 0.1265). Further, superior repeatability was achieved with the reduced dose scan for the cobalt-chromium head (p = 0.0058), however there was no difference between doses for the ceramic head (p = 0.8148).

DISCUSSION

CT-based wear measurement repeatability is excellent and consistent with prior literature even when implementing a larger femoral head, a ceramic femoral head, or reducing radiation dose to 25% of a conventional clinical scan.

Meta-learning guidance for robust medical image synthesis: Addressing the real-world misalignment and corruptions

Deep learning-based image synthesis for medical imaging is currently an active research topic with various clinically relevant applications. Recently, methods allowing training with misaligned data have started to emerge, yet current solution lack robustness and cannot handle other corruptions in the dataset. In this work, we propose a solution to this problem for training synthesis network for datasets affected by mis-registration, artifacts, and deformations. Our proposed method consists of three key innovations: meta-learning inspired re-weighting scheme to directly decrease the influence of corrupted instances in a mini-batch by assigning lower weights in the loss function, non-local feature-based loss function, and joint training of image synthesis network together with spatial transformer (STN)-based registration networks with specially designed regularization. Efficacy of our method is validated in a controlled synthetic scenario, as well as public dataset with such corruptions. This work introduces a new framework that may be applicable to challenging scenarios and other more difficult datasets.

Microstructure-Informed Myelin Mapping (MIMM) from routine multi-echo gradient echo data using multiscale physics modeling of iron and myelin effects and QSM

PURPOSE

Myelin quantification is used in the study of demyelination in neurodegenerative diseases. A novel noninvasive MRI method, Microstructure-Informed Myelin Mapping (MIMM), is proposed to quantify the myelin volume fraction (MVF) from a routine multi-gradient echo sequence (mGRE) using a multiscale biophysical signal model of the effects of microstructural myelin and iron.

THEORY AND METHODS

In MIMM, the effects of myelin are modeled based on the Hollow Cylinder Fiber Model accounting for anisotropy, while iron is considered as an isotropic paramagnetic point source. This model is used to create a dictionary of mGRE magnitude signal evolution and total voxel susceptibility using finite elements of size 0.2 μm. Next, voxel-by-voxel stochastic matching pursuit between acquired mGRE data (magnitude+QSM) and the pre-computed dictionary generates quantitative MVF and iron susceptibility maps. Dictionary matching was evaluated under three conditions: (1) without fiber orientation (basic), (2) with fiber orientation obtained using DTI, and (3) with fiber orientation obtained using an atlas (atlas). MIMM was compared with the three-pool complex fitting (3PCF) using T2-relaxometry myelin water fraction (MWF) map as reference.

RESULTS

The DTI MIMM and atlas MIMM approaches were equally effective in reducing the overestimation of MVF in certain white matter tracts observed in the basic MIMM approach, and they both showed good agreement with T2-relaxometry MWF. MIMM MVF reduced myelin overestimation of globus pallidus observed in 3PCF MWF.

CONCLUSION

MIMM processing of mGRE data can provide MVF maps from routine clinical scans without requiring special sequences.

DLPVI: Deep learning framework integrating projection, view-by-view backprojection, and image domains for high- and ultra-sparse-view CBCT reconstruction

This study proposes a deep learning framework, DLPVI, which integrates projection, view-by-view backprojection (VVBP), and image domains to improve the quality of high-sparse-view and ultra-sparse-view cone-beam computed tomography (CBCT) images. The DLPVI comprises a projection domain sub-framework, a VVBP domain sub-framework, and a Transformer-based image domain model. First, full-view projections were restored from sparse-view projections via the projection domain sub-framework, then filtered and view-by-view backprojected to generate VVBP raw data. Next, the VVBP raw data was processed by the VVBP domain sub-framework to suppress residual noise and artifacts, and produce CBCT axial images. Finally, the axial images were further refined using the image domain model. The DLPVI was trained, validated, and tested on CBCT data from 163, 30, and 30 real patients respectively. Quantitative metrics including root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) were calculated to evaluate the method performance. The DLPVI was compared with 15 state-of-the-art (SOTA) methods, including 2 projection domain models, 10 image domain models, and 3 projection-image dual-domain frameworks, on 1/8 high-sparse-view and 1/16 ultra-sparse-view reconstruction tasks. Statistical analysis was conducted using the Kruskal-Wallis test, followed by the post-hoc Dunn's test. Experimental results demonstrated that the DLPVI outperformed all 15 SOTA methods for both tasks, with statistically significant improvements (p < 0.05 in Kruskal-Wallis test and p < 0.05/15 in Dunn's test). The proposed DLPVI effectively improves the quality of high- and ultra-sparse-view CBCT images.

DDoCT: Morphology preserved dual-domain joint optimization for fast sparse-view low-dose CT imaging

Computed tomography (CT) is continuously becoming a valuable diagnostic technique in clinical practice. However, the radiation dose exposure in the CT scanning process is a public health concern. Within medical diagnoses, mitigating the radiation risk to patients can be achieved by reducing the radiation dose through adjustments in tube current and/or the number of projections. Nevertheless, dose reduction introduces additional noise and artifacts, which have extremely detrimental effects on clinical diagnosis and subsequent analysis. In recent years, the feasibility of applying deep learning methods to low-dose CT (LDCT) imaging has been demonstrated, leading to significant achievements. This article proposes a dual-domain joint optimization LDCT imaging framework (termed DDoCT) which uses noisy sparse-view projection to reconstruct high-performance CT images with joint optimization in projection and image domains. The proposed method not only addresses the noise introduced by reducing tube current, but also pays special attention to issues such as streak artifacts caused by a reduction in the number of projections, enhancing the applicability of DDoCT in practical fast LDCT imaging environments. Experimental results have demonstrated that DDoCT has made significant progress in reducing noise and streak artifacts and enhancing the contrast and clarity of the images.

Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup's findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence-focused research and applications in anesthesiology. Anesthesiology's progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup's recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence-based tools for perioperative care.

Improved Birthweight Prediction With Feature-Wise Linear Modulation, GRU, and Attention Mechanism in Ultrasound Data

OBJECTIVES

Birthweight prediction in fetal development presents a challenge in direct measurement and often depends on empirical formulas based on the clinician's experience. Existing methods suffer from low accuracy and high execution times, limiting their clinical effectiveness. This study aims to introduce a novel approach integrating feature-wise linear modulation (FiLM), gated recurrent unit (GRU), and Attention network to improve birthweight prediction using ultrasound data.

METHODS

The proposed method utilizes FiLM for adaptive modulation, dynamically adjusting layer activations based on input specifics for enhanced information extraction. GRU is employed to capture sequential dependencies, recognizing the evolving maternal and fetal parameters during pregnancy. The Attention network selectively focuses on crucial parameters, dynamically adjusting feature weights for accurate predictions. The study evaluates classification accuracies for three groups: appropriate-for-gestational-age, large-for-gestational-age, and small-for-gestational-age (SGA). Prediction errors are minimized by optimizing parameters and using mean squared error as the loss function. Experimental evaluations are performed using multiple metrics.

RESULTS

The proposed strategy attains a high prediction accuracy of 98.8%, outperforming existing methods such as ensemble transfer learning model (83.5%), BabyNet++ (91.7%), bi-directional LSTM with CNN and a hybrid whale with oppositional fruit fly optimization (89.2%), linear regression-random forest-artificial neural network (79.5%), and Attention MFP-Unet (93.6%). The integrated network provides advanced insights into birthweight dynamics, enhancing both interpretability and accuracy.

CONCLUSIONS

The findings of this study are vital for birthweight prediction, clinical delivery guideline development, and implementation of decision-making. The proposed approach supports clinicians in making informed decisions during obstetric examinations and assists pregnant women in weight management, showcasing significant advancements in maternal healthcare.

Data-Driven Volumetric Computed Tomography Image Generation From Surface Structures Using a Patient-Specific Deep Leaning Model

PURPOSE

Optical surface imaging presents radiation-dose-free and noninvasive approaches for image guided radiation therapy, allowing continuous monitoring during treatment delivery. However, it falls short in cases where correlation of motion between body surface and internal tumor is complex, limiting the use of purely surface guided surrogates for tumor tracking. Relying solely on surface guided radiation therapy (SGRT) may not ensure accurate intrafractional monitoring. This work aims to develop a data-driven framework, mitigating the limitations of SGRT in lung cancer radiation therapy by reconstructing volumetric computed tomography (CT) images from surface images.

METHODS AND MATERIALS

We conducted a retrospective analysis involving 50 patients with lung cancer who underwent radiation therapy and had 10-phase 4-dimensional CT (4DCT) scans during their treatment simulation. For each patient, we used 9 phases of 4DCT images for patient-specific model training and validation, reserving 1 phase for testing purposes. Our approach employed a surface-to-volume image synthesis framework, harnessing cycle-consistency generative adversarial networks to transform surface images into volumetric representations. The framework was extensively validated using an additional 6-patient cohort with resimulated 4DCT.

RESULTS

The proposed technique has produced accurate volumetric CT images from the patient's body surface. In comparison with the ground truth CT images, those generated synthetically by the proposed method exhibited the gross tumor volume center of mass difference of 1.72 ± 0.87 mm, the overall mean absolute error of 36.2 ± 7.0 HU, structural similarity index measure of 0.94 ± 0.02, and Dice score coefficient of 0.81 ± 0.07. Furthermore, the robustness of the proposed framework was found to be linked to respiratory motion.

CONCLUSIONS

The proposed approach provides a novel solution to overcome the limitation of SGRT for lung cancer radiation therapy, which can potentially enable real-time volumetric imaging during radiation treatment delivery for accurate tumor tracking without radiation-induced risk. This data-driven framework offers a comprehensive solution to tackle motion management in radiation therapy, without necessitating the rigid application of first principles modeling for organ motion.

A dual-stage framework for segmentation of the brain anatomical regions with high accuracy

OBJECTIVE

This study presents a novel deep learning-based framework for precise brain MR region segmentation, aiming to identify the location and the shape details of different anatomical structures within the brain.

MATERIALS AND METHODS

The approach uses a two-stage 3D segmentation technique on a dataset of adult subjects, including cognitively normal participants and individuals with cognitive decline. Stage 1 employs a 3D U-Net to segment 13 brain regions, achieving a mean DSC of 0.904 ± 0.060 and a mean HD95 of 1.52 ± 1.53 mm (a mean DSC of 0.885 ± 0.065 and a mean HD95 of 1.57 ± 1.35 mm for smaller parts). For challenging regions like hippocampus, thalamus, cerebrospinal fluid, amygdala, basal ganglia, and corpus callosum, Stage 2 with SegResNet refines segmentation, improving mean DSC to 0.921 ± 0.048 and HD95 to 1.17 ± 0.69 mm.

RESULTS

Statistical analysis reveals significant improvements (p-value < 0.001) for these regions, with DSC increases ranging from 1.3 to 3.2% and HD95 reductions of 0.06-0.33 mm. Comparisons with recent studies highlight the superior performance of the performed method.

DISCUSSION

The inclusion of a second stage for refining the segmentation of smaller regions demonstrates substantial improvements, establishing the framework's potential for precise and reliable brain region segmentation across diverse cognitive groups.

Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging

PURPOSE

The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit.

MATERIALS AND METHODS

In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. One solid water and 4 solid iodine inserts with known concentrations (2, 5, 10, and 15 mg I/mL) were inserted into phantoms. Each phantom setting was scanned on a PCCT system (Siemens Alpha) with 4 kV options (70 and 90 kV under Quantum Mode, 120 and 140 kV under QuantumPlus Mode) and clinical dual-source (3.0 pitch) protocol. For each phantom setting, radiation dose (CTDI vol ) was determined by clinical dose settings and matched for all kV acquisitions. Sixty percent clinical dose images were also acquired. Reconstruction was matched across all acquisitions using Qr40 kernel and QIR level 3. Virtual monoenergetic images (VMIs) between 40 and 80 keV with 10 keV interval were generated on the scanner. Low-energy and high-energy images were reconstructed from each scan and subsequently used to generate an iodine map (IM) using an image-based 2-material decomposition method. Image noise of VMIs from each kV acquisition was calculated and compared between kV options. Absolute percent error (APE) of iodine CT number accuracy in VMIs was calculated and compared. Root mean square error (RMSE) and bias of iodine quantification from IMs were compared across kV options.

RESULTS

At the newborn size and 50 keV VMI, noise is lower at low kV acquisitions (70 kV: 10.5 HU, 90 kV: 10.4 HU), compared with high kV acquisitions (120 kV: 13.8 HU, 140 kV: 13.9 HU). At the newborn size and 70 keV VMI, the image noise from different kV options is comparable (9.4 HU for 70 kV, 8.9 HU for 90 kV, 9.7 HU for 120 kV, 10.2 HU for 140 kV). For APE of VMI, high kV (120 or 140 kV) performed overall better than low kV (70 or 90 kV). At the 5-year-old size, APE of 90 kV (median: 3.6%) is significantly higher ( P < 0.001, Kruskal-Wallis rank sum test with Bonferroni correction) than 140 kV (median: 1.6%). At adult size, APE of 70 kV (median: 18.0%) is significantly higher ( P < 0.0001, Kruskal-Wallis rank sum test with Bonferroni correction) than 120 kV (median: 1.4%) or 140 kV (median: 0.8%). The high kV also demonstrated lower RMSE and bias than the low kV across all controlled conditions. At 10-year-old size, RMSE and bias of 120 kV are 1.4 and 0.2 mg I/mL, whereas those from 70 kV are 1.9 and 0.8 mg I/mL.

CONCLUSIONS

The high kV options (120 or 140 kV) on the PCCT unit demonstrated overall better performance than the low kV options (70 or 90 kV), in terms of image quality of VMIs and IMs. Our results recommend the use of high kV for general body imaging on the PCCT.

Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography

OBJECTIVE

To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients.

MATERIALS AND METHODS

From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m2 were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups.

RESULTS

The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001).

CONCLUSION

DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients.

CLINICAL RELEVANCE STATEMENT

Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality.

KEY POINTS

Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.

Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque

Vulnerable atherosclerotic plaque is a type of plaque that poses a significant risk of high mortality in patients with cardiovascular disease. Ultrasound has long been used for carotid atherosclerosis screening and plaque assessment due to its safety, low cost and non-invasive nature. However, conventional ultrasound techniques have limitations such as subjectivity, operator dependence, and low inter-observer agreement, leading to inconsistent and possibly inaccurate diagnoses. In recent years, a promising approach to address these limitations has emerged through the integration of artificial intelligence (AI) into ultrasound imaging. It was found that by training AI algorithms with large data sets of ultrasound images, the technology can learn to recognize specific characteristics and patterns associated with vulnerable plaques. This allows for a more objective and consistent assessment, leading to improved diagnostic accuracy. This article reviews the application of AI in the field of diagnostic ultrasound, with a particular focus on carotid vulnerable plaques, and discusses the limitations and prospects of AI-assisted ultrasound. This review also provides a deeper understanding of the role of AI in diagnostic ultrasound and promotes more research in the field.

A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net

Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to traditional mammography and has recently gained considerable attention. However, reviewing hundreds of ABUS slices imposes a high workload on radiologists, increasing review time and potentially leading to diagnostic errors. Consequently, there is a strong need for efficient computer-aided detection, CADe, systems. In recent years, researchers have proposed deep learning-based CADe systems to enhance mass detection accuracy. However, these methods are highly dependent on the number of training samples and often struggle to balance detection accuracy with the false positive rate. To reduce the workload for radiologists and achieve high detection sensitivities with low false positive rates, this study introduces a novel CADe system based on a self-supervised framework that leverages unannotated ABUS datasets to improve detection results. The proposed framework is integrated into an innovative 3-D convolutional neural network called DATTR2U-Net, which employs a multi-task learning approach to simultaneously train inpainting and denoising pretext tasks. A fully convolutional network is then attached to the DATTR2U-Net for the detection task. The proposed method is validated on the TDSCABUS public dataset, demonstrating promising detection results with a recall of 0.7963 and a false positive rate of 5.67 per volume that signifies its potential to improve detection accuracy while reducing workload for radiologists. The code is available at: github.com/Pooryamn/SSL_ABUS.

Evaluation of the anticarcinogenic effects of Rutin on brain tissue in mice with Ehrlich ascites carcinoma by micro-computed tomography and histological methods

BACKGROUND

Studies for new treatment strategies on cancer continue, and new searches continue in the diagnosis and evaluation of cancer. This study examined the possible anticarcinogenic effect of Rutin on the brain tissues of male mice with Ehrlich ascites carcinoma (EAC).

MATERIAL AND METHODS

We used micro-computed tomography (micro-CT) and histologically Hematoxylin&Eosin (H&E) staining methods for evaluation.

RESULTS

In the evaluation results, we saw a significant decrease in the brain volume of the tumor group to the control group. The difference in volume between the Rutin treatment group and the control group was not significant. In the brain tissues of the tumor group, numerous degenerated neurons characterized by pericellular/perivascular space expansion, cell swelling, or expansion were detected in the cortex and hippocampus regions. We showed a reduction in the damage rate in the Rutin treated group.

CONCLUSION

As a result, Rutin was found to have an anticarcinogenic effect. In addition to the classical histological evaluation, we used a newer method, micro-CT, in our study. We believe that this study has important results both in terms of its originality and adding new information to the literature.

Proton FLASH Irradiation Using a Synchrotron Accelerator: Differences by Irradiation Positions

PURPOSE

To establish an ultra-high dose-rate (UHDR) radiation system using a synchrotron proton beam accelerator and to compare the effects by irradiation positions on cultured cells and chick embryos.

METHODS AND MATERIALS

Protons for UHDR were obtained by applying high-frequency power at much higher levels than usual to extract all protons within approximately 50 ms. Subsequently, monitoring with a Faraday cup was performed immediately after synchrotron extraction and the waveform was adjusted accordingly. Four cultured tumor lines, 2 normal cell lines, and chick embryos were used. UHDR radiation therapy (UHDR-RT) at 6 to 18 Gy (200-300 Gy/s, single exposure) and conventional dose-rate radiation therapy (Conv-RT) at 6 to 18 Gy (3 Gy/s) were administered to the 1-cm spread-out Bragg peak (SOBP) and the plateau region preceding SOBP. After irradiation, disparities in cell growth rates and cell cycle progression were assessed, and cell survival was evaluated via colony assay. Chick embryos were also examined for survival.

RESULTS

UHDR-RT was achieved at a range of 40 to 800 Gy/s, encompassing both plateau and peak phases. In vitro studies demonstrated similar cell-killing effects between UHDR-RT and Conv-RT in cancer cells. Significant apoptotic effects and G2 arrest were observed during the cell cycle under peak UHDR-RT conditions. The FLASH effect was not observed in normal single cells under normal atmospheric conditions. Stronger cell-killing effects were noted in V79 spheroids exposed to peak UHDR-RT than peak Conv-RT. Moreover, in chick embryos, an increase in survival rate, indicative of the FLASH effect, was observed.

CONCLUSIONS

The FLASH effect was also achieved with UHDR-RT using a synchrotron proton beam accelerator in chick embryos. The cell-killing effects in cancer cells were higher with peak UHDR-RT that may be due to the higher linear energy transfer at the SOBP.

Reirradiation for Locally Recurrent Head and Neck Cancer: State-of-the-Art and Future Directions

Reirradiation of the head and neck presents one of the most complex and challenging scenarios faced by (for) clinicians due to the narrow therapeutic window. Its use is increasing in clinical practice, often guided by empirical and pragmatic approaches due to the limited availability of high-level evidence from randomized clinical trials. Successful reirradiation requires a precise balance between tumor control probability (TCP) and normal tissue complication probability (NTCP). Advances in radiation technologies, including intensity-modulated radiation therapy (IMRT), proton beam therapy (PBT), and stereotactic body radiation therapy (SBRT), have enabled more precise high-dose delivery, potentially improving dose distribution and reducing severe toxicity. This review explores current state-of-the-art approaches to reirradiating recurrent head and neck cancer, focusing on modern reirradiation techniques and critically assessing the literature on their clinical application, integration with systemic therapy, and future directions. It also addresses key practical challenges related to patient selection and toxicity/risk management, offering a comprehensive overview of the evolving treatment landscape and highlighting some of the most complex issues clinicians face in reirradiation.

Development and clinical evaluation of an AI-assisted respiratory state classification system for chest X-rays: A BMI-Specific approach

PURPOSE

In this study, we aimed to develop and clinically evaluate an artificial intelligence (AI)-assisted support system for determining inhalation and exhalation states on chest X-ray images, focusing on the specific challenge of respiratory state determination.

METHODS

We developed a body mass index (BMI)-specific approach for respiratory state classification in chest X-rays using separate models for normal and obesity groups. Feature extraction was performed using four pre-trained networks (EfficientNet B0, GoogleNet, Xception, and VGG16) combined with Naive Bayes classification. A database of 3200 chest X-ray images from 1600 patients, labeled for respiratory states using temporal subtraction techniques, was utilized. The system's clinical utility was assessed through an observational study involving eight radiological technologists with varying experience levels.

RESULTS

The approach combining EfficientNet B0 late-layer with Naive Bayes classification and GoogleNet's end-to-end model demonstrated the highest performance. The support system significantly improved the area under the curve from 0.728 to 0.796 in the normal BMI group and from 0.752 to 0.817 in the obesity group (p < 0.05), showing particular effectiveness in classifying exhalation states in obese patients.

CONCLUSION

The developed AI-assisted support system enhances radiological technologists' ability to determine respiratory states across varying levels of experience, particularly in challenging cases involving obese patients. This system contributes to improving image quality assessment and workflow efficiency by potentially reducing unnecessary re-imaging.

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals

BACKGROUND AND OBJECTIVE

Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain signals. Neurodegenerative disorders like Alzheimer's Disease (AD) and Frontotemporal Dementia (FD) are increasingly prevalent due to age-related brain volume reduction. Despite advances, existing models often lack comprehensive multi-class classification capabilities and are computationally expensive. This study addresses these gaps by proposing EEGConvNeXt, a novel convolutional neural network (CNN) model for detecting AD and FD using electroencephalogram (EEG) signals with high accuracy.

MATERIALS AND METHOD

In this research, we employ an open-access EEG signal public dataset containing three distinct classes: AD, FD, and control subjects. We then constructed a newly proposed EEGConvNeXt model comprised of a 2-dimensional CNN algorithm that firstly converts the EEG signals into power spectrogram-based images. Secondly, these images were used as input for the proposed EEGConvNeXt model for automated classification of AD, FD, and a control outcome. The proposed EEGConvNeXt model is therefore a lightweight model that contributes to a new image classification CNN structure based on the transformer model with four primary stages: a stem, a main model, downsampling, and an output stem.

RESULTS

The EEGConvNeXt model achieved a classification accuracy of ∼95.70% for three-class detection (AD, FD, and control), validated using a hold-out strategy. Binary classification cases, such as AD versus FD and FD versus control, achieved accuracies exceeding 98%, demonstrating the model's robustness across scenarios.

CONCLUSIONS

The proposed EEGConvNeXt model demonstrates high classification performance with a lightweight architecture suitable for deployment in resource-constrained settings. While the study establishes a novel framework for AD and FD detection, limitations include reliance on a relatively small dataset and the need for further validation on diverse populations. Future research should focus on expanding datasets, optimizing architecture, and exploring additional neurological disorders to enhance the model's utility in clinical applications.

Preliminary experiments to produce lutetium-177 in the TRR-1/M1 Thai research reactor

Lutetium-177 has emerged as a highly efficient radionuclide for medical applications, particularly in the field of targeted radionuclide therapy. Its production has been increasingly optimized through neutron activation techniques, which offer distinct advantages over alternative methods. Utilizing the TRR-1/M1 research reactor, which has been in operation for nearly six decades, provides a strategic opportunity for advancing domestic radioisotope production, thereby supporting the medical sector in Thailand. The TRR-1/M1 reactor, despite its operational age, continues to exhibit considerable potential for contributing to medical research and radioisotope development in Thailand. Preliminary experimental results, conducted at a flux of 1.42 × 1012 n/cm2/s demonstrated promising outcomes, even under operational constraints such as fuel management limitations. Notably, the direct neutron activation of natural lutetium oxide notably yielded a specific activity of 177Lu at 10.92 GBq/g (295.06 mCi/g) with a production yield of 44.8%, with projections reaching 222 GBq/g (6 Ci/g) after 40 days of neutron irradiation. In comparison, the indirect method, using natural ytterbium oxide as a precursor, achieved a maximum specific activity of 177Lu at 6.6 MBq/g (180.3 μCi/g) with a yield of 37.8% of a theoretical maximum of 17.6 MBq/g (476 μCi/g) after only 10 h of neutron activation. These results highlight the feasibility and promise of 177Lu radioisotope production in Thailand.

The history of ankylosing spondylitis/axial spondyloarthritis - what is the driving force of new knowledge?

The history of (axial) spondyloarthritis has started several centuries ago. Since the end of the 19th century major achievements have been made. This historical review tries to show how closely the advances in clinical medicine in rheumatology have been related to advances made in basic sciences.

Understanding the Importance of Blood-Brain Barrier Alterations in Brain Arteriovenous Malformations and Implications for Treatment: A Dynamic Contrast-Enhanced-MRI-Based Prospective Study

BACKGROUND AND OBJECTIVES

The major clinical implication of brain arteriovenous malformations (bAVMs) is spontaneous intracranial hemorrhage. There is a growing body of experimental evidence proving that inflammation and blood-brain barrier (BBB) dysfunction are involved in both the clinical course of the disease and the risk of bleeding. However, how bAVM treatment affects perilesional BBB disturbances is yet unclear.

METHODS

We assessed the permeability changes of the BBB using dynamic contrast-enhanced MRI (DCE-MRI) in a series of bAVMs (n = 35), before and at a mean of 5 (±2) days after treatment. A set of cerebral cavernous malformations (CCMs) (n = 16) was used as a control group for the assessment of the surgical-related collateral changes. The extended Tofts pharmacokinetic model was used to extract permeability (K trans ) values in the lesional, perilesional, and normal brain tissues.

RESULTS

In patients with bAVM, the permeability of BBB was higher in the perilesional of bAVM tissue compared with the rest of the brain parenchyma (mean K trans 0.145 ± 0.104 vs 0.084 ± 0.035, P = .004). Meanwhile, no significant changes were seen in the perilesional brain of CCM cases (mean K trans 0.055 ± 0.056 vs 0.061 ± 0.026, P = .96). A significant decrease in BBB permeability was evident in the perilesional area of bAVM after surgical resection (mean K trans 0.145 ± 0.104 vs 0.096 ± 0.059, P = .037). This benefit in BBB permeability reduction after surgery seemed to surpass the relative increase in permeability inherent to the surgical manipulation.

CONCLUSION

In contrast to CCMs, BBB permeability in patients with bAVM is increased in the perilesional parenchyma, as assessed using DCE-MRI. However, bAVM surgical resection seems to reduce BBB permeability in the perilesional tissue. No evidence of the so-called breakthrough phenomenon was detected in our series. DCE-MRI could become a valuable tool to follow the longitudinal course of BBB damage throughout the natural history and clinical course of bAVMs.

Deep learning radiomics nomograms predict Isocitrate dehydrogenase (IDH) genotypes in brain glioma: A multicenter study

PURPOSE

To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype.

METHODS

A total of 402 glioma patients from two independent centers were retrospectively included, and the data from center I was randomly divided into a training cohort (n = 239) and an internal validation cohort (n = 103) on a 7:3 basis. Center II served as an independent external validation cohort (n = 60). We developed a DLRN for IDH classification of gliomas based on T2 images. This hybrid model integrates deep learning features, radiomics features, and clinical features most relevant to IDH genotypes and finally classifies them using multivariate logistic regression analysis. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the performance of the model and applied the DLRN score to the survival analysis of some of the follow-up glioma patients.

RESULTS

The proposed model had an area under the curve (AUC) of 0.98 in an externally validated cohort, and DLRN scores were significantly associated with the overall survival of glioma patients.

CONCLUSIONS

Deep learning radiomics nomograms performed well in non-invasively predicting IDH mutation status in gliomas, assisting stratified management and targeted therapy for glioma patients.

Feature-targeted deep learning framework for pulmonary tumorous Cone-beam CT (CBCT) enhancement with multi-task customized perceptual loss and feature-guided CycleGAN

Thoracic Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for lung cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in loss of lung anatomy which contains crucial pulmonary tumorous and functional information. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details containing crucial tumorous information due to lack of targeted guidance. To address this issue, we propose a novel feature-targeted deep learning framework which generates ultra-quality pulmonary imaging from CBCT of lung cancer patients via a multi-task customized feature-to-feature perceptual loss function and a feature-guided CycleGAN. The framework comprises two main components: a multi-task learning feature-selection network (MTFS-Net) for building up a customized feature-to-feature perceptual loss function (CFP-loss); and a feature-guided CycleGan network. Our experiments showed that the proposed framework can generate synthesized CT (sCT) images for the lung that achieved a high similarity to CT images, with an average SSIM index of 0.9747 and an average PSNR index of 38.5995 globally, and an average Pearman's coefficient of 0.8929 within the tumor region on multi-institutional datasets. The sCT images also achieved visually pleasing performance with effective artifacts suppression, noise reduction, and distinctive anatomical details preservation. Functional imaging tests further demonstrated the pulmonary texture correction performance of the sCT images, and the similarity of the functional imaging generated from sCT and CT images has reached an average DSC value of 0.9147, SCC value of 0.9615 and R value of 0.9661. Comparison experiments with pixel-to-pixel loss also showed that the proposed perceptual loss significantly enhances the performance of involved generative models. Our experiment results indicate that the proposed framework outperforms the state-of-the-art models for pulmonary CBCT enhancement. This framework holds great promise for generating high-quality pulmonary imaging from CBCT that is suitable for supporting further analysis of lung cancer treatment.

Synthetic CT generation using Zero TE MR for head-and-neck radiotherapy

BACKGROUND AND PURPOSE

MRI-based synthetic CTs (synCTs) show promise to replace planning CT scans in various anatomical regions. However, the head-and-neck region remains challenging because of patient-specific air, bone and soft tissues interfaces and oropharynx cavities. Zero-Echo-Time (ZTE) MRI can be fast and silent, accurately discriminate bone and air, and could potentially lead to high dose calculation accuracy, but is relatively unexplored for the head-and-neck region. Here, we prospectively evaluated the dosimetric accuracy of a novel, fast ZTE sequence for synCT generation.

MATERIALS AND METHODS

The method was developed based on 127 patients and validated in an independent test (n = 17). synCTs were generated using a multi-task 2D U-net from ZTE MRIs (scanning time: 2:33 min (normal scan) or 56 s (accelerated scan)). Clinical treatment plans were recalculated on the synCT. The Hounsfield Units (HU) and dose-volume-histogram metrics were compared between the synCT and CT. Subsequently, synthetic treatment plans were generated to systematically assess dosimetry accuracy in different anatomical regions using dose-volume-histogram metrics.

RESULTS

The mean absolute error between the synCT and CT was 94 ± 11 HU inside the patient contour. For the clinical plans, 98.8% of PTV metrics deviated less than 2% between synCT and CT and all OAR metrics deviated less than 1 Gy. The synthetic plans showed larger dose differences depending on the location of the PTV.

CONCLUSIONS

Excellent dose agreement was found based on clinical plans between the CT and a ZTE-MR-based synCT in the head-and-neck region. Synthetic plans are an important addition to clinical plans to evaluate the dosimetric accuracy of synCT scans.

An improved feature extraction algorithm for robust Swin Transformer model in high-dimensional medical image analysis

The Swin Transformer is recently developed transformer architecture with promising results in various computer vision tasks. Medical image analysis is a complex and critical task that requires high dimensional feature extraction. The significant challenge in medical image analysis is the limited availability of annotated data for training. It has been proposed that a multitask learning scheme be put in place. Swin Transformer can be trained for all the medical image analysis tasks simultaneously so that general features can be learned from the model and used for other new tasks and data. In most cases, the medical images have poor properties such as noise, artifacts, and low contrast. The Swin Transformer presents an adaptive attention mechanism: its attention weights are learned dynamically according to input quality. It could selectively focus on essential regions in an image while discarding noise or irrelevant information. Medical images may have very complex anatomical structures. In this sense, an iterative transformer encoder is proposed to form a hierarchical structure with gradually decreasing dimensionality between layers-so that the attention mechanism is applied at different scales, capturing local and long-range relationships between image patches. This research proposes a robust Swin Transformer architecture for high-dimensional feature extraction in medical images. The proposed algorithm reached 80.76 % accuracy, 80.28 % precision, 78.04 % recall, 76.46 % F1-Score and 73.46 % critical success index.

Structure/function alterations and related neurotransmitter activity maps in high myopia patients

This study explored the relationship between brain structure and functional pattern as well as the potential neurotransmitter activity alterations in patients with high myopia (HM). Total 33 HM patients and 31 healthy controls were included. Gray matter volume (GMV) was employed to represent brain structure indicator, and amplitude of low-frequency fluctuations (ALFF) was used as an indicator of function. Use the data fusion method of parallel independent component analysis (ICA) to identify the independent components of two patterns and analyze the relationship between them. The spatial correlations between the altered ICA value and neurotransmitter maps were calculated. The results show that there is a significantly related sets of independent components (GMV_IC5 and ALFF_IC4) between the HM and healthy control groups in terms of structure and function. The structural components mainly include the temporal lobe, frontal lobe, cingulate gyrus, and occipital lobe; the functional components are primarily composed of the precuneus, occipital lobe, temporal lobe, and lingual Gyrus. The change value of GMV_IC5 is significantly correlated with serotonin 5-hydroxytryptamine receptor (subtype 1a, 1b and 2a), dopamine D1, gamma-aminobutyric acid (GABAa), and metabotropic glutamate receptor 5; while, the altered ALFF in ALFF_IC4 is significantly correlated with serotonin 5-hydroxytryptamine receptor subtype 2a, dopamine D1, and GABAa. Research results suggest the structural and functional components that change together reflect the association between the visual brain regions and the temporal-frontal areas in HM, as well as their correlation with serotonin receptors, dopamine, and the GABA neurotransmitter system.

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learning (ML) advancements have revolutionized its diagnosis and treatment approaches. Although, the researchers are continuously developing automated ADHD diagnostic tools, there is no reliable ML-based diagnostic system for clinicians. Thus, the study aims to systematically review ML and DL-based approaches for ADHD diagnosis, leveraging brain data from magnetic resonance imaging (MRI) and electroencephalogram (EEG) data. A methodical review for the period 2016 to 2022 is conducted by following the PRISMA guidelines. Four reputable repositories, namely PubMed, IEEE, ScienceDirect, and Springer are searched for the related literature on ADHD diagnosis using MRI/EEG data. 87 studies are selected after screening abstracts of the papers. We critically conducted an analysis of these studies by examining various aspects related to training ML/DL-models, including diverse datasets, hyperparameter tuning, overfitting, and interpretability. The quality and risk assessment is conducted using the QUADAS2 tool to determine the bias due to patient selection, index test, reference standard, and flow and timing. Our rigours analysis observed significant diversity in dataset acquisition and its size, feature extraction and selection techniques, validation strategies and classifier choices. Our findings emphasize the need for generalizability, transparency, interpretability, and reproducibility in future research. The challenges and potential solutions associated with integrating diagnostic models into clinical settings are also discussed. The identified research gaps will guide researchers in developing a reliable ADHD diagnostic system that addresses the associated challenges.

Enhancing Ion Emission: Insights from Molecular Dynamics and Monte Carlo Simulations

Gas cluster ion beam (GCIB) guns have found several applications in science and industry, such as surface smoothing or depth profiling and surface analysis in conjunction with secondary ion mass spectrometry (SIMS). The former application is severely hindered by the low amount of ejected secondary ions, which can be boosted by more than an order of magnitude by properly selecting the size of cluster projectiles and changing their constituent particles from argon to water. The mechanism of this phenomenon is still unknown. By combining molecular dynamics (MD) and Monte Carlo (MC) simulations with experimental results, we posit that the increase in ion yield can be attributed to proton transfer in long-lived complexes of sample molecules and hydronium (H3O+) ion from the projectile. The number of molecule-water complexes formed in simulations is directly proportional to the experimental signal intensity, with a small deviation for projectiles containing more than 7000 water molecules.