Monte Carlo simulations of microdosimetry and radiolytic species production at long time post proton irradiation using GATE and Geant4-DNA

BACKGROUND

Radiobiological effectiveness of radiation in cancer treatment can be studied at different scales (molecular till organ scale) and different time post irradiation. The production of free radicals and reactive oxygen species during water radiolysis is particularly relevant to understand the fundamental mechanisms playing a role in observed biological outcomes. The development and validation of Monte Carlo tools integrating the simulation of physical, physico-chemical and chemical stages after radiation is very important to maintain with experiments.

PURPOSE

Therefore, in this study, we propose to validate a new Geant4-DNA chemistry module through the simulation of water radiolysis and Fricke dosimetry experiments on a proton preclinical beam line.

MATERIAL AND METHODS

In this study, we used the GATE Monte Carlo simulation platform (version 9.3) to simulate a 67.5 MeV proton beam produced with the ARRONAX isochronous cyclotron (IBA Cyclone 70XP) at conventional dose rate (0.2 Gy/s) to simulate the irradiation of ultra-pure liquid water samples and Fricke dosimeter. We compared the depth dose profile with measurements performed with a plane parallel Advanced PTW 34045 Markus ionization chamber. Then, a new Geant4-DNA chemistry application proposed from Geant4 version 11.2 has been used to assess the evolution ofHO • ${\mathrm{HO}}^ \bullet $ ,e aq - ${\mathrm{e}}_{{\mathrm{aq}}}^ - $ ,H 3 O + ${{\mathrm{H}}}_3{{\mathrm{O}}}^ + $ ,H 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ ,H 2 ${{\mathrm{H}}}_2$ ,HO 2 • ${\mathrm{HO}}_2^ \bullet $ ,HO 2 - , O 2 • - ${\mathrm{HO}}_2^ - ,{\mathrm{\ O}}_2^{ \bullet - }$ andHO - ${\mathrm{HO}}^ - $ reactive species along time until 1-h post-irradiation. In particular, the effect of oxygen and pH has been investigated through comparisons with experimental measurements of radiolytic yields forH 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ and Fe3+.

RESULTS

GATE simulations reproduced, within 4%, the depth dose profile in liquid water. With Geant4-DNA, we were able to reproduce experimentalH 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ radiolytic yields 1-h post-irradiation in aerated and deaerated conditions, showing the impact of small changes in oxygen concentrations on species evolution along time. For the Fricke dosimeter, simulated G(Fe3+) is 15.97 ± 0.2 molecules/100 eV which is 11% higher than the measured value (14.4 ± 04 molecules/100 eV).

CONCLUSIONS

These results aim to be consolidated by new comparisons involving other radiolytic species, such ase aq - ${\mathrm{e}}_{{\mathrm{aq}}}^ - $ or, O 2 • - $,{\mathrm{\ O}}_2^{ \bullet - }$ to further study the mechanisms underlying the FLASH effect observed at ultra-high dose rates (UHDR).

An updated variable RBE model for proton therapy

Objective.While a constant relative biological effectiveness (RBE) of 1.1 forms the basis for clinical proton therapy, variable RBE models are increasingly being used in plan evaluation. However, there is substantial variation across RBE models, and several newin vitrodatasets have not yet been included in the existing models. In this study, an updatedin vitroproton RBE database was collected and used to examine current RBE model assumptions, and to propose an up-to-date RBE model as a tool for evaluating RBE effects in clinical settings.Approach.A proton database (471 data points) was collected from the literature, almost twice the size of the previously largest model database. Each data point included linear-quadratic model parameters and linear energy transfer (LET). Statistical analyses were performed to test the validity of commonly applied assumptions of phenomenological RBE models, and new model functions were proposed forRBEmaxandRBEmin(RBE at the lower and upper dose limits). Previously published models were refitted to the database and compared to the new model in terms of model performance and RBE estimates.Main results.The statistical analysis indicated that the intercept of theRBEmaxfunction should be a free fitting parameter and RBE estimates were clearly higher for models with free intercept.RBEminincreased with increasing LET, while a dependency ofRBEminon the reference radiation fractionation sensitivity (α/βx) did not significantly improve model performance. Evaluating the models, the new model gave overall lowest RMSE and highest R2 score. RBE estimates in the distal part of a spread-out-Bragg-peak in water (α/βx= 2.1 Gy) were 1.24-1.51 for original models, 1.25-1.49 for refits and 1.42 for the new model.Significance.An updated RBE model based on the currently largest database among published phenomenological models was proposed. Overall, the new model showed better performance compared to refitted published RBE models.

Seracam: characterisation of a new small field of view hybrid gamma camera for nuclear medicine

BACKGROUND

Portable gamma cameras are being developed for nuclear medicine procedures such as thyroid scintigraphy. This article introduces Seracam® - a new technology that combines small field of view gamma imaging with optical imaging - and reports its performance and suitability for small organ imaging.

METHODS

The count rate capability, uniformity, spatial resolution, and sensitivity for 99mTc are reported for four integrated pinhole collimators of nominal sizes of 1 mm, 2 mm, 3 mm and 5 mm. Characterisation methodology is based on NEMA guidelines, with some adjustments necessitated by camera design. Two diagnostic scenarios - thyroid scintigraphy and gastric emptying - are simulated using clinically relevant activities and geometries to investigate application-specific performance. A qualitative assessment of the potential benefits and disadvantages of Seracam is also provided.

RESULTS

Seracam's performance across the measured characteristics is appropriate for small field of view applications in nuclear medicine. At an imaging distance of 50 mm, corresponding to a field of view of 77.6 mm × 77.6 mm, spatial resolution ranged from 4.6 mm to 26 mm and sensitivity from 3.6 cps/MBq to 52.2 cps/MBq, depending on the collimator chosen. Results from the clinical simulations were particularly promising despite the challenging scenarios investigated. The optimal collimator choice was strongly application dependent, with gastric emptying relying on the higher sensitivity of the 5 mm pinhole whereas thyroid imaging benefitted from the enhanced spatial resolution of the 1 mm pinhole. Signal to noise ratio in images was improved by pixel binning. Seracam has lower measured sensitivity when compared to a traditional large field of view gamma camera, for the simulated applications this is balanced by advantages such as high spatial resolution, portability, ease of use and real time gamma-optical image fusion and display.

CONCLUSION

The results show that Seracam has appropriate performance for small organ 99mTc imaging. The results also show that the performance of small field of view systems must be considered holistically and in clinically appropriate scenarios.

Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging

The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.

Feasibility of using micro silica bead TLDs for in-Vivo dosimetry of CT-based HDR prostate brachytherapy: An experimental and simulation study

PURPOSE

Feasibility of silica-based dosimeters for IVD of HDR prostate brachytherapy.

MATERIAL AND METHODS

Plastic dosimeter holders and a water-fillable prostate phantom were built in-house. Interstitial prostate brachytherapy and Monte Carlo simulations were performed. The treatment planning, Monte-Carlo simulation, and dosimetry results were compared.

RESULTS

The relative differences between TLD-TPS, TLD-MCNP, and TPS-MCNP were 0.2-6.9 %, 0.5-6.5 %, and 0.6-6.3 %, respectively.

CONCLUSION

Micro-silica bead dosimeters can perform offline in situ quality assurance in HDR prostate brachytherapy.

Quantitative calcium-based assessment of osteoporosis in dual-layer spectral CT

OBJECTIVES

To evaluate a novel calcium-only imaging technique (VCa) with subtracted bone marrow in osteoporosis in dual-layer CT (DLCT) compared to conventional CT images (CI) and dual-energy X-ray absorptiometry (DXA).

MATERIAL AND METHODS

Images of a multi-energy CT phantom with calcium inserts, quantitative CT calibration phantom, and of 55 patients (mean age: 64.6 ± 11.5 years) were acquired on a DLCT to evaluate bone mineral density (BMD). CI, calcium-suppressed images, and VCa were calculated. For investigating the association of VCa and CI with DXA a subsample of 30 patients (<90 days between DXA and CT) was used. Multiple regression analysis was performed to identify further factors improving the prediction of DXA BMD.

RESULTS

The calcium concentrations of the CT phantom inserts were significantly associated with CT numbers from VCa (R2 = 0.94) and from CI (R2 = 0.89-0.92). VCa showed significantly higher CT numbers than CI in the phantom (p ≤ 0.001) and clinical setting (p < 0.001). CT numbers from VCa were significantly associated with CI (R2 = 0.95, p < 0.001) and with DXA (R2 = 0.31, p = 0.007), whereas no significant association between DXA and CI was found. Prediction of DXA BMD based on CT numbers derived from VCa yielded R2 = 0.76 in multiple regression analysis. ROC for the differentiation of normal from pathologic BMD in VCa yielded an AUC of 0.7, and a cut-off value of 126HU (sensitivity: 0.90; specificity: 0.47).

CONCLUSION

VCa images showed better agreement with DXA and known calcium concentrations than CI, and could be used to estimate BMD. A VCa cut-off of 126HU could be used to identify abnormal bone mineral density.

Stereotactic body radiation therapy for prostate cancer: a dosimetric comparison of IMRT and VMAT using flattening filter and flattening filter-free beams

This retrospective study was performed to evaluate plan quality and treatment delivery parameters of stereotactic body radiation therapy (SBRT) for prostate cancer. The study utilized different isocentric modulated techniques: intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) using 6 MV flattening filter (FF) and 10 MV flattening filter-free beams (FFF). Fifteen retrospective prostate cancer patients were selected for this study. Sixty plans were created with an SBRT-prescribed dose of 36.25 Gy delivered in five fractions. Planning target volume (PTV) coverage, plan quality indices, doses delivered to organs at risk (OARs), and treatment delivery parameters were compared for all plans. It turned out that VMAT plans, particularly those using the FFF beam, provided superior target conformality and a steeper dose gradient as compared to IMRT plans. Additionally, VMAT plans showed better OARs sparing compared to IMRT plans. However, IMRT plans delivered a lower maximum dose to the target than VMAT plans. Importantly, the VMAT plans resulted in reduced treatment delivery parameters, including beam on time (BOT), monitor unit (MU), and modulation factor (MF), compared to IMRT plans. Furthermore, a statistically significant difference was observed in BOT and mean body dose between FF and FFF beams, with FFF beams showing superior performance. Considering all results, VMAT using 10 MV (FFF) is suggested for treating prostate cancer patients with SBRT. This offers the fastest delivery in addition to maintaining the highest plan quality.

An improved 3D-UNet-based brain hippocampus segmentation model based on MR images

OBJECTIVE

Accurate delineation of the hippocampal region via magnetic resonance imaging (MRI) is crucial for the prevention and early diagnosis of neurosystemic diseases. Determining how to accurately and quickly delineate the hippocampus from MRI results has become a serious issue. In this study, a pixel-level semantic segmentation method using 3D-UNet is proposed to realize the automatic segmentation of the brain hippocampus from MRI results.

METHODS

Two hundred three-dimensional T1-weighted (3D-T1) nongadolinium contrast-enhanced magnetic resonance (MR) images were acquired at Hangzhou Cancer Hospital from June 2020 to December 2022. These samples were divided into two groups, containing 175 and 25 samples. In the first group, 145 cases were used to train the hippocampus segmentation model, and the remaining 30 cases were used to fine-tune the hyperparameters of the model. Images for twenty-five patients in the second group were used as the test set to evaluate the performance of the model. The training set of images was processed via rotation, scaling, grey value augmentation and transformation with a smooth dense deformation field for both image data and ground truth labels. A filling technique was introduced into the segmentation network to establish the hippocampus segmentation model. In addition, the performance of models established with the original network, such as VNet, SegResNet, UNetR and 3D-UNet, was compared with that of models constructed by combining the filling technique with the original segmentation network.

RESULTS

The results showed that the performance of the segmentation model improved after the filling technique was introduced. Specifically, when the filling technique was introduced into VNet, SegResNet, 3D-UNet and UNetR, the segmentation performance of the models trained with an input image size of 48 × 48 × 48 improved. Among them, the 3D-UNet-based model with the filling technique achieved the best performance, with a Dice score (Dice score) of 0.7989 ± 0.0398 and a mean intersection over union (mIoU) of 0.6669 ± 0.0540, which were greater than those of the original 3D-UNet-based model. In addition, the oversegmentation ratio (OSR), average surface distance (ASD) and Hausdorff distance (HD) were 0.0666 ± 0.0351, 0.5733 ± 0.1018 and 5.1235 ± 1.4397, respectively, which were better than those of the other models. In addition, when the size of the input image was set to 48 × 48 × 48, 64 × 64 × 64 and 96 × 96 × 96, the model performance gradually improved, and the Dice scores of the proposed model reached 0.7989 ± 0.0398, 0.8371 ± 0.0254 and 0.8674 ± 0.0257, respectively. In addition, the mIoUs reached 0.6669 ± 0.0540, 0.7207 ± 0.0370 and 0.7668 ± 0.0392, respectively.

CONCLUSION

The proposed hippocampus segmentation model constructed by introducing the filling technique into a segmentation network performed better than models built solely on the original network and can improve the efficiency of diagnostic analysis.

Treatment of Darier Disease with Radiation Therapy: Case Report and Literature Review

Darier's disease (DD) is an autosomal dominant genodermatosis characterized by hyperkeratotic papules, often accompanied by scaling and crusting. Managing DD presents significant challenges due to the absence of an effective cure, with only symptom targeting treatments currently available. This study presents a case of refractory DD that showed poor response to established pharmacological treatments but demonstrated improvement with low-dose superficial X-ray radiotherapy (SRT). The radiation was delivered as a single 200 cGy treatment, which visibly improved the condition. Considering the different degrees of side effects, sequelae, and risk of developing radiation-induced cancer after exposure to moderate levels of radiation, it may be considered that we attempt to treat recalcitrant DD initially by applying a low dose of radiation in order to mitigate these undesired side effects. If larger doses or additional courses are necessary due to inadequate response, the risks and benefits must be carefully evaluated and discussed with patients.

Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours

Objectives

This study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours.

Methods

Multimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours.

Results

In terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886-0.996], and 0.956 [0.873-1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887-1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887-1.000], and 1.000 [0.999-1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867-1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990-1.000] and 1.000 [0.999-1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999-1.000] under the early fusion strategy.

Conclusion

This study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Radiomic-Based Approaches in the Multi-metastatic Setting: A Quantitative Review

Background

Radiomics traditionally focuses on analyzing a single lesion within a patient to extract tumor characteristics, yet this process may overlook inter-lesion heterogeneity, particularly in the multi-metastatic setting. There is currently no established method for combining radiomic features in such settings, leading to diverse approaches with varying strengths and limitations. Our quantitative review aims to illuminate these methodologies, assess their replicability, and guide future research toward establishing best practices, offering insights into the challenges of multi-lesion radiomic analysis across diverse datasets.

Methods

We conducted a comprehensive literature search to identify methods for integrating data from multiple lesions in radiomic analyses. We replicated these methods using either the author's code or by reconstructing them based on the information provided in the papers. Subsequently, we applied these identified methods to three distinct datasets, each depicting a different metastatic scenario.

Results

We compared ten mathematical methods for combining radiomic features across three distinct datasets, encompassing a total of 16,850 lesions in 3,930 patients. Performance of these methods was evaluated using the Cox proportional hazards model and benchmarked against univariable analysis of total tumor volume. We observed variable performance in methods across datasets. However, no single method consistently outperformed others across all datasets. Notably, while some methods surpassed total tumor volume analysis in certain datasets, others did not. Averaging methods showed higher median performance in patients with colorectal liver metastases, and in soft tissue sarcoma, concatenation of radiomic features from different lesions exhibited the highest median performance among tested methods.

Conclusions

Radiomic features can be effectively selected or combined to estimate patient-level outcomes in multi-metastatic patients, though the approach varies by metastatic setting. Our study fills a critical gap in radiomics research by examining the challenges of radiomic-based analysis in this setting. Through a comprehensive review and rigorous testing of different methods across diverse datasets representing unique metastatic scenarios, we provide valuable insights into effective radiomic analysis strategies.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to patient setup errors

PURPOSE

This study aimed to analyse correlations between planning factors including plan geometry and plan complexity with robustness to patient setup errors.

METHODS

Multiple-target brain stereotactic radiosurgery (SRS) plans were obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets with a 20 Gy prescription. Setup error was simulated using an in-house tool. Dose to targets was assessed via dose covering 99 % (D99 %) of gross tumour volume (GTV) and 98 % of planning target volume (PTV). Dose to organs at risk was assessed using volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc of brainstem. Plan complexity was assessed via edge metric, modulation complexity score, mean multi-leaf collimator (MLC) gap, mean MLC speed and plan modulation.

RESULTS

Even for small (0.5 mm/°) errors, GTV D99 % was reduced by up to 20 %. The strongest correlation was found between lower complexity plans (larger mean MLC gap and lower edge metric) and higher robustness to setup error. Lower complexity plans had 1 %-20 % fewer targets/scenarios with GTV D99 % falling below the specified tolerance threshold. These complexity metrics correlated with 100 % isodose volume sphericity and dose conformity, though similar conformity was achievable with a range of complexities.

CONCLUSIONS

A higher level of importance should be directed towards plan complexity when considering plan robustness. It is recommended when planning multi-target SRS, larger MLC gaps and lower MLC aperture irregularity be considered during plan optimisation due to higher robustness should patient positioning errors occur.

Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction

BACKGROUND

Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP).

OBJECTIVE

To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image.

METHOD

This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step.

RESULTS

With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods.

CONCLUSIONS

The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration.

Metrology for advanced radiotherapy using particle beams with ultra-high dose rates

Dosimetry of ultra-high dose rate beams is one of the critical components which is required for safe implementation of FLASH radiotherapy (RT) into clinical practice. In the past years several national and international programmes have emerged with the aim to address some of the needs that are required for translation of this modality to clinics. These involve the establishment of dosimetry standards as well as the validation of protocols and dosimetry procedures. This review provides an overview of recent developments in the field of dosimetry for FLASH RT, with particular focus on primary and secondary standard instruments, and provides a brief outlook on the future work which is required to enable clinical implementation of FLASH RT.

Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB

Background

Recent innovations are making radiology more advanced for patient and patient services. Under the immense burden of radiology practice, Artificial Intelligence (AI) assists in obtaining Computed Tomography (CT) images with less scan time, proper patient placement, low radiation dose (RD), and improved image quality (IQ). Hence, the aim of this study was to evaluate and compare the positioning accuracy, RD, and IQ of AI-based automatic and manual positioning techniques for CT kidney ureters and bladder (CT KUB).

Methods

This prospective study included 143 patients in each group who were referred for computed tomography (CT) KUB examination. Group 1 patients underwent manual positioning (MP), and group 2 patients underwent AI-based automatic positioning (AP) for CT KUB examination. The scanning protocol was kept constant for both the groups. The off-center distance, RD, and quantitative and qualitative IQ of each group were evaluated and compared.

Results

The AP group (9.66±6.361 mm) had significantly less patient off-center distance than the MP group (15.12±9.55 mm). There was a significant reduction in RD in the AP group compared with that in the MP group. The quantitative image noise (IN) was lower, with a higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the AP group than in the MP group (p<0.05). Qualitative IQ parameters such as IN, sharpness, and overall IQ also showed significant differences (p< 0.05), with higher scores in the AP group than in the MP group.

Conclusions

The AI-based AP showed higher positioning accuracy with less off-center distance (44%), which resulted in 12% reduction in RD and improved IQ for CT KUB imaging compared with MP.

The Best of Both Worlds: Ultra-high-pitch Pulmonary Angiography with Free-Breathing Technique by Means of Photon-Counting Detector CT for Diagnosis of Acute Pulmonary Embolism

RATIONALE AND OBJECTIVES

To assess image quality and radiation dose of ultra-high-pitch CT pulmonary angiography (CTPA) with free-breathing technique for diagnosis of pulmonary embolism using a photon-counting detector (PCD) CT compared to matched energy-integrating detector (EID)-based single-energy CTPA.

MATERIALS AND METHODS

Fifty-one PCD-CTPAs were prospectively compared to 51 CTPAs on a third-generation dual-source EID-CT. CTPAs were acquired with an ultra-high-pitch protocol with free-breathing technique (40 mL contrast medium, pitch 3.2) at 140 kV (PCD) and 70-100 kV (EID). Iodine maps were reconstructed from spectral PCD-CTPAs. Image quality of CTPAs and iodine maps was assessed independently by three radiologists. Additionally, CT attenuation numbers within pulmonary arteries as well as signal-to-noise and contrast-to-noise ratios (SNR, CNR) were compared. Administered radiation dose was compared.

RESULTS

CT attenuation was higher in the PCD-group (all P < 0.05). CNR and SNR were higher in lobar pulmonary arteries in PCD-CTPAs (P < 0.05), whereas no difference was ascertained within the pulmonary trunk (P > 0.05). Image quality of PCD-CTPA was rated best by all readers (excellent/good image quality in 96.1% of PCD-CTPAs vs. 50.9% of EID-CTPAs). PCD-CT produced no non-diagnostic scans vs. three non-diagnostic (5.9%) EID-CTPAs. Radiation dose was lower with PCD-CT than with EID-CT (effective dose 1.33 ± 0.47 vs. 1.80 ± 0.82 mSv; all P < 0.05).

CONCLUSION

Ultra-high-pitch CTPA with free-breathing technique with PCD-CT allows for superior image quality with significantly reduced radiation dose and full spectral information. With the ultra-high pitch, only PCD-CTPA enables reconstruction of iodine maps containing additional functional information.

Dosimetry and Monte Carlo modelling of the Papillon+ contact X-ray brachytherapy device

PURPOSE

This study aimed to develop and validate a Monte Carlo (MC) model for the Papillon+ contact x-ray brachytherapy (CXB) device, producing 50 kilovolt (kV) X-rays, specifically focusing on its application with a 25 mm diameter rectal applicator for contact therapy.

MATERIAL AND METHODS

The validation process involved depth dose and transverse dose profile measurements using EBT3 gafchromic films positioned in a plastic water low energy range phantom. The half-value layer (HVL) was further measured and derived from the simulated X-ray spectra.

RESULTS

Excellent agreement within ±2% was achieved between the measured and simulated on-axis depth dose curves for the 25 mm rectal applicator. Transverse dose profile measurements showed a high level of agreement between the simulation and measurements, on average 3.1% in contact with the applicator at the surface of the phantom and on average 1.7% at 10 mm depth. A close agreement within 5.5% was noticed concerning the HVL between the measurement and simulation. The simulated gamma spectra and 2D-dose distribution demonstrated a soft X-ray energy spectrum and a uniform dose distribution in contact with the applicator.

CONCLUSIONS

An MC model was successfully developed for the Papillon+ eBT device with a 25 mm diameter rectal applicator. The validated model, with its demonstrated accuracy in depth dose and transverse dose profile simulations, is a valuable tool for quality assurance and patient safety and, in a later phase, may be used for treatment planning, dose calculations and tissue inhomogeneity corrections.

Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer

BACKGROUND

The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC).

METHODS

One hundred and twenty-eight BC patients underwent a 18F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical (C), clinical and metabolic (C+M) and clinical, metabolic and tumour BF (C+M+BF) information combined. Algorithms were trained on 80% of the dataset and tested on the remaining 20%. Univariate and multivariate features selections were carried out on the training dataset. A total of 50 shuffle splits were performed. The analysis was carried out on the whole dataset (HER2 and Triple Negative (TN)), and separately in HER2 (N=76) and TN (N=52) tumours.

RESULTS

In the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively).

CONCLUSION

Baseline clinical data combined with global and texture tumour metabolism parameters assessed by 18F-FDG PET/CT provide a better prediction of pCR after NAC in patients with BC compared to clinical parameters alone for TN, and HER2 and TN tumours together. In contrast, adding BF parameters to the models did not improve prediction, regardless of the tumour subgroup analysed.

A method to improve sensitivity of Ferrous ammonium sulfate - Benzoic acid - Xylenol orange (FBX) aqueous radiation dosimeter

Ferrous ammonium sulfate - Benzoic acid - Xylenol orange (FBX) solution is known for its dosimetry properties in the dose range applicable in radiation oncology. Several attempts at improving its dose sensitivity have been reported in literature. The current work explores a novel method to improve the dose response of the system in the range 0-10 Gy with the original standard composition of the solution. Value of the sensitivity of the dosimeter was found to be 7.471/Gy with excellent linearity using the developed method. This is 115 times higher than the sensitivity obtained using the conventional methods.

Technological Advances in SPECT and SPECT/CT Imaging

Single photon emission tomography/computed tomography (SPECT/CT) is a mature imaging technology with a dynamic role in the diagnosis and monitoring of a wide array of diseases. This paper reviews the technological advances, clinical impact, and future directions of SPECT and SPECT/CT imaging. The focus of this review is on signal amplifier devices, detector materials, camera head and collimator designs, image reconstruction techniques, and quantitative methods. Bulky photomultiplier tubes (PMTs) are being replaced by position-sensitive PMTs (PSPMTs), avalanche photodiodes (APDs), and silicon PMs to achieve higher detection efficiency and improved energy resolution and spatial resolution. Most recently, new SPECT cameras have been designed for cardiac imaging. The new design involves using specialised collimators in conjunction with conventional sodium iodide detectors (NaI(Tl)) or an L-shaped camera head, which utilises semiconductor detector materials such as CdZnTe (CZT: cadmium-zinc-telluride). The clinical benefits of the new design include shorter scanning times, improved image quality, enhanced patient comfort, reduced claustrophobic effects, and decreased overall size, particularly in specialised clinical centres. These noticeable improvements are also attributed to the implementation of resolution-recovery iterative reconstructions. Immense efforts have been made to establish SPECT and SPECT/CT imaging as quantitative tools by incorporating camera-specific modelling. Moreover, this review includes clinical examples in oncology, neurology, cardiology, musculoskeletal, and infection, demonstrating the impact of these advancements on clinical practice in radiology and molecular imaging departments.

Design, construction, and dosimetry of 3D printed heterogeneous phantoms for synchrotron brain cancer radiation therapy quality assurance

Objective.This study aims to design, manufacture, and test 3D printed quality assurance (QA) dosimetry phantoms for synchrotron brain cancer radiation therapy at the Australian synchrotron.Approach.Fabricated 3D printed phantoms from simple slab phantoms, a preclinical rat phantom, and an anthropomorphic head phantom were fabricated and characterized. Attenuation measurements of various polymers, ceramics and metals were acquired using synchrotron monochromatic micro-computed tomography (CT) imaging. Polylactic acid plus, VeroClear, Durable resin, and tricalcium phosphate were used in constructing the phantoms. Furthermore, 3D printed bone equivalent materials were compared relative to ICRU bone and hemihydrate plaster. Homogeneous and heterogeneous rat phantoms were designed and fabricated using tissue-equivalent materials. Geometric accuracy, CT imaging, and consistency were considered. Moreover, synchrotron broad-beam x-rays were delivered using a 3 Tesla superconducting multipole wiggler field for four sets of synchrotron radiation beam qualities. Dose measurements were acquired using a PinPoint ionization chamber and compared relative to a water phantom and a RMI457 Solid Water phantom. Experimental depth doses were compared relative to calculated doses using a Geant4 Monte Carlo simulation.Main results.Polylactic acid (PLA+) shows to have a good match with the attenuation coefficient of ICRU water, while both tricalcium phosphate and hydroxyapatite have good attenuation similarity with ICRU bone cortical. PLA+ material can be used as substitute to RMI457 slabs for reference dosimetry with a maximum difference of 1.84%. Percent depth dose measurement also shows that PLA+ has the best match with water and RMI457 within ±2.2% and ±1.6%, respectively. Overall, PLA+ phantoms match with RMI457 phantoms within ±3%.Significance and conclusion.The fabricated phantoms are excellent tissue equivalent equipment for synchrotron radiation dosimetry QA measurement. Both the rat and the anthropomorphic head phantoms are useful in synchrotron brain cancer radiotherapy dosimetry, experiments, and future clinical translation of synchrotron radiotherapy and imaging.

Photon-Counting CT in the Head and Neck: Current Applications and Future Prospects

Photon-counting detectors (PCDs) represent a major milestone in the evolution of CT imaging. CT scanners using PCD systems have already been shown to generate images with substantially greater spatial resolution, superior iodine contrast-to-noise ratio, and reduced artifact compared with conventional energy-integrating detector-based systems. These benefits can be achieved with considerably decreased radiation dose. Recent studies have focused on the advantages of PCD-CT scanners in numerous anatomic regions, particularly the coronary and cerebral vasculature, pulmonary structures, and musculoskeletal imaging. However, PCD-CT imaging is also anticipated to be a major advantage for head and neck imaging. In this paper, we review current clinical applications of PCD-CT in head and neck imaging, with a focus on the temporal bone, facial bones, and paranasal sinuses; minor arterial vasculature; and the spectral capabilities of PCD systems.

An in-vivo treatment monitoring system for ion-beam radiotherapy based on 28 Timepix3 detectors

Ion-beam radiotherapy is an advanced cancer treatment modality offering steep dose gradients and a high biological effectiveness. These gradients make the therapy vulnerable to patient-setup and anatomical changes between treatment fractions, which may go unnoticed. Charged fragments from nuclear interactions of the ion beam with the patient tissue may carry information about the treatment quality. Currently, the fragments escape the patient undetected. Inter-fractional in-vivo treatment monitoring based on these charged nuclear fragments could make ion-beam therapy safer and more efficient. We developed an ion-beam monitoring system based on 28 hybrid silicon pixel detectors (Timepix3) to measure the distribution of fragment origins in three dimensions. The system design choices as well as the ion-beam monitoring performance measurements are presented in this manuscript. A spatial resolution of 4 mm along the beam axis was achieved for the measurement of individual fragment origins. Beam-range shifts of1.5 mm were identified in a clinically realistic treatment scenario with an anthropomorphic head phantom. The monitoring system is currently being used in a prospective clinical trial at the Heidelberg Ion Beam Therapy Centre for head-and-neck as well as central nervous system cancer patients.

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation

OBJECTIVES

This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data.

MATERIALS AND METHODS

A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF.

RESULTS

For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed.

CONCLUSION

Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.

Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging offers unique insights into tumor biology and treatment response, it is imperative to elucidate the challenges and constraints inherent in this domain to facilitate their translation into clinical practice. This review examines the challenges and limitations of applying radiomics to PET imaging, synthesizing findings from the last five years (2019-2023) and highlights the significance of addressing these challenges to realize the full clinical potential of radiomics in oncology and molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, and Web of Science, using keywords relevant to radiomics issues in PET imaging. Only studies published in peer-reviewed journals were eligible for inclusion in this review. Although many studies have highlighted the potential of radiomics in predicting treatment response, assessing tumor heterogeneity, enabling risk stratification, and personalized therapy selection, various challenges regarding the practical implementation of the proposed models still need to be addressed. This review illustrates the challenges and limitations of radiomics in PET imaging across various cancer types, encompassing both phantom and clinical investigations. The analyzed studies highlight the importance of reproducible segmentation methods, standardized pre-processing and post-processing methodologies, and the need to create large multicenter studies registered in a centralized database to promote the continuous validation and clinical integration of radiomics into PET imaging.

Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions

Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.

Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease

OBJECTIVES

To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD).

MATERIALS AND METHODS

This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups.

RESULTS

A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002).

CONCLUSION

Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD.

CLINICAL RELEVANCE STATEMENT

The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD.

KEY POINTS

Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 3: Contribution from Calculating Absorbed Dose from Time-Integrated Activity

Image-based dosimetry-guided radiopharmaceutical therapy has the potential to personalize treatment by limiting toxicity to organs at risk and maximizing the therapeutic effect. The 177Lu dosimetry challenge of the Society of Nuclear Medicine and Molecular Imaging consisted of 5 tasks assessing the variability in the dosimetry workflow. The fifth task investigated the variability associated with the last step, dose conversion, of the dosimetry workflow on which this study is based. Methods: Reference variability was assessed by 2 medical physicists using different software, methods, and all possible combinations of input segmentation formats and time points as provided in the challenge. General descriptive statistics for absorbed dose values from the global submissions from participants were calculated, and variability was measured using the quartile coefficient of dispersion. Results: For the liver, which included lesions with high uptake, variabilities of up to 36% were found. The baseline analysis showed a variability of 29% in absorbed dose results for the liver from datasets where lesions included and excluded were grouped, indicating that variation in how lesions in normal liver were treated was a significant source of variability. For other organs and lesions, variability was within 7%, independently of software used except for the local deposition method. Conclusion: The choice of dosimetry method or software had a small contribution to the overall variability of dose estimates.

Navigating the straits: realizing the potential of proton FLASH through physics advances and further pre-clinical characterization

Ultra-high dose-rate 'FLASH' radiotherapy may be a pivotal step forward for cancer treatment, widening the therapeutic window between radiation tumour killing and damage to neighbouring normal tissues. The extent of normal tissue sparing reported in pre-clinical FLASH studies typically corresponds to an increase in isotoxic dose-levels of 5-20%, though gains are larger at higher doses. Conditions currently thought necessary for FLASH normal tissue sparing are a dose-rate ≥40 Gy s-1, dose-per-fraction ≥5-10 Gy and irradiation duration ≤0.2-0.5 s. Cyclotron proton accelerators are the first clinical systems to be adapted to irradiate deep-seated tumours at FLASH dose-rates, but even using these machines it is challenging to meet the FLASH conditions. In this review we describe the challenges for delivering FLASH proton beam therapy, the compromises that ensue if these challenges are not addressed, and resulting dosimetric losses. Some of these losses are on the same scale as the gains from FLASH found pre-clinically. We therefore conclude that for FLASH to succeed clinically the challenges must be systematically overcome rather than accommodated, and we survey physical and pre-clinical routes for achieving this.

A high-throughput focused collimator for OAR-sparing preclinical proton FLASH studies: commissioning and validation

Objective. To fabricate and validate a novel focused collimator designed to spare normal tissue in a murine hemithoracic irradiation model using 250 MeV protons delivered at ultra-high dose rates (UHDRs) for preclinical FLASH radiation therapy (FLASH-RT) studies.Approach. A brass collimator was developed to shape 250 MeV UHDR protons from our Varian ProBeam. Six 13 mm apertures, of equivalent size to kV x-ray fields historically used to perform hemithorax irradiations, were precisely machined to match beam divergence, allowing concurrent hemithoracic irradiation of six mice while sparing the contralateral lung and abdominal organs. The collimated field profiles were characterized by film dosimetry, and a radiation survey of neutron activation was performed to ensure the safety of staff positioning animals.Main results. The brass collimator produced 1.2 mm penumbrae radiation fields comparable to kV x-rays used in preclinical studies. The penumbrae in the six apertures are similar, with full-width half-maxima of 13.3 mm and 13.5 mm for the central and peripheral apertures, respectively. The collimator delivered a similar dose at an average rate of 52 Gy s-1for all apertures. While neutron activation produces a high (0.2 mSv h-1) initial ambient equivalent dose rate, a parallel work-flow in which imaging and setup are performed without the collimator ensures safety to staff.Significance. Scanned protons have the greatest potential for future translation of FLASH-RT in clinical treatments due to their ability to treat deep-seated tumors with high conformality. However, the Gaussian distribution of dose in proton spots produces wider lateral penumbrae compared to other modalities. This presents a challenge in small animal pre-clinical studies, where millimeter-scale penumbrae are required to precisely target the intended volume. Offering high-throughput irradiation of mice with sharp penumbrae, our novel collimator-based platform serves as an important benchmark for enabling large-scale, cost-effective radiobiological studies of the FLASH effect in murine models.

Mechanism and therapeutic targets of the involvement of a novel lysosomal proton channel TMEM175 in Parkinson's disease

Parkinson's disease (PD), recognized as the second most prevalent neurodegenerative disease in the aging population, presents a significant challenge due to the current lack of effective treatment methods to mitigate its progression. Many pathogenesis of PD are related to lysosomal dysfunction. Moreover, extensive genetic studies have shown a significant correlation between the lysosomal membrane protein TMEM175 and the risk of developing PD. Building on this discovery, TMEM175 has been identified as a novel potassium ion channel. Intriguingly, further investigations have found that potassium ion channels gradually close and transform into hydrion "excretion" channels in the microenvironment of lysosomes. This finding was further substantiated by studies on TMEM175 knockout mice, which exhibited pronounced motor dysfunction in pole climbing and suspension tests, alongside a notable reduction in dopamine neurons within the substantia nigra compacta. Despite these advancements, the current research landscape is not without its controversies. In light of this, the present review endeavors to methodically examine and consolidate a vast array of recent literature on TMEM175. This comprehensive analysis spans from the foundational research on the structure and function of TMEM175 to expansive population genetics studies and mechanism research utilizing cellular and animal models.A thorough understanding of the structure and function of TMEM175, coupled with insights into the intricate mechanisms underpinning lysosomal dysfunction in PD dopaminergic neurons, is imperative. Such knowledge is crucial for pinpointing precise intervention targets, thereby paving the way for novel therapeutic strategies that could potentially alter the neurodegenerative trajectory of PD.

Simultaneous Second Harmonic Generation and Multiphoton Excited Photoluminescence in Samarium-Doped BaTiO3 Nanoparticles Functionalized with Poly(ethylene glycol)

In this work, samarium-doped BaTiO3 (BT:Sm) nanoparticles (NPs) were prepared and coated with poly(ethylene glycol) (PEG) to investigate their optical characteristics and compatibility with biological systems. The structure, particle morphology, optical properties, and biological compatibility of the NPs were assessed. The results demonstrated the formation of BT:Sm and [(BT:Sm)-PEG]. The relative intensities and positions of peaks in the X-ray diffraction (XRD) are consistent with an average crystallite size of ∼75 nm. The Raman spectra showed that Sm doping produced the typical tetragonal peaks at around 306 and 715 cm-1, and Fourier transform infrared (FTIR) spectroscopy showed that the PEGylation process was effective. Also, our investigation demonstrates the potential of these NPs as very temperature-sensitive nanosensors with a resolution exceeding 0.5 °C, which is achievable through optical excitation. We also analyze their emission properties. Finally, we present a study related with the mitochondrial activity of naked and PEG-coated NPs. The results indicate that neither naked nor PEG-coated NPs exhibit changes in mitochondrial metabolism, as indicated by quantitative cell viability and morphological visualization. The PEG-coated NPs prevented the formation of aggregates in cell culture compared to naked NPs, demonstrating the significance of PEG as a stabilizing agent.

Clinical application of a GPU-accelerated monte carlo dose verification for cyberknife M6 with Iris collimator

PURPOSE

To apply an independent GPU-accelerated Monte Carlo (MC) dose verification for CyberKnife M6 with Iris collimator and evaluate the dose calculation accuracy of RayTracing (TPS-RT) algorithm and Monte Carlo (TPS-MC) algorithm in the Precision treatment planning system (TPS).

METHODS

GPU-accelerated MC algorithm (ArcherQA-CK) was integrated into a commercial dose verification system, ArcherQA, to implement the patient-specific quality assurance in the CyberKnife M6 system. 30 clinical cases (10 cases in head, and 10 cases in chest, and 10 cases in abdomen) were collected in this study. For each case, three different dose calculation methods (TPS-MC, TPS-RT and ArcherQA-CK) were implemented based on the same treatment plan and compared with each other. For evaluation, the 3D global gamma analysis and dose parameters of the target volume and organs at risk (OARs) were analyzed comparatively.

RESULTS

For gamma pass rates at the criterion of 2%/2 mm, the results were over 98.0% for TPS-MC vs.TPS-RT, TPS-MC vs. ArcherQA-CK and TPS-RT vs. ArcherQA-CK in head cases, 84.9% for TPS-MC vs.TPS-RT, 98.0% for TPS-MC vs. ArcherQA-CK and 83.3% for TPS-RT vs. ArcherQA-CK in chest cases, 98.2% for TPS-MC vs.TPS-RT, 99.4% for TPS-MC vs. ArcherQA-CK and 94.5% for TPS-RT vs. ArcherQA-CK in abdomen cases. For dose parameters of planning target volume (PTV) in chest cases, the deviations of TPS-RT vs. TPS-MC and ArcherQA-CK vs. TPS-MC had significant difference (P < 0.01), and the deviations of TPS-RT vs. TPS-MC and TPS-RT vs. ArcherQA-CK were similar (P > 0.05). ArcherQA-CK had less calculation time compared with TPS-MC (1.66 min vs. 65.11 min).

CONCLUSIONS

Our proposed MC dose engine (ArcherQA-CK) has a high degree of consistency with the Precision TPS-MC algorithm, which can quickly identify the calculation errors of TPS-RT algorithm for some chest cases. ArcherQA-CK can provide accurate patient-specific quality assurance in clinical practice.

Characterization of accurate 3D collimator-detector response function for single- and multi-lofthole collimated SPECT cameras

PURPOSE

Collimator-detector response function (CDRF) of a SPECT scanner refers to the image generated from a point source of activity. This research aims to characterize the CDRF of a breast-dedicated SPECT imager equipped with a lofthole collimator using GATE Monte Carlo simulation.

MATERIALS AND METHODS

To do so, a cylindrical multi-lofthole collimation system with lofthole apertures dedicated to breast imaging was modeled using the GATE Monte Carlo simulator. The dependency of the CDRF on the source-to-collimator distance of a single-lofthole as well as 8-lofthole collimations was assessed and then compared. In addition, the 3D-sensitivity map of the 8-lofthole collimation was derived. Finally, fair comparisons were conducted between the response of the 8-lofthole collimator and that of an 8-pinhole and also existing analytical derivations. In all cases, a data acquisition period of 5.0 min with an in-air 99mTc point source was considered.

RESULTS

For the single-lofthole collimator, 4.5 times increasing the magnification factor leads to a 16- and twofold improvement in the sensitivity and spatial resolution, respectively. In the single-lofthole collimator, the resolution and sensitivity are degraded as the source-to-aperture distance increases. For the cylindrical 8-lofthole collimator, the findings confirm that CDRF strongly depends on source-to-aperture distance and angle of photon incidence. For a 30 mm in-plane offset point, a 25% increase in sensitivity is observed compared to that of the center of the FOV. Increasing the angle from 0∘ to 34∘ results in a 50% reduction in sensitivity. Furthermore, the findings illustrate that spatial resolution follows a quadratic function as10 - 3 d 2 + 2 × 10 - 4 d + R 0 where d is an offset along the x-, y-, and z-axis, and R0 is the spatial resolution at the center of the FOV.

CONCLUSION

In conclusion, both spatial resolution and sensitivity of the lofthole collimation are considerably angle- and offset-dependent within the FOV of single- and multi-lofthole collimated SPECT imagers.

The verification of the utility of a commercially available phantom combination for quality control in contrast-enhanced mammography

Contrast-enhanced mammography is being increasingly implemented clinically, providing much improved contrast between tumour and background structures, particularly in dense breasts. Although CEM is similar to conventional mammography it differs via an additional exposure with high energy X-rays (≥ 40 kVp) and subsequent image subtraction. Because of its special operational aspects, the CEM aspect of a CEM unit needs to be uniquely characterised and evaluated. This study aims to verify the utility of a commercially available phantom set (BR3D model 020 and CESM model 022 phantoms (CIRS, Norfolk, Virginia, USA)) in performing key CEM performance tests (linearity of system response with iodine concentration and background subtraction) on two models of CEM units in a clinical setting. The tests were successfully performed, yielding results similar to previously published studies. Further, similarities and differences in the two systems from different vendors were highlighted, knowledge of which may potentially facilitate optimisation of the systems.

Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum

AIMS

The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum.

METHODS

A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability.

RESULTS

We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia.

CONCLUSION

The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.

Assessing focal spot alignment in clinical linear accelerators: a comprehensive evaluation with triplet phantoms

A fundamental parameter to evaluate the beam delivery precision and stability on a clinical linear accelerator (linac) is the focal spot position (FSP) measured relative to the collimator axis of the radiation head. The aims of this work were to evaluate comprehensive data on FSP acquired on linacs in clinical use and to establish the ability of alternative phantoms to detect effects on patient plan delivery related to FSP. FSP measurements were conducted using a rigid phantom holding two ball-bearings at two different distances from the radiation source. Images of these ball-bearings were acquired using the electronic portal imaging device (EPID) integrated with each linac. Machine QA was assessed using a radiation head-mounted PTW STARCHECK phantom. Patient plan QA was investigated using the SNC ArcCHECK phantom positioned on the treatment couch, irradiated with VMAT plans across a complete 360° gantry rotation and three X-ray energies. This study covered eight Elekta linacs, including those with 6 MV, 18 MV, and 6 MV flattening-filter-free (FFF) beams. The largest range in the FSP was found for 6 MV FFF. The FSP of one linac, retrofitted with 6 MV FFF, displayed substantial differences in FSP compared to 6 MV FFF beams on other linacs, which all had FSP ranges less than 0.50 mm and 0.25 mm in the lateral and longitudinal directions, respectively. The PTW STARCHECK phantom proved effective in characterising the FSP, while the SNC ArcCHECK measurements could not discern FSP-related features. Minor variations in FSP may be attributed to adjustments in linac parameters, component replacements necessary for beam delivery, and the wear and tear of various linac components, including the magnetron and gun filament. Consideration should be given to the ability of any particular phantom to detect a subsequent impact on the accuracy of patient plan delivery.

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

Cardiovascular Magnetic Resonance Radiomics to Identify Components of the Extracellular Matrix in Dilated Cardiomyopathy

BACKGROUND

Current cardiovascular magnetic resonance sequences cannot discriminate between different myocardial extracellular space (ECSs), including collagen, noncollagen, and inflammation. We sought to investigate whether cardiovascular magnetic resonance radiomics analysis can distinguish between noncollagen and inflammation from collagen in dilated cardiomyopathy.

METHODS

We identified data from 132 patients with dilated cardiomyopathy scheduled for an invasive septal biopsy who underwent cardiovascular magnetic resonance at 3 T. Cardiovascular magnetic resonance imaging protocol included native and postcontrast T1 mapping and late gadolinium enhancement (LGE). Radiomic features were computed from the midseptal myocardium, near the biopsy region, on native T1, extracellular volume (ECV) map, and LGE images. Principal component analysis was used to reduce the number of radiomic features to 5 principal radiomics. Moreover, a correlation analysis was conducted to identify radiomic features exhibiting a strong correlation (r>0.9) with the 5 principal radiomics. Biopsy samples were used to quantify ECS, myocardial fibrosis, and inflammation.

RESULTS

Four histopathological phenotypes were identified: low collagen (n=20), noncollagenous ECS expansion (n=49), mild to moderate collagenous ECS expansion (n=42), and severe collagenous ECS expansion (n=21). Noncollagenous expansion was associated with the highest risk of myocardial inflammation (65%). Although native T1 and ECV provided high diagnostic performance in differentiating severe fibrosis (C statistic, 0.90 and 0.90, respectively), their performance in differentiating between noncollagen and mild to moderate collagenous expansion decreased (C statistic: 0.59 and 0.55, respectively). Integration of ECV principal radiomics provided better discrimination and reclassification between noncollagen and mild to moderate collagen (C statistic, 0.79; net reclassification index, 0.83 [95% CI, 0.45-1.22]; P<0.001). There was a similar trend in the addition of native T1 principal radiomics (C statistic, 0.75; net reclassification index, 0.93 [95% CI, 0.56-1.29]; P<0.001) and LGE principal radiomics (C statistic, 0.74; net reclassification index, 0.59 [95% CI, 0.19-0.98]; P=0.004). Five radiomic features per sequence were identified with correlation analysis. They showed a similar improvement in performance for differentiating between noncollagen and mild to moderate collagen (native T1, ECV, LGE C statistic, 0.75, 0.77, and 0.71, respectively). These improvements remained significant when confined to a single radiomic feature (native T1, ECV, LGE C statistic, 0.71, 0.70, and 0.64, respectively).

CONCLUSIONS

Radiomic features extracted from native T1, ECV, and LGE provide incremental information that improves our capability to discriminate noncollagenous expansion from mild to moderate collagen and could be useful for detecting subtle chronic inflammation in patients with dilated cardiomyopathy.

A rare instance of latent systematic error in volumetric-modulated arc therapy with field-extended multi-isocentre irradiation leading to a serious dose-delivery accident

Volumetric-modulated arc therapy (VMAT) with field-extended multi-isocentre irradiation (VMAT-FEMII) is an effective irradiation technique, particularly for large planning target volumes in the craniocaudal direction. A variety of treatment planning techniques have been reported to reduce the dosimetric impact. However, there is no guarantee that unexpected latent systematic errors would not occur. Herein, we report the experience with a rare case that could have led to a serious VMAT-FEMII-related accident. A patient with uterine cervical carcinoma was scheduled for VMAT-FEMII to the whole pelvis and the para-aortic lymph node region. A combination of the two sets of field groups with different isocentres was planned: one to cover the para-aortic lymph nodes and the other to cover the whole pelvis. Measurements based on the pretreatment dose delivery quality assurance (QA) revealed an unexpected overdose of >20% in the field overlap region. This overdose phenomenon is not reflected in the calculated dose distribution in the radiotherapy treatment planning system. Therefore, the plan was altered; a homogeneous dose distribution inside the dose junction was achieved. Several analyses were performed to elucidate the overdosing phenomenon. However, no conclusive answer was found to why non-reflection at the calculated dose distribution was found. The limitations to VMAT-FEMII are primarily related to systematic errors in the positional setup from patient-derived and/or mechanical sources. However, this report highlights a rare case of overdosing caused by inverse optimization and dose calculation. We recommend checking the aperture status of the jaw and multi-leaf collimator at each control point of the treatment plan and using a high-resolution image measurement system on a VMAT-FEMII QA to confirm the dose junction status.

Development of a novel phantom for tau PET imaging

PURPOSE

The cortical uptake of tau positron emission tomography (PET) tracers corresponds to the Braak stage and reflects the distribution and progression of tau neurofibrillary tangles. The present study aimed to develop and validate the basic performance of a novel tau PET phantom, as well as to establish standard test procedures and analytical methods.

METHODS

The tau PET phantom consisted of a brain simulation section simulated medial temporal lobe region and resolution and uniformity sections. The brain simulation section and hot rods and uniformity section contained 4 and 2 kBq/mL of 18F, respectively and images were acquired three times for 20 min with a PET/CT scanner. The resolution section was visually assessed with two sets of hot and cold rods. Recovery coefficients (RCs) as a quantitative value and coefficient of variation (CV) as image noise were determined based on the brain simulation and the uniformity section, respectively.

RESULTS

Preparation of activity in the phantom was repeatable among three measurements. The quality of images in the brain simulation and uniformity section with the rods was good. The 5- or 6-mm rods were detected separately. The mean RCs calculated based on the VOI template were between 0.75 and 0.83. The CV at the center slice of uniformity section was 5.54%.

CONCLUSIONS

We developed a novel tau PET phantom to assess quantitative value, image noise, and detectability and resolution from brain simulation section, uniformity section, and rods, respectively. This phantom will contribute to the standardization and harmonization of tau PET imaging.

Ultra-high-resolution photon-counting detector CT in evaluating coronary stent patency: a comparison to invasive coronary angiography

OBJECTIVES

To determine the diagnostic accuracy of ultra-high-resolution photon-counting detector CT angiography (UHR PCD-CTA) for evaluating coronary stent patency compared to invasive coronary angiography (ICA).

METHODS

Consecutive, clinically referred patients with prior coronary stent implantation were prospectively enrolled between August 2022 and March 2023 and underwent UHR PCD-CTA (collimation, 120 × 0.2 mm). Two radiologists independently analyzed image quality of the in-stent lumen using a 5-point Likert scale, ranging from 1 ("excellent") to 5 ("non-diagnostic"), and assessed all coronary stents for the presence of in-stent stenosis (≥ 50% lumen narrowing). The diagnostic accuracy of UHR PCD-CTA was determined, with ICA serving as the standard of reference.

RESULTS

A total of 44 coronary stents in 18 participants (mean age, 83 years ± 6 [standard deviation]; 12 women) were included in the analysis. In 3/44 stents, both readers described image quality as non-diagnostic, whereas reader 2 noted a fourth stent to have non-diagnostic image quality. In comparison to ICA, UHR PCD-CTA demonstrated a sensitivity, specificity, and accuracy of 100% (95% CI [confidence interval] 47.8, 100), 92.3% (95% CI 79.1, 98.4), and 93.2% (95% CI 81.3, 98.6) for reader 1 and 100% (95% CI 47.8, 100), 87.2% (95% CI 72.6, 95.7), and 88.6% (95% CI 75.4, 96.2) for reader 2, respectively. Both readers observed a 100% negative predictive value (36/36 stents and 34/34 stents). Stent patency inter-reader agreement was 90.1%, corresponding to a substantial Cohen's kappa value of 0.72.

CONCLUSIONS

UHR PCD-CTA enables non-invasive assessment of coronary stent patency with high image quality and diagnostic accuracy.

CLINICAL RELEVANCE STATEMENT

Ultra-high-resolution photon-counting detector CT angiography represents a reliable and non-invasive method for assessing coronary stent patency. Its high negative predictive value makes it a promising alternative over invasive coronary angiography for the rule-out of in-stent stenosis.

KEY POINTS

• CT-based evaluation of coronary stent patency is limited by stent-induced artifacts and spatial resolution. • Ultra-high-resolution photon-counting detector CT accurately evaluates coronary stent patency compared to invasive coronary angiography. • Photon-counting detector CT represents a promising method for the non-invasive rule-out of in-stent stenosis.

Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

Determination of intrinsic energy dependence of point-like inorganic scintillation detector in brachytherapy

BACKGROUND

Inorganic scintillation detectors (ISDs) are promising for in vivo dosimetry in brachytherapy (BT). ISDs have fast response, providing time resolved dose rate information, and high sensitivity, attributed to high atomic numbers. However, the conversion of the detector signal to absorbed dose-to-water is highly dependent on the energy spectrum of the incident radiation. This dependence is comprised of absorbed dose energy dependence, obtainable with Monte Carlo (MC) simulation, and the absorbed dose-to-signal conversion efficiency or intrinsic energy dependence requiring measurements. Studies have indicated negligible intrinsic energy dependence of ZnSe:O-based ISDs in Ir-192 BT. A full characterization has not been performed earlier.

PURPOSE

This study characterizes the intrinsic energy dependence of ZnSe:O-based ISDs for kV X-ray radiation qualities, with energies relevant for BT.

METHODS

Three point-like ISDs made from fiber-coupled cuboid ZnSe:O-based scintillators were calibrated at the Swedish National Metrology Laboratory for ionizing radiation. The calibration was done in terms of air kerma free-in-air,K air ${K}_{{\mathrm{air}}}$ , in 13 X-ray radiation qualities, Q $Q$ , from 25 to 300 kVp (CCRI 25-250 kV and ISO 4037 N-series), and in terms of absorbed dose to water,D w ${D}_{\mathrm{w}}$ , in a Co-60 beam,Q 0 ${Q}_0$ . The mean absorbed dose to the ISDs, relative toK air ${K}_{{\mathrm{air}}}$ andD w ${D}_{\mathrm{w}}$ , were obtained with the MC code TOPAS (Geant4) using X-ray spectra obtained with SpekPy software and laboratory filtration data and a generic Co-60 source. The intrinsic energy dependence was determined as a function of effective photon energy,E e f f ${E}_{eff}$ , (relative to Co-60). The angular dependence of the ISD signal was measured in a 25 kVp (0.20 mm Al HVL) and 135 kVp beam (0.48 mm Cu HVL), by rotating the ISDs 180° around the fiber's longitudinal axis (perpendicular to the beam). A full 360° was not performed due to setup limitations. The impact of detector design was quantified with MC simulation.

RESULTS

Above 30 keVE e f f ${E}_{eff}$ the intrinsic energy dependence varied with less than 5 ± 4% from unity for all detectors (with the uncertainty expressed as the mean of all expanded measurement uncertainties for individualE e f f ${E}_{eff}$ above 30 keV, k = 2). Below 30 keV, it decreased with up to 17% and inter-detector variations of 13% were observed, likely due to differences in detector geometry not captured by the simulations using nominal geometry. In the 25 kVp radiation quality, the ISD signal varied with 24% over a ∼45° rotation. For 135 kVp, the corresponding variation was below 3%. Assuming a 0.05 mm thicker layer of reflective paint around the sensitive volume changed the absorbed dose with 6.3% at the lowestE e f f ${E}_{eff}$ , and with less than 2% at higher energies.

CONCLUSION

The study suggests that the ISDs have an intrinsic energy dependence relative to Co-60 lower than 5 ± 4% in radiation qualities withE e f f ${E}_{eff}\ $ > 30 keV. Therefore, they could in principle be calibrated in a Co-60 beam quality and transferred to such radiation qualities with correction factors determined only by the absorbed dose energy dependence obtained from MC simulations. This encourages exploration of the ISDs' applications in intensity modulated BT with Yb-169 or other novel intermediate energy isotopes.

A modular, high dynamic range passive neutron dosimeter and imaging diagnostic

The multi-decade neutron dosimeter and imaging diagnostic (MDND) is a passive diagnostic that utilizes the polyethylene (n, p) nuclear reaction to enhance the diagnostic's sensitivity for time and energy integrated neutron measurements in the range of 2.45-14.1 MeV. The MDND utilizes a combination of radiochromic film, phosphor image plates, and solid-state nuclear track detectors, with the goal of providing several orders of magnitude of dynamic range in terms of measured neutron fluence. The diagnostic design was guided by simulations in the Monte Carlo N-Particle (MCNP) transport code to determine the optimum thickness of the polyethylene convertor for maximum proton fluence incident on the detection medium as a function of incident neutron energy. In addition, the simulation results of complete diagnostic assemblies, or "stacks," were used to determine the total dynamic range of an MDND in terms of measured neutron source yield, which was found to be between around 107 and 1015 emitted into 4π with the detector located 1 m away from the source. Complimentary to these simulations, individual detectors within a stack were simulated and analyzed to determine response as a function of neutron energy and yield. This work presents the diagnostic design, MCNP simulation results, and analysis of expected signals for varying neutron sources.

Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography

BACKGROUND

Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality.

OBJECTIVE

To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children.

MATERIALS AND METHODS

This single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1-17 years) who underwent sequential abdominal CT scans-one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)-within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups.

RESULTS

The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05).

CONCLUSION

Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.

Active learning using adaptable task-based prioritisation

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for a new kidney segmentation task, unseen in training, using between approximately 40% to 60% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6% and 10.2% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.