In Situ Hydrogel with Immobilized Mn-Porphyrin for Reactive Oxygen Species Scavenging, Oxygen Generation, and Risedronate Delivery in Bone Defect Treatment

We propose a hydrogel immobilized with manganese porphyrin (MnP), a biomimetic superoxide dismutase (SOD), and catalase (CAT) to modulate reactive oxygen species (ROS) and hypoxia that impede the repair of large bone defects. Our hydrogel synthesis involved thiolated chitosan and polyethylene glycol-maleimide conjugated with MnPs (MnP-PEG-MAL), which enabled in situ gelation via a click reaction. Through optimization, a hydrogel with mechanical properties and catalytic effects favorable for bone repair was selected. Additionally, the hydrogel was incorporated with risedronate to induce synergistic effects of ROS scavenging, O2 generation, and sustained drug release. In vitro studies demonstrated enhanced proliferation and differentiation of MG-63 cells and suppressed proliferation and differentiation of RAW 264.7 cells in ROS-rich environments. In vivo evaluation of a calvarial bone defect model revealed that this multifunctional hydrogel facilitated significant bone regeneration. Therefore, the hydrogel proposed in this study is a promising strategy for addressing complex wound environments and promoting effective bone healing.

TEMPy: a toolkit for the modeling of weighted tissue equivalent material in diagnostic imaging

Objective.Accurate simulation of human tissues is imperative for advancements in diagnostic imaging, particularly in the fields of dosimetry and image quality evaluation. Developing Tissue Equivalent Materials (TEMs) with radiological characteristics akin to those of human tissues is essential for ensuring the reliability and relevance of imaging studies. This study presents the development of a mathematical model and a new toolkit (TEMPy) for obtaining the best composition of materials that mimic the radiological characteristics of human tissues. The model and the toolkit are described, along with an example showcasing its application to obtain desired TEMs.Approach.The methodology consisted of fitting volume fractions of the components of TEM in order to determine its linear attenuation coefficient as close as possible to the linear attenuation coefficient of the reference material. The fitting procedure adopted a modified Least Square Method including a weight function. This function reflects the contribution of the x-ray spectra in the suitable energy range of interest. TEMPy can also be used to estimate the effective atomic number and electron density of the resulting TEM.Main results.TEMPy was used to obtain the chemical composition of materials equivalent to water and soft tissue, in the energy range used in x-ray imaging (10 -150 keV) and for breast tissue using the energy range (5-40 keV). The maximum relative difference between the linear attenuation coefficients of the developed and reference materials was ±5% in the considered energy ranges.Significance.TEMPy facilitates the formulation of TEMs with radiological properties closely mimicking those of real tissues, aiding in the preparation of physical anthropomorphic or geometric phantoms for various applications. The toolkit is freely available to interested readers.

A feasibility study of dosimetry for breast cancer radiotherapy based on body surface changes

BACKGROUND

The requirement for precise and effective delivery of the actual dose to the patient grows along with the complexity of breast cancer radiotherapy. Dosimetry during treatment has become a crucial component of guaranteeing the efficacy and security.

PURPOSE

To propose a dosimetry method during breast cancer radiotherapy based on body surface changes.

METHODS

A total of 29 left breast cancer radiotherapy cases were retroactively retrieved from an earlier database for analysis. Non-rigid image registration and dose recalculation of the planning computed tomography (CT) referring to the Cone-beam computed tomography were performed to obtain dose changes. The study used 3D point cloud feature extraction to characterize body surface changes. Based on the correlation proof, a mapping model is developed between body surface changes and dose changes using neural network framework. The MSE metrics, the Euclidean distances of feature points and the 3D gamma pass rate metric were used to assess the prediction accuracy.

RESULTS

A strong correlation exist between body surface changes and dose changes (first canonical correlation coefficient = 0.950). For the dose deformation field and dose amplitude difference in the test set, the MSE of the predicted and actual values were 0.136 pixels and 0.229 cGy, respectively. After deforming the planning dose into a deformed one, the feature points' Euclidean distance between it and the recalculated dose changes from 9.267 ± 1.879 mm to 0.456 ± 0.374 mm. The 3D gamma pass rate of 90% or higher for the 2 mm/2% criteria were achieved by 80.8% of all cases, with a minimum pass rate of 75.9% and a maximum pass rate of 99.6%. Pass rate for the 3 mm/2% criteria ranged from 87.8% to 99.8%, with 92.3% of the cases having a pass rate of 90% or higher.

CONCLUSIONS

This study provides a dosimetry method that is non-invasive, real-time, and requires no additional dose for breast cancer radiotherapy.

Body physical parameters instead of water equivalent diameter to calculate size-specific dose estimate in adult chest CT

This study aimed to investigate body physical parameters as substitutes for water equivalent diameter (Dw) while calculating size-specific dose estimates (SSDEs) during adult chest computed tomography (CT). A retrospective analysis was conducted on 776 patients. Patients were divided into training set (542 patients) and validation set (234 patients) according to a ratio of 7:3. The correlations between physical parameters and Dw were analyzed. The differences between SSDEsubstitutes and the reference SSDE (SSDEreference) were compared. Strong positive correlations were observed between body mass index (BMI) and Dw as well as between weight and Dw in overall, male, and female patients (all p < 0.001). The correlations between BMI and Dw were stronger than those between weight and Dw in overall, male, and female subjects (all p < 0.001). SSDEweight and SSDEBMI were not significantly different from SSDEreference (p > 0.05). The RMSEs of overall patients between SSDEweight and SSDEreference as well as between SSDEBMI and SSDEreference were 0.237 and 0.2, respectively. The use of sex-specific regression equations for BMI caused a slightly reduction in RMSE. Weight and BMI can be used as surrogate parameters for Dw when calculating SSDE in adult chest CT exams, with BMI being the preferred substitute parameter.

Investigation of abdominal artery delineation by photon-counting detector CT

OBJECTIVES

To evaluate the ability of 50-keV virtual monoenergetic images (VMI) to depict abdominal arteries in abdominal CT angiography (CTA) compared with 70-keV VMI with photon-counting detector CT (PCD-CT).

METHODS

Fifty consecutive patients who underwent multiphase abdominal scans between March and April 2023 were included. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantitatively assessed for the abdominal aorta (AA), celiac artery (CeA), superior mesenteric artery (SMA), renal artery (RA), and right hepatic artery (RHA) at both 50- and 70-keV VMI. In addition, 3D images from CTA were analyzed to measure arterial lengths and evaluate the visualization of distal branches.

RESULTS

Significantly higher SNR and CNR were observed at 50-keV compared to 70-keV VMI for all arteries: AA (36.54 and 48.28 vs. 25.70 and 28.46), CeA (22.39 and 48.38 vs. 19.09 and 29.15), SMA (23.34 and 49.34 vs. 19.67 and 29.71), RA (22.88 and 48.84 vs. 20.15 and 29.41), and RHA (14.38 and 44.41 vs. 13.45 and 27.18), all p < 0.05. Arterial lengths were also significantly longer at 50-keV: RHA (192.6 vs. 180.3 mm), SMA (230.9 vs. 216.5 mm), and RA (95.9 vs. 92.0 mm), all p < 0.001.

CONCLUSION

In abdominal CTA with PCD-CT, 50-keV VMI demonstrated superior quantitative image quality compared to 70-keV VMI. In addition, 50-keV VMI 3D CTA allowed better visualization of abdominal artery branches, highlighting its potential clinical advantage for improved imaging and detailed assessment of abdominal arteries.

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms

BACKGROUND

Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT.

METHODS

Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed.

RESULTS

DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058).

CONCLUSION

DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used.

RELEVANCE STATEMENT

Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction.

KEY POINTS

Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.

The Picasso's skepticism on computer science and the dawn of generative AI: questions after the answers to keep "machines-in-the-loop"

Starting from Picasso's quote ("Computers are useless. They can only give you answers"), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such as large language models (LLMs) in radiology, where their potential in reporting, image synthesis, and analysis is notable. However, the need for improvements, evaluations, and regulations prior to clinical use is also clear. Integration of LLMs into clinical workflow needs cautiousness, to avoid or at least mitigate risks associated with false diagnostic suggestions. We highlight challenges in synthetic image generation, inherent biases in AI models, and privacy concerns, stressing the importance of diverse training datasets and robust data privacy measures. We examine the regulatory landscape, including the 2023 Executive Order on AI in the United States and the 2024 AI Act in the European Union, which set standards for AI applications in healthcare. This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a "machines-in-the-loop" approach.

Two-dimensional oxygen-diffusion modelling for FLASH proton therapy with pencil beam scanning-Impact of diffusive tissue properties, dose, dose rate and scan patterns

Objective.Oxygen depletion is generally believed to play an important role in the FLASH effect-a differential reduction of the radiosensitivity of healthy tissues, relative to that of the tumour under ultra-high dose-rate (UHDR) irradiation conditions. In proton therapy (PT) with pencil-beam scanning (PBS), the deposition of dose, and, hence, the degree of (radiolytic) oxygen depletion varies both spatially and temporally. Therefore, the resulting oxygen concentration and the healthy-tissue sparing effect through radiation-induced hypoxia varies both spatially and temporally as well.Approach.We propose and numerically solve a physical oxygen diffusion model to study these effects and their dependence on tissue parameters and the scan pattern in pencil-beam delivery. Since current clinical FLASH PT (FLASH-PT) is based on 250 MeV shoot-through (transmission) beams, for which dose and dose rate (DR) hardly vary with depth compared to the variation transverse to the beam axis, we focus on the two-dimensional case. We numerically integrate the model to obtain the oxygen concentration in each voxel as a function of time and extract voxel-based and spatially and temporarily integrated metrics for oxygen (FLASH) enhanced dose. Furthermore, we evaluate the impact on oxygen enhancement of standard pencil-beam delivery patterns and patterns that were optimised on dose-rate. Our model can contribute to the identification of tissue properties and pencil-beam delivery parameters that are critical for FLASH-PT and it may be used for the optimisation of FLASH-PT treatment plans and their delivery.Main results.(i) the diffusive properties of oxygen are critical for the steady state concentration and therefore the FLASH effect, even more so in two dimensions when compared to one dimension. (ii) The FLASH effect through oxygen depletion depends primarily on dose and less on other parameters. (iii) At a fixed fraction dose there is a slight dependence on DR. (iv) Scan patterns optimised on DR slightly increase the oxygen induced FLASH effect.Significance.To our best knowledge, this is the first study assessing the impact of scan-pattern optimization (SPO) in FLASH-PT with PBS on a biological FLASH model. While the observed impact of SPO is relatively small, a larger effect is expected for larger target volumes. A better understanding of the FLASH effect and the role of oxygen (depletion) therein is essential for the further development of FLASH-PT with PBS, and SPO.

Development and first implementation of a novel multi-modality cardiac motion and dosimetry phantom for radiotherapy applications

BACKGROUND

Cardiac applications in radiation therapy are rapidly expanding including magnetic resonance guided radiation therapy (MRgRT) for real-time gating for targeting and avoidance near the heart or treating ventricular tachycardia (VT).

PURPOSE

This work describes the development and implementation of a novel multi-modality and magnetic resonance (MR)-compatible cardiac phantom.

METHODS

The patient-informed 3D model was derived from manual contouring of a contrast-enhanced Coronary Computed Tomography Angiography scan, exported as a Stereolithography model, then post-processed to simulate female heart with an average volume. The model was 3D-printed using Elastic50A to provide MR contrast to water background. Two rigid acrylic modules containing cardiac structures were designed and assembled, retrofitting to an MR-safe programmable motor to supply cardiac and respiratory motion in superior-inferior directions. One module contained a cavity for an ion chamber (IC), and the other was equipped with multiple interchangeable cavities for plastic scintillation detectors (PSDs). Images were acquired on a 0.35 T MR-linac for validation of phantom geometry, motion, and simulated online treatment planning and delivery. Three motion profiles were prescribed: patient-derived cardiac (sine waveform, 4.3 mm peak-to-peak, 60 beats/min), respiratory (cos4 waveform, 30 mm peak-to-peak, 12 breaths/min), and a superposition of cardiac (sine waveform, 4 mm peak-to-peak, 70 beats/min) and respiratory (cos4 waveform, 24 mm peak-to-peak, 12 breaths/min). The amplitude of the motion profiles was evaluated from sagittal cine images at eight frames/s with a resolution of 2.4 mm × 2.4 mm. Gated dosimetry experiments were performed using the two module configurations for calculating dose relative to stationary. A CT-based VT treatment plan was delivered twice under cone-beam CT guidance and cumulative stationary doses to multi-point PSDs were evaluated.

RESULTS

No artifacts were observed on any images acquired during phantom operation. Phantom excursions measured 49.3 ± 25.8%/66.9 ± 14.0%, 97.0 ± 2.2%/96.4 ± 1.7%, and 90.4 ± 4.8%/89.3 ± 3.5% of prescription for cardiac, respiratory, and cardio-respiratory motion profiles for the 2-chamber (PSD) and 12-substructure (IC) phantom modules respectively. In the gated experiments, the cumulative dose was <2% from expected using the IC module. Real-time dose measured for the PSDs at 10 Hz acquisition rate demonstrated the ability to detect the dosimetric consequences of cardiac, respiratory, and cardio-respiratory motion when sampling of different locations during a single delivery, and the stability of our phantom dosimetric results over repeated cycles for the high dose and high gradient regions. For the VT delivery, high dose PSD was <1% from expected (5-6 cGy deviation of 5.9 Gy/fraction) and high gradient/low dose regions had deviations <3.6% (6.3 cGy less than expected 1.73 Gy/fraction).

CONCLUSIONS

A novel multi-modality modular heart phantom was designed, constructed, and used for gated radiotherapy experiments on a 0.35 T MR-linac. Our phantom was capable of mimicking cardiac, cardio-respiratory, and respiratory motion while performing dosimetric evaluations of gated procedures using IC and PSD configurations. Time-resolved PSDs with small sensitive volumes appear promising for low-amplitude/high-frequency motion and multi-point data acquisition for advanced dosimetric capabilities. Illustrating VT planning and delivery further expands our phantom to address the unmet needs of cardiac applications in radiotherapy.

Evaluating stereotactic accuracy with patient-specific MRI distortion corrections for frame-based radiosurgery

PURPOSE

This study examines how MRI distortions affect frame-based SRS treatments and assesses the need for clinical distortion corrections.

METHODS

The study included 18 patients with 80 total brain targets treated using frame-based radiosurgery. Distortion within patients' MRIs were corrected using Cranial Distortion Correction (CDC) software, which utilizes the patient's CT to alter planning MRIs to reduce inherent intra-cranial distortion. Distortion was evaluated by comparing the original planning target volumes (PTVORIG) to targets contoured on corrected MRIs (PTVCORR). To provide an internal control, targets were also re-contoured on uncorrected (PTVRECON) MRIs. Additional analysis was done to assess if 1 mm expansions to PTVORIG targets would compensate for patient-specific distortions. Changes in target volumes, DICE and JACCARD similarity coefficients, minimum PTV dose (Dmin), dose to 95% of the PTV (D95%), and normal tissue receiving 12 Gy (V12Gy), 10 Gy (V10Gy), and 5 Gy (V5Gy) were calculated and evaluated. Student's t-tests were used to determine if changes in PTVCORR were significantly different than intra-contouring variability quantified by PTVRECON.

RESULTS

PTVRECON and PTVCORR relative changes in volume were 6.19% ± 10.95% and 1.48% ± 32.92%. PTVRECON and PTVCORR similarity coefficients were 0.90 ± 0.08 and 0.73 ± 0.16 for DICE and 0.82 ± 0.12 and 0.60 ± 0.18 for JACCARD. PTVRECON and PTVCORR changes in Dmin were -0.88% ± 8.77% and -12.9 ± 17.3%. PTVRECON and PTVCORR changes in D95% were -0.34% ± 5.89 and -8.68% ± 13.21%. The 1 mm expanded PTVORIG targets did not entirely cover 14 of the 80 PTVCORR targets. Normal tissue changes (V12Gy, V10Gy, V5Gy) calculated with PTVRECON were (-0.09% ± 7.39%, -0.38% ± 5.67%, -0.08% ± 2.04%) and PTVCORR were (-2.14% ± 7.34%, -1.42% ± 5.45%, -0.61% ± 1.93%). Except for V10Gy, all PTVCORR changes were significantly different (p < 0.05) than PTVRECON.

CONCLUSION

MRIs used for SRS target delineation exhibit notable geometric distortions that may compromise optimal dosimetric accuracy. A uniform 1 mm expansion may result in geometric misses; however, the CDC algorithm provides a feasible solution for rectifying distortions, thereby enhancing treatment precision.

Authentic assessment in medical education: exploring AI integration and student-as-partners collaboration

BACKGROUND

Traditional assessments often lack flexibility, personalized feedback, real-world applicability, and the ability to measure skills beyond rote memorization. These may not adequately accommodate diverse learning styles and preferences, nor do they always foster critical thinking or creativity. The inclusion of Artificial Intelligence (AI), especially Generative Pre-trained Transformers, in medical education marks a significant shift, offering both exciting opportunities and notable challenges for authentic assessment practices. Various fields, including anatomy, physiology, pharmacy, dentistry, and pathology, are anticipated to employ the metaverse for authentic assessments increasingly. This innovative approach will likely enable students to engage in immersive, project-based learning experiences, facilitating interdisciplinary collaboration and providing a platform for real-world application of knowledge and skills.

METHODS

This commentary paper explores how AI, authentic assessment, and Student-as-Partners (SaP) methodologies can work together to reshape assessment practices in medical education.

RESULTS

The paper provides practical insights into effectively utilizing AI tools to create authentic assessments, offering educators actionable guidance to enhance their teaching practices. It also addresses the challenges and ethical considerations inherent in implementing AI-driven assessments, emphasizing the need for responsible and inclusive practices within medical education. Advocating for a collaborative approach between AI and SaP methodologies, the commentary proposes a robust plan to ensure ethical use while upholding academic integrity.

CONCLUSION

Through navigating emerging assessment paradigms and promoting genuine evaluation of medical knowledge and proficiency, this collaborative effort aims to elevate the quality of medical education and better prepare learners for the complexities of clinical practice.

Effect of backscatter radiation on the occupational eye-lens dose

We quantified the level of backscatter radiation generated from physicians' heads using a phantom. We also evaluated the shielding rate of the protective eyewear and optimal placement of the eye-dedicated dosimeter (skin surface or behind the Pb-eyewear). We performed diagnostic X-rays of two head phantoms: Styrofoam (negligible backscatter radiation) and anthropomorphic (included backscatter radiation). Radiophotoluminescence glass dosimeters were used to measure the eye-lens dose, with or without 0.07-mm Pb-equivalent protective eyewear. We used tube voltages of 50, 65 and 80 kV because the scattered radiation has a lower mean energy than the primary X-ray beam. The backscatter radiation accounted for 17.3-22.3% of the eye-lens dose, with the percentage increasing with increasing tube voltage. Furthermore, the shielding rate of the protective eyewear was overestimated, and the eye-lens dose was underestimated when the eye-dedicated dosimeter was placed behind the protective eyewear. We quantified the backscatter radiation generated from physicians' heads. To account for the effect of backscatter radiation, an anthropomorphic, rather than Styrofoam, phantom should be used. Close contact of the dosimeter with the skin surface is essential for accurate evaluation of backscatter radiation from physician's own heads. To assess the eye-lens dose accurately, the dosimeter should be placed near the eye. If the dosimeter is placed behind the lens of the protective eyewear, we recommend using a backscatter radiation calibration factor of 1.2-1.3.

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA.

Ozone enhances the efficacy of radiation therapy in esophageal cancer

Radioresistance is increasingly developed in esophageal cancer. Increasing radiation sensitivity can reduce the mortality of esophageal cancer. To investigate the effect and mechanism of ozone on the radiotherapy sensitization of esophageal carcinoma. KYSE150 cells were xenografted subcutaneously into nude mice and irradiated with 8 Gy radiation according to different subgroups (sham, radiation, ozone and radiation+ozone group (n = 10 per group)). Half of the mice were used to determine the body weight, tumor size and tumor weight. Half of the mice were used to collect peripheral blood. The serum was centrifuged to detect circulating cell-free DNA (cf-DNA), interleukin-6 (IL-6), interferon-γ (IFN-γ), myeloperoxidase (MPO)-DNA complexes, tumor necrosis factor-α (TNF-α), matrix metalloproteinase-9 (MMP-9) and hypoxia-inducible factor-1α (HIF-1α) using commercial kits. The levels of phosphorylation AMP-activated protein kinase (p-AMPK) and scavenger receptor-A (SR-A) were measured by immunocytochemistry and Western blotting in the tumor tissues of mice. Ozone alone or combined with radiation therapy significantly reduced the body weight, tumor volume and tumor weight of esophageal cancer compared to the sham group. The ELISA results showed that the levels of cf-DNA, IFN-γ, MPO-DNA complexes, TNF-α, IL-6, HIF-1α and MMP-9 in the peripheral blood of mice treated with ozone combined with radiation were significantly lower compared with the radiation group. Ozone, synergistically with radiation, significantly increased the protein expression of p-AMPK and SR-A. Ozone may increase the radiosensitivity of esophageal cancer by inhibiting neutrophil extracellular traps.

DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy

In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity

BACKGROUND

The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined.

PURPOSE

To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI.

MATERIAL AND METHODS

The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity.

RESULTS

The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010).

CONCLUSION

DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

169Yb-based high dose rate intensity modulated brachytherapy for focal treatment of prostate cancer

PURPOSE

This study compares conventional 192Ir-based high dose rate brachytherapy (HDR-BT) with 169Yb-based HDR intensity modulated brachytherapy (IMBT) for focal prostate cancer treatment. Additionally, the study explores the potential to generate less invasive treatment plans with IMBT by reducing the number of catheters needed to achieve acceptable outcomes.

METHODS AND MATERIALS

A retrospective dosimetric study of ten prostate cancer patients initially treated with conventional 192Ir-based HDR-BT and 5-14 catheters was employed. RapidBrachyMCTPS, a Monte Carlo-based treatment planning system was used to calculate and optimize dose distributions. For 169Yb-based HDR IMBT, a custom 169Yb source combined with 0.8 mm thick platinum shields placed inside 6F catheters was used. Furthermore, dose distributions were investigated when iteratively removing catheters for less invasive treatments.

RESULTS

With IMBT, the urethra D10 and D0.1cc decreased on average by 15.89 and 15.65 percentage points (pp) and the rectum V75 and D2cc by 1.53 and 11.54 pp, respectively, compared to the conventional clinical plans. Similar trends were observed when the number of catheters decreased. On average, there was an observed increase in PTV V150 from 2.84 pp with IMBT when utilizing all catheters to 8.83 pp when four catheters were removed. PTV V200 increased from 0.42 to 2.96 pp on average. Hotspots in the body were however lower with IMBT compared to conventional clinical plans.

CONCLUSIONS

169Yb-based HDR IMBT for focal treatment of prostate cancer has the potential to successfully deliver clinically acceptable, less invasive treatment with reduced dose to organs at risk.

Stability analysis of patient-specific 4DCT- and 4DCBCT-based correspondence models

BACKGROUND

Surrogate-based motion compensation in stereotactic body radiation therapy (SBRT) strongly relies on a constant relationship between an external breathing signal and the internal tumor motion over the course of treatment, that is, a stable patient-specific correspondence model.

PURPOSE

This study aims to develop methods for analyzing the stability of correspondence models by integrating planning 4DCT and pretreatment 4D cone-beam computed tomography (4DCBCT) data and assessing the relation to patient-specific clinical parameters.

METHODS

For correspondence modeling, a regression-based approach is applied, correlating patient-specific internal motion (vector fields computed by deformable image registration) and external breathing signals (recorded by Varian's RPM and RGSC system). To analyze correspondence model stability, two complementary methods are proposed. (1) Target volume-based analysis: 4DCBCT-based correspondence models predict clinical target volumes (GTV and internal target volume [ITV]) within the planning 4DCT, which are evaluated by overlap and distance measures (Dice similarity coefficient [DSC]/average symmetric surface distance [ASSD]). (2) System matrix-based analysis: 4DCBCT-based regression models are compared to 4DCT-based models using mean squared difference (MSD) and principal component analysis of the system matrices. Stability analysis results are correlated with clinical parameters. Both methods are applied to a dataset of 214 pretreatment 4DCBCT scans (Varian TrueBeam) from a cohort of 46 lung tumor patients treated with ITV-based SBRT (planning 4DCTs acquired with Siemens AS Open and SOMATOM go.OPEN Pro CT scanners).

RESULTS

Consistent results across the two complementary analysis approaches (Spearman correlation coefficient of0.6 / 0.7 $0.6/ 0.7$ between system matrix-based MSD and GTV-based DSC/ASSD) were observed. Analysis showed that stability was not predominant, with 114/214 fraction-wise models not surpassing a threshold ofD S C > 0.7 $DSC > 0.7$ for the GTV, and only 14/46 patients demonstrating aD S C > 0.7 $DSC > 0.7$ in all fractions. Model stability did not degrade over the course of treatment. The mean GTV-based DSC is0.59 ± 0.26 $0.59\pm 0.26$ (mean ASSD of2.83 ± 3.37 $2.83\pm 3.37$ ) and the respective ITV-based DSC is0.69 ± 0.20 $0.69\pm 0.20$ (mean ASSD of2.35 ± 1.81 $2.35\pm 1.81$ ). The clinical parameters showed a strong correlation between smaller tumor motion ranges and increased stability.

CONCLUSIONS

The proposed methods identify patients with unstable correspondence models prior to each treatment fraction, serving as direct indicators for the necessity of replanning and adaptive treatment approaches to account for internal-external motion variations throughout the course of treatment.

Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study

Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-environment interaction in AUD remain largely unknown. In this proof-of-concept study, we showed, for the first time, the neuroepigenetic biomarker capability of non-invasive imaging of class I histone deacetylase (HDAC) epigenetic enzymes in the in vivo brain for classifying AUD patients from healthy controls using a machine learning approach in the context of precision diagnosis. Eleven AUD patients and 16 age- and sex-matched healthy controls completed a simultaneous positron emission tomography-magnetic resonance (PET/MR) scan with the HDAC-binding radiotracer [11C]Martinostat. Our results showed lower HDAC expression in the anterior cingulate region in AUD. Furthermore, by applying a genetic algorithm feature selection, we identified five particular brain regions whose combined [11C]Martinostat relative standard uptake value (SUVR) features could reliably classify AUD vs. controls. We validate their promising classification reliability using a support vector machine classifier. These findings inform the potential of in vivo HDAC imaging biomarkers coupled with machine learning tools in the objective diagnosis and molecular translation of AUD that could complement the current diagnostic and statistical manual of mental disorders (DSM)-based intervention to propel precision medicine forward.

Deep learning-based voxel sampling for particle therapy treatment planning

Objective.Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency.Approach.Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based onP-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels.Main results.On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection.Significance.The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.

Evaluation of the measurement accuracy and uncertainty of a solid-state detector under diagnostic x-ray beam conditions

OBJECTIVE

An accurate measurement of x-ray beams is expected to reduce the uncertainties associated with estimating radiation risk to patients in clinical settings. To perform assessment tasks based on the readings of a solid-state detector (SSD) using semiconductor technology, the characteristics of the detector should be elucidated. In this study, we evaluated the measurement accuracy of a new SSD under diagnostic x-ray beam conditions in terms of air kerma, tube voltage, and half-value layer (HVL). The performance of the SSD was then compared with those of reference instruments.

METHODS

The tube voltage was varied within the range of 50-120 kV in steps of 10 kV and the thickness and materials of additional filters were concurrently changed (several combinations were tested). In addition, the dose rate and energy dependence of the SSD were also investigated. These effects were analyzed based on statistical significance tests. Furthermore, the expanded uncertainties in the series of measurements were meticulously calculated.

RESULTS

The results showed average relative differences of -3.26 ± 1.33%, 0.44 ± 1.01%, and -2.60 ± 3.31% for air kerma, tube voltage, and HVL, respectively. Furthermore, air kerma did not exhibit any dependence on dose rate and energy, in contrast to tube voltage and HVL measurements.

CONCLUSION

The measurement values of the SSD fall within the acceptable range of uncertainty, highlighting its measurement accuracy and reliability. Furthermore, based on the characteristics elucidated by this study, valuable insights are provided concerning the assurance of appropriate measurement values in clinical settings.

Evaluation of knowledge-based planning models for male pelvic CBCT-based online adaptive radiotherapy on conventional linear accelerators

PURPOSE

To assess the practicality of employing a commercial knowledge-based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone-beam computed tomography (iCBCT) datasets.

METHODS AND MATERIALS

Intact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose-volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan-generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow.

RESULTS

Model testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09 ± 02:06 (intact) and 07:12 ± 01:04 (bed).

CONCLUSION

This study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Objective. Photon-counting micro-computed tomography (micro-CT) is a major advance in small animal preclinical imaging. Small molecule- and nanoparticle-based contrast agents have been widely used to enable the differentiation of liver tumors from surrounding tissues using photon-counting micro-CT. However, there is a notable gap in the application of these market-available agents to the imaging of breast and ovarian tumors using photon-counting micro-CT. Herein, we have used photon-counting micro-CT to determine the effectiveness of these contrast agents in differentiating ovarian and breast tumor xenografts in live, intact mice.Approach. Nude mice carrying different types of breast and ovarian tumor xenografts (AU565, MDA-MB-231 and SKOV-3 human cancer cells) were injected with ISOVUE-370 (a small molecule-based agent) or Exitron Nano 12000 (a nanoparticle-based agent) and subjected to photon-counting micro-CT. To improve tumor visualization using photon-counting micro-CT, we developed a novel color visualization method, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand.Main results. Ourin vivoexperiments confirm the effectiveness of this color visualization approach, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggest that Exitron Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 delivers a more comprehensive tumor enhancement but with significant variance between scans due to its short blood half-time. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based contrast agent (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemical analysis.Significance. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor progression and response to treatments.

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET

BACKGROUND

Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.

METHODS

Clinical uEXPLORER total-body PET/CT datasets of [18F]FDG (N = 52), [18F]FAPI (N = 46) and [68Ga]FAPI (N = 60) were retrospectively enrolled in this study. We developed an improved 3D conditional generative adversarial network (cGAN) to directly estimate attenuation and scatter-corrected PET images from non-attenuation and scatter-corrected (NASC) PET images. The feasibility of the proposed 3D cGAN-based ASC was validated using four training strategies: (1) Paired 3D NASC and CT-ASC PET images from three tracers were pooled into one centralized server (CZ-ASC). (2) Paired 3D NASC and CT-ASC PET images from each tracer were individually used (DL-ASC). (3) Paired NASC and CT-ASC PET images from one tracer ([18F]FDG) were used to train the networks, while the other two tracers were used for testing without fine-tuning (NFT-ASC). (4) The pre-trained networks of (3) were fine-tuned with two other tracers individually (FT-ASC). We trained all networks in fivefold cross-validation. The performance of all ASC methods was evaluated by qualitative and quantitative metrics using CT-ASC as the reference.

RESULTS

CZ-ASC, DL-ASC and FT-ASC showed comparable visual quality with CT-ASC for all tracers. CZ-ASC and DL-ASC resulted in a normalized mean absolute error (NMAE) of 8.51 ± 7.32% versus 7.36 ± 6.77% (p < 0.05), outperforming NASC (p < 0.0001) in [18F]FDG dataset. CZ-ASC, FT-ASC and DL-ASC led to NMAE of 6.44 ± 7.02%, 6.55 ± 5.89%, and 7.25 ± 6.33% in [18F]FAPI dataset, and NMAE of 5.53 ± 3.99%, 5.60 ± 4.02%, and 5.68 ± 4.12% in [68Ga]FAPI dataset, respectively. CZ-ASC, FT-ASC and DL-ASC were superior to NASC (p < 0.0001) and NFT-ASC (p < 0.0001) in terms of NMAE results.

CONCLUSIONS

CZ-ASC, DL-ASC and FT-ASC demonstrated the feasibility of providing accurate and robust ASC for multi-tracer total-body PET, thereby reducing the radiation hazards to patients from redundant CT examinations. CZ-ASC and FT-ASC could outperform DL-ASC for cross-tracer total-body PET AC.

Adaptive radiotherapy for muscle invasive bladder cancer: a retrospective audit of two bladder filling protocols

BACKGROUND

Radical radiotherapy for muscle-invasive bladder cancer (MIBC) is challenging due to large variations in bladder shape, size and volume during treatment, with drinking protocols often employed to mitigate geometric uncertainties. Utilising adaptive radiotherapy together with CBCT imaging to select a treatment plan that best fits the bladder target and reduce normal tissue irradiation is an attractive option to compensate for anatomical changes. The aim of this retrospective study was to compare a bladder empty (BE) protocol to a bladder filling (BF) protocol with regards to variations in target volumes, plan of the day (PoD) selection and plan dosimetry throughout treatment.

METHODS

Forty patients were included in the study; twenty were treated with a BE protocol and twenty with a BF protocol to a total prescribed dose of 55 Gy in 20 fractions. Small, medium and large bladder plans were generated using three different CTV to PTV margins. Bladder (CTV) volumes were delineated on planning CTs and online pre-treatment CBCTs. Differences in CTV volumes throughout treatment, plan selection, PTV volumes and resulting dose metrics were compared for both protocols.

RESULTS

Mean bladder volume differed significantly on both the planning CTs and online pre-treatment CBCTs between the protocols (p < 0.05). Significant differences in bladder volumes were observed between the planning CT and pre-treatment CBCTs for BF (p < 0.05) but not for BE (p = 0.11). Both protocols saw a significant decrease in bladder volume between first and final treatment fractions (p < 0.05). Medium plans were preferentially selected for BE whilst when using the BF protocol the small plan was chosen most frequently. With no significant change to PTV coverage between the protocols, the volume of body receiving 25.0-45.8 Gy was found to be significantly smaller for BE patients (p < 0.05).

CONCLUSIONS

This work provides evidence in favour of a BE protocol compared to a BF protocol for radical radiotherapy for MIBC. The smaller treatment volumes observed in the BE protocol led to reduced OAR and total body doses and were also observed to be more consistent throughout the treatment course. These results highlight improvements in dosimetry for patients who undergo a BE protocol for MIBC.

Comparison of Image Quality and Radiation Dose in Pediatric Temporal Bone CT Using Photon-Counting Detector CT and Energy-Integrating Detector CT

BACKGROUND AND PURPOSE

Currently, there is a lack of research directly comparing photon-counting detector CT (PCD-CT) and energy-integrating detector CT (EID-CT) in pediatric temporal bone CT imaging. The purpose of this study was to compare the image quality and radiation dose of temporal bone CT scans in pediatric patients acquired with PCD-CT and EID-CT.

MATERIALS AND METHODS

The retrospective study included a total of 110 pediatric temporal bone CT scans (PCD-CT, n = 52; EID-CT, n = 58). Two independent readers evaluated the spatial resolution of 4 anatomic structures (tympanic membrane, incudostapedial joint, stapedial crura, and cochlear modiolus) and overall image quality by using a 4-point scale. Interreader agreement was assessed. Dose-length product for each CT was compared, and subgroup analyses were performed based on age (younger than 3 years, 3-5 years, 6-11 years, and 12 years and above).

RESULTS

PCD-CT demonstrated statistically significantly higher scores than EID-CT for all items (tympanic membrane, 2.9 versus 2.4; incudostapedial joint, 3.6 versus 2.6; stapedial crura, 3.2 versus 2.4; cochlear modiolus, 3.4 versus 2.8; overall image quality, 3.6 versus 2.8; P < .05). Interreader agreement ranged from good to excellent (interclass correlation coefficients, 0.6-0.81). PCD-CT exhibited a 43% dose reduction compared with EID-CT, with a particularly substantial reduction of over 70% in the subgroups of children younger than 6 years.

CONCLUSIONS

PCD temporal bone CT achieves significantly superior imaging quality at a lower radiation dose compared with EID-CT.

A review of 177Lu dosimetry workflows: how to reduce the imaging workloads?

177 Lu radiopharmaceutical therapy is a standardized systemic treatment, with a typical dose of 7.4 GBq per injection, but its response varies from patient to patient. Dosimetry provides the opportunity to personalize treatment, but it requires multiple post-injection images to monitor the radiopharmaceutical's biodistribution over time. This imposes an additional imaging burden on centers with limited resources. This review explores methods to lessen this burden by optimizing acquisition types and minimizing the number and duration of imaging sessions. After summarizing the different steps of dosimetry and providing examples of dosimetric workflows for177 Lu -DOTATATE and177 Lu -PSMA, we examine dosimetric workflows based on a reduced number of acquisitions, or even just one. We provide a non-exhaustive description of simplified methods and their assumptions, as well as their limitations. Next, we detail the specificities of each normal tissue and tumors, before reviewing dose-response relationships in the literature. In conclusion, we will discuss the current limitations of dosimetric workflows and propose avenues for improvement.

Deep learning reconstruction for coronary CT angiography in patients with origin anomaly, stent or bypass graft

PURPOSE

To develop and validate a deep learning (DL)-model for automatic reconstruction for coronary CT angiography (CCTA) in patients with origin anomaly, stent or bypass graft.

MATERIAL AND METHODS

In this retrospective study, a DL model for automatic CCTA reconstruction was developed with training and validation sets from 6063 and 1962 patients. The algorithm was evaluated on an independent external test set of 812 patients (357 with origin anomaly or revascularization, 455 without). The image quality of DL reconstruction and manual reconstruction (using dedicated cardiac reconstruction software provided by CT vendors) was compared using a 5-point scale. The successful reconstruction rates and post-processing time for two methods were recorded.

RESULTS

In the external test set, 812 patients (mean age, 64.0 ± 11.6, 100 with origin anomalies, 152 with stents, 105 with bypass grafts) were evaluated. The successful rates for automatic reconstruction were 100% (455/455), 97% (97/100), 100% (152/152), and 76.2% (80/105) in patients with native vessel, origin anomaly, stent, and bypass graft, respectively. The image quality scores were significantly higher for DL reconstruction than those for manual approach in all subgroups (4 vs. 3 for native vessel, 4 vs. 4 for origin anomaly, 4 vs. 3 for stent and 4 vs. 3 for bypass graft, all p < 0.001). The overall post-processing time was remarkably reduced for DL reconstruction compared to manual method (11 s vs. 465 s, p < 0.001).

CONCLUSIONS

The developed DL model enabled accurate automatic CCTA reconstruction of bypass graft, stent and origin anomaly. It significantly reduced post-processing time and improved clinical workflow.

Comparative post-therapeutic dosimetry between 2D planar-based and hybrid-based methods for personalized Lu-177 treatment

PURPOSE

This study aims to compare the calculated absorbed dose in target organs and tumors obtained using the different imaging protocols and the calculation methodologies implemented by HERMES HybridViewer dosimetry software for 177Lu-PSMA I&T and 177Lu-DOTATATE therapy.

METHODS

Multiple time-point whole-body planar images and one SPECT/CT image were acquired from 18 patients including 177Lu-PSMA I&T (13 patients) and 177Lu-DOTATATE treatment (5 patients) after administration of 3.80-8.58 GBq injected activity. The regions of interest were drawn in the whole body, kidneys, liver, urinary bladder, salivary glands, and tumors to determine the time-integrated activity (TIA) in source organs. Absorbed doses in target organs were calculated according to the Medical Internal Radiation Dose (MIRD) scheme using the HERMES HybridViewer dosimetry integrated with OLINDA/EXM V.2.1 that utilizes the non-uniform rational B-splines (NURBS) for computational digital phantom.

RESULTS

The planar-based dosimetry showed a higher dose per injected activity compared to the hybrid-based dosimetry, primarily due to organ overlap. The highest difference in absorbed dose between the imaging scenarios was observed in the spleen with a variation of up to 51.6%, while the difference for other target organs and tumors was less than 40%.

CONCLUSION

The dosimetry calculation derived from the 2D planar-based method consistently demonstrates a significantly higher absorbed dose in organs and tumors compared with the hybrid-based method. However, the hybrid method outperforms the planar method in terms of tumor visualization and overlap-free organ delineation.

Prediction of dose distributions for non-small cell lung cancer patients using MHA-ResUNet

BACKGROUND

The current level of automation in the production of radiotherapy plans for lung cancer patients is relatively low. With the development of artificial intelligence, it has become a reality to use neural networks to predict dose distributions and provide assistance for radiation therapy planning. However, due to the significant individual variability in the distribution of non-small cell lung cancer (NSCLC) planning target volume (PTV) and the complex spatial relationships between the PTV and organs at risk (OARs), there is still a lack of a high-precision dose prediction network tailored to the characteristics of NSCLC.

PURPOSE

To assist in the development of volumetric modulated arc therapy (VMAT) plans for non-small cell lung cancer patients, a deep neural network is proposed to predict high-precision dose distribution.

METHODS

This study has developed a network called MHA-ResUNet, which combines a large-kernel dilated convolution module and multi-head attention (MHA) modules. The network was trained based on 80 VMAT plans of NSCLC patients. CT images, PTV, and OARs were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks' performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics.

RESULTS

The MAE between the predicted dose distribution and the manually planned dose distribution within the PTV is 1.43 Gy, and the D95 error is less than 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA-ResUNet is the smallest in PTV and OARs.

CONCLUSIONS

The proposed MHA-ResUNet network improves the receptive field and filters the shallow features to learn the relative spatial relation between the PTV and the OARs, enabling accurate prediction of dose distributions in NSCLC patients undergoing VMAT radiotherapy.

kV reference dosimetry in Australia and New Zealand: Survey results and trends

PURPOSE

To assess the number of radiotherapy kilovoltage (kV) units in service, their clinical utilization, and methodology and equipment used for absorbed dose determination across Australia and New Zealand.

METHODS

A survey was sent to 61 Australian and New Zealand radiotherapy providers in the second half of 2023.

RESULTS

Fifty-seven responses were received, with 43 departments having kV units and providing beam quality data for 185 therapeutic kV beams 20-300 kVp. Percentage depth dose curves were compared between five clinical beams with 100 kVp and 2.13-6.28 mm Aluminum half value layers (HVLs), demonstrating large differences that can occur between beams with the same kVp. Eighteen departments provided clinical utilization data for their kV units, with a total of 4458 treatment courses and their corresponding kVp reported. All departments complied with national and international recommendations with respect to the equipment used for reference dosimetry of kV beams; 77% of ionization chambers used for absorbed dose determination were of Farmer-type, with the remaining 23% being plane parallel soft x-ray chambers. Methods of derivation of air-kerma calibration factors varied, with 73% of respondents using a draft document disseminated by the Australian Primary Standards laboratory, 23% using HVL alone, and 6% using other methods.

CONCLUSIONS

The results of this survey provide a snapshot of kilovoltage radiation therapy use and the number of kV units across Australia and New Zealand. This data can be used as a point of reference for future investigations into clinical utilization and reference dosimetry methods across Australia and New Zealand or for comparisons with other countries, facilitating standardization of reference dosimetry practice for kilovoltage units.

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach

Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100μm, a thickness of 500μm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.

Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set

PURPOSE

This study aims to evaluate the effectiveness of advanced deep learning models, specifically YOLOv8 and EfficientNetV2, in detecting meniscal tears on magnetic resonance imaging (MRI) using a relatively small data set.

METHOD

Our data set consisted of MRI studies from 642 knees-two orthopaedic surgeons labelled and annotated the MR images. The training pipeline included MRI scans of these knees. It was divided into two stages: initially, a deep learning algorithm called YOLO was employed to identify the meniscus location, and subsequently, the EfficientNetV2 deep learning architecture was utilized to detect meniscal tears. A concise report indicating the location and detection of a torn meniscus is provided at the end.

RESULT

The YOLOv8 model achieved mean average precision at 50% threshold (mAP@50) scores of 0.98 in the sagittal view and 0.985 in the coronal view. Similarly, the EfficientNetV2 model obtained area under the curve scores of 0.97 and 0.98 in the sagittal and coronal views, respectively. These outstanding results demonstrate exceptional performance in meniscus localization and tear detection.

CONCLUSION

Despite a relatively small data set, state-of-the-art models like YOLOv8 and EfficientNetV2 yielded promising results. This artificial intelligence system enhances meniscal injury diagnosis by generating instant structured reports, facilitating faster image interpretation and reducing physician workload.

LEVEL OF EVIDENCE

Level III.

Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks

BACKGROUND

X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is inefficient and inconsistent, especially when large amounts of image data are being evaluated. Thus, subjective assessment cannot meet current QC needs.

PURPOSE

To meet current QC needs and improve the efficiency of image quality assessment, a complete set of quality assessment criteria must be established and implemented using artificial intelligence (AI) technology. Therefore, we proposed a multi-criteria AI system for automatically assessing the image quality of knee radiographs.

METHODS

A knee radiograph QC knowledge graph containing 16 "acquisition technique" labels representing 16 image quality defects and five "clarity" labels representing five grades of clarity were developed. Ten radiographic technologists conducted three rounds of QC based on this graph. The single-person QC results were denoted as QC1 and QC2, and the multi-person QC results were denoted as QC3. Each technologist labeled each image only once. The ResNet model structure was then used to simultaneously perform classification (detection of image quality defects) and regression (output of a clarity score) tasks to construct an image QC system. The QC3 results, comprising 4324 anteroposterior and lateral knee radiographs, were used for model training (70% of the images), validation (10%), and testing (20%). The 865 test set data were used to evaluate the effectiveness of the AI model, and an AI QC result, QC4, was automatically generated by the model after training. Finally, using a double-blind method, the senior QC expert reviewed the final QC results of the test set with reference to the results QC3 and QC4 and used them as a reference standard to evaluate the performance of the model. The precision and mean absolute error (MAE) were used to evaluate the quality of all the labels in relation to the reference standard.

RESULTS

For the 16 "acquisition technique" features, QC4 exhibited the highest weighted average precision (98.42% ± 0.81%), followed by QC3 (91.39% ± 1.35%), QC2 (87.84% ± 1.68%), and QC1 (87.35% ± 1.71%). For the image clarity features, the MAEs between QC1, QC2, QC3, and QC4 and the reference standard were 0.508 ± 0.021, 0.475 ± 0.019, 0.237 ± 0.016, and 0.303 ± 0.018, respectively.

CONCLUSIONS

The experimental results show that our automated quality assessment system performed well in classifying the acquisition technique used for knee radiographs. The image clarity quality evaluation accuracy of the model must be further improved but is generally close to that of radiographic technologists. Intelligent QC methods using knowledge graphs and convolutional neural networks have the potential for clinical applications.

Organ and tumor dosimetry including method simplification for [177Lu]Lu-PSMA-I&T for treatment of metastatic castration resistant prostate cancer

BACKGROUND

Internal dosimetry in individual patients is essential for safe and effective radioligand therapy. Multiple time point imaging for accurate dosimetry is time consuming and hence can be demanding for nuclear medicine departments as well as patients. The objectives of this study were (1) to assess absorbed doses to organs at risk and tumor lesions for [177Lu]Lu-PSMA-I&T using whole body SPECT imaging and (2) to investigate possible simplified dosimetry protocols.

METHODS

This study included 16 patients each treated with 4 cycles of [177Lu]Lu-PSMA-I&T. They underwent quantitative whole body SPECT/CT imaging (3 bed positions) at four time points (TP) comprising 2 h, 24 h, 48 h and 72-168 h post-injection (p.i.). Full 3D dosimetry (reference method) was performed for all patients and dose cycles for organs at risk (kidneys, parotid glands and submandibular glands) and up to ten tumor lesions per patient (resulting in 90 lesions overall). The simplified dosimetry methods (SM) included (1) generating time activity curves for subsequent cycles using a single TP of imaging applying the kinetics of dose cycle 1, and for organs at risk also (2) simple extrapolation from dose cycle 1 and (3) from both, dose cycle 1 and 2.

RESULTS

Normalized absorbed doses were 0.71 ± 0.32 mGy/MBq, 0.28 ± 0.12 mGy/MBq and 0.22 ± 0.08 mGy/MBq for kidneys, parotid glands and submandibular glands, respectively. Tumor doses decreased from 3.86 ± 3.38 mGy/MBq in dose cycle 1 to 2.01 ± 2.65 mGy/MBq in dose cycle 4. Compared to the full dosimetry approach the SM 1 using single TP imaging at 48 h p.i. resulted in the most accurate and precise results for the organs at risk in terms of absorbed doses per cycle and total cumulated dose. For tumor lesions better results were achieved using the fourth TP (≥ 72 h p.i.).

CONCLUSION

Simplification of safety dosimetry protocols is possible for [177Lu]Lu-PSMA-I&T therapy. If tumor dosimetry is of interest a later imaging TP (≥ 72 h p.i.) should be used/added to account for the slower kinetics of tumors compared to organs at risk.

Parallel comparison of ocular metrics in non-human primates with high myopia by LS900, ultrasonography and MRI-based 3D reconstruction

We investigate the ocular dimensions and shape by using Lenstar900 (LS900), A-scan ultrasonography, and Magnetic Resonance Imaging (MRI) in highly myopic Macaca fascicularis. The ocular dimensions data of LS900, A-scan ultrasonography and MRI was assessed from 8 eyes (4 adult male cynomolgus macaque) with extremely high myopia (≤-1000DS) and compared by means of coefficients of concordance and 95% limits of agreement. Multiple regression analysis was performed to explore the associations between ocular biometry, volume, refraction and inter-instrument discrepancies. Test-retest reliability of three measurements of ocular parameters at two time points was almost equal (intraclass correlation = 0.831 to 1.000). The parallel-forms reliability of three measurements was strong for vitreous chamber depth (VCD) (coefficient of concordance = 0.919 to 0.981), moderate for axial length (AL) (coefficient of concordance = 0.486 to 0.981), and weak for anterior chamber depth (ACD) (coefficient of concordance = 0.267 to 0.621) and lens thickness (LT) (coefficient of concordance = 0.035 to 0.631). The LS900 and MRI systematically underestimated the ACD and LT comparing to A-scan ultrasonography (P < 0.05). Notably, the average AL on LS900 displayed a significant correlation with those on MRI (r = 0.978, P < 0.001) and A-scan ultrasonography (r = 0.990, P < 0.001). Almost 4/5 eyeballs were prolate. The mean eyeball volume positively correlated with AL (r = 0.782, P = 0.022), the width (r = 0.945, P = 0.000), and the length (r = 0.782, P = 0.022) of eyeball, while negatively correlated with SER (r = -0.901, P = 0.000). In conclusion, there was a high inter-instrument concordance for VCD with LS900, A-scan ultrasonography and MRI, while ACD and LT were underestimated with LS900 compared to A-scan ultrasonography, and the LS900 and A-scan ultrasonography could reliably measure the AL. MRI further revealed an equatorial globe shape in extremely myopic non-human primates.

Dense Perovskite Thick Film Enabled by Saturated Solution Filling for Sensitive X-ray Detection and Imaging

Thick polycrystalline perovskite films synthesized by using solution processes show great potential in X-ray detection applications. However, due to the evaporation of the solvent, many pinholes and defects appear in the thick films, which deteriorate their optoelectronic properties and diminish their X-ray detection performance. Therefore, the preparation of large area and dense perovskite thick films is desired. Herein, we propose an effective strategy of filling the pores with a saturated precursor solution. By adding the saturated perovskite solution to the polycrystalline perovskite thick film, the original perovskite film will not be destroyed because of the solution-solute equilibrium relationship. Instead, it promotes in situ crystal growth within the thick film during the annealing process. The loosely packed grains in the original thick perovskite film are connected, and the pores and defects are partially filled and fixed. Finally, a much denser perovskite thick film with improved optoelectronic properties has been obtained. The optimized thick film exhibits an X-ray sensitivity of 1616.01 μC Gyair-1 cm-2 under an electric field of 44.44 V mm-1 and a low detection limit of 28.64 nGyair s-1 under an electric field of 22.22 V mm-1. These values exceed the 323.86 μC Gyair-1 cm-2 and 40.52 nGyair s-1 of the pristine perovskite thick film measured under the same conditions. The optimized thick film also shows promising working stability and X-ray imaging capability.

Structure-aware diffusion for low-dose CT imaging

Reducing the radiation dose leads to the x-ray computed tomography (CT) images suffering from heavy noise and artifacts, which inevitably interferes with the subsequent clinic diagnostic and analysis. Leading works have explored diffusion models for low-dose CT imaging to avoid the structure degeneration and blurring effects of previous deep denoising models. However, most of them always begin their generative processes with Gaussian noise, which has little or no structure priors of the clean data distribution, thereby leading to long-time inference and unpleasant reconstruction quality. To alleviate these problems, this paper presents a Structure-Aware Diffusion model (SAD), an end-to-end self-guided learning framework for high-fidelity CT image reconstruction. First, SAD builds a nonlinear diffusion bridge between clean and degraded data distributions, which could directly learn the implicit physical degradation prior from observed measurements. Second, SAD integrates the prompt learning mechanism and implicit neural representation into the diffusion process, where rich and diverse structure representations extracted by degraded inputs are exploited as prompts, which provides global and local structure priors, to guide CT image reconstruction. Finally, we devise an efficient self-guided diffusion architecture using an iterative updated strategy, which further refines structural prompts during each generative step to drive finer image reconstruction. Extensive experiments on AAPM-Mayo and LoDoPaB-CT datasets demonstrate that our SAD could achieve superior performance in terms of noise removal, structure preservation, and blind-dose generalization, with few generative steps, even one step only.

Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity

Radiomics has traditionally focused on individual tumors, often neglecting the integration of metastatic disease, particularly in patients with non-small cell lung cancer. This study sought to examine intra-patient inter-tumor lesion heterogeneity indices using radiomics, exploring their relevance in metastatic lung adenocarcinoma. Consecutive adults newly diagnosed with metastatic lung adenocarcinoma underwent contrast-enhanced CT scans for lesion segmentation and radiomic feature extraction. Three methods were devised to measure distances between tumor lesion profiles within the same patient in radiomic space: centroid to lesion, lesion to lesion, and primitive to lesion, with subsequent calculation of mean, range, and standard deviation of these distances. Associations between HIs, disease control rate, objective response rate to first-line treatment, and overall survival were explored. The study included 167 patients (median age 62.3 years) between 2016 and 2019, divided randomly into experimental (N = 117,546 lesions) and validation (N = 50,232 tumor lesions) cohorts. Patients without disease control/objective response and with poorer survival consistently systematically exhibited values of all heterogeneity indices. Multivariable analyses revealed that the range of primitive-to-lesion distances was associated with disease control in both cohorts and with objective response in the validation cohort. This metrics showed univariable associations with overall survival in the experimental. In conclusion, we proposed original methods to estimate the intra-patient inter-tumor lesion heterogeneity using radiomics that demonstrated correlations with patient outcomes, shedding light on the clinical implications of inter-metastases heterogeneity. This underscores the potential of radiomics in understanding and potentially predicting treatment response and prognosis in metastatic lung adenocarcinoma patients.

Synthesis, biological evaluation and metadynamics simulations of novel N-methyl β-sheet breaker peptides as inhibitors of Alzheimer's β-amyloid fibrillogenesis

Several scientific evidences report that a central role in the pathogenesis of Alzheimer's disease is played by the deposition of insoluble aggregates of β-amyloid proteins in the brain. Because Aβ is self-assembling, one possible design strategy is to inhibit the aggregation of Aβ peptides using short peptide fragments homologous to the full-length wild-type Aβ protein. In the past years, several studies have reported on the synthesis of some short synthetic peptides called β-sheet breaker peptides (BSBPs). Herein, we present the synthesis of novel (cell-permeable) N-methyl BSBPs, designed based on literature information on the structural key features of BSBPs. Three-dimensional GRID-based pharmacophore peptide screening combined with PT-WTE metadynamics was performed to support the results of the design and microwave-assisted synthesis of peptides 2 and 3 prepared and analyzed for their fibrillogenesis inhibition activity and cytotoxicity. An HR-MS-based cell metabolomic approach highlighted their cell permeability properties.

US & MR/CT Image Fusion with Markerless Skin Registration: A Proof of Concept

This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound acquisition. The system eliminates the need for external physical markers and complex training, making image fusion feasible for physicians with different experience levels. The integrated system involves a portable 3D camera for patient-specific surface acquisition, an electromagnetic tracking system, and US components. The fusion algorithm comprises two main parts: skin segmentation and rigid co-registration, both integrated into the US machine. The co-registration aligns the surface extracted from CT/MR images with the 3D surface acquired by the camera, facilitating rapid and effective fusion. Experimental tests in different settings, validate the system's accuracy, computational efficiency, noise robustness, and operator independence.

Investigation of intra-fractionated range guided adaptive proton therapy: I. On-line PET imaging and range measurement

Objective.Develop a prototype on-line positron emission tomography (PET) scanner and evaluate its capability of on-line imaging and intra-fractionated proton-induced radioactivity range measurement.Approach.Each detector consists of 32 × 32 array of 2 × 2 × 30 mm3Lutetium-Yttrium Oxyorthosilicate scintillators with single-scintillator-end readout through a 20 × 20 array of 3 × 3 mm2Silicon Photomultipliers. The PET can be configurated with a full-ring of 20 detectors for conventional PET imaging or a partial-ring of 18 detectors for on-line imaging and range measurement. All detector-level readout and processing electronics are attached to the backside of the system gantry and their output signals are transferred to a field-programable-gate-array based system electronics and data acquisition that can be placed 2 m away from the gantry. The PET imaging performance and radioactivity range measurement capability were evaluated by both the offline study that placed a radioactive source with known intensity and distribution within a phantom and the online study that irradiated a phantom with proton beams under different radiation and imaging conditions.Main results.The PET has 32 cm diameter and 6.5 cm axial length field-of-view (FOV), ∼2.3-5.0 mm spatial resolution within FOV, 3% sensitivity at the FOV center, 18%-30% energy resolution, and ∼9 ns coincidence time resolution. The offline study shows the PET can determine the shift of distal falloff edge position of a known radioactivity distribution with the accuracy of 0.3 ± 0.3 mm even without attenuation and scatter corrections, and online study shows the PET can measure the shift of proton-induced positron radioactive range with the accuracy of 0.6 ± 0.3 mm from the data acquired with a short-acquisition (60 s) and low-dose (5 MU) proton radiation to a human head phantom.Significance.This study demonstrated the capability of intra-fractionated PET imaging and radioactivity range measurement and will enable the investigation on the feasibility of intra-fractionated, range-shift compensated adaptive proton therapy.

Impact of delivery variations on 3D dose distributions for volumetric modulated arc therapy plans of various complexity

BACKGROUND

Delivery variations during radiotherapy can cause discrepancies between planned and delivered dose distribution. These variations could arise from random and systematic offsets in certain machine parameters or systematic offsets related to the calibration process of the treatment unit.

PURPOSE

The aim of this study was to present a novel simulation-based methodology to evaluate realistic delivery variations in three dimensions (3D). Additionally, we investigated the dosimetric impact of delivery variations for volumetric modulated arc therapy (VMAT) plans for different treatment sites and complexities.

METHODS

Twelve VMAT plans for different treatment sites (prostate-, head & neck-, lung-, and gynecological cancer) were selected. The clinical plan used for the treatment of each patient was reoptimized to create one plan with reduced complexity (i.e., simple plan) and one of higher complexity (i.e., complex plan). This resulted in a total of 36 plans. Delivery variations were simulated by randomly introducing offsets in multi-leaf collimator position, jaw position, gantry angle and collimator angle simultaneously. Twenty simulations were carried out for each of the 36 plans, yielding 720 simulated deliveries. To explore the impact of individual offsets, additional simulations were conducted for each type of offset separately. A 3D dose calculation was performed for each simulation using the same calculation engine as for the clinical plan. Two standard deviations (2SD) of dose were determined for every voxel for 3D-spatial evaluations. The dose variation in certain DVH metrics, that is, D2% and D98% for the clinical target volume and five different DVH metrics for selected organs at risk, was calculated for the twenty simulated deliveries of each plan. For comparison, the effect of delivery variations was assessed by conducting measurements with the Delta4 phantom.

RESULTS

The volume of voxels with 2SD above 1% of the prescribed dose was consistently larger for the complex plans in comparison to their corresponding simple and clinical plans. 2SDs larger than 1% were in many cases, found to accumulate outside the planning target volume. For complex plans, regions with 2SDs larger than 1% were detected also inside the high dose region, exhibiting, on average, a size six times larger volume, than those observed in simple plans. Similar results were found for all treatment sites. Variation in the selected DVH metrics for the simulated deliveries was generally largest for the complex plans with few exceptions. When comparing the 2SD distribution of the measurements with the 2SD distribution from the simulations, the spatial information showed deviations outside the PTV in both simulations and measurements. However, the measured values were, on average, 35% higher for the prostate plans and 10% higher for the head & neck plans compared to the simulated values.

CONCLUSIONS

The presented methodology effectively quantified and localized dose deviations due to delivery offsets. The 3D analysis provided information that was undetectable using the analysis based on DVH metrics. Dosimetric uncertainties due to delivery variations were prominent at the edge of the high-dose region irrespective of treatment site and plan complexity. Dosimetric uncertainties inside the high-dose region was more profound for plans of higher complexity.

Incorporating boron distribution variations in microdosimetric kinetic model-based relative biological effectiveness calculations for boron neutron capture therapy

This study introduces the MKM_B model, an approach derived from the MKM model, designed to evaluate the biological effectiveness of Boron Neutron Capture Therapy (BNCT) in the face of challenges from varying microscopic boron distributions. The model introduces a boron compensation factor, allowing for the assessment of compound Biological Effectiveness (CBE) values for different boron distributions. Utilizing the TOPAS simulation platform, the lineal energy spectrum of particles in BNCT was simulated, and the sensitivity of the MKM_B model to parameter variations and the influence of cell size on the model were thoroughly investigated. The CBE values for 10B-boronphenylalanine (BPA) and 10B-sodium (BSH) were determined to be 3.70 and 1.75, respectively. These calculations were based on using the nucleus radius of 2.5 μm and the cell radius of 5 μm while considering a 50% surviving fraction. It was observed that as cell size decreased, the CBE values for both BPA and BSH increased. Additionally, the model parameter rd was identified as having the most significant impact on CBE, with other parameters showing moderate effects. The development of the MKM_B model enables the accurate prediction of CBE under different boron distributions in BNCT. This model offers a promising approach to optimize treatment planning by providing increased accuracy in biological effectiveness.

Clinical applicability of Linac-integrated CBCT based NIPAM 3D dosimetry: a dual-institutional investigation

Objective.To develop and benchmark a novel 3D dose verification technique consisting of polymer gel dosimetry (PGD) with cone-beam-CT (CBCT) readout through a two-institution study. The technique has potential for wide and robust applicability through reliance on CBCT readout.Approach. Three treatment plans (3-field, TG119-C-shape spine, 4-target SRS) were created by two independent institutions (Institutions A and B). A Varian Truebeam linear accelerator was used to deliver the plans to NIPAM polymer gel dosimeters produced at both institutions using an identical approach. For readout, a slow CBCT scan mode was used to acquire pre- and post-irradiation images of the gel (1 mm slice thickness). Independent gel analysis tools were used to process the PGD images (A: VistaAce software, B: in-house MATLAB code). Comparing planned and measured doses, the analysis involved a combination of 1D line profiles, 2D contour plots, and 3D global gamma maps (criteria ranging between 2%1 mm and 5%2 mm, with a 10% dose threshold).Main results. For all gamma criteria tested, the 3D gamma pass rates were all above 90% for 3-field and 88% for the SRS plan. For the C-shape spine plan, we benchmarked our 2% 2 mm result against previously published work using film analysis (93.4%). For 2%2 mm, 99.4% (Institution A data), and 89.7% (Institution B data) were obtained based on VistaAce software analysis, 83.7% (Institution A data), and 82.9% (Institution B data) based on MATLAB.Significance. The benchmark data demonstrate that when two institutions follow the same rigorous procedures gamma passing rates up to 99%, for 2%2 mm criteria can be achieved for substantively different treatment plans. The use of different software and calibration techniques may have contributed to the variation in the 3D gamma results. By sharing the data across institutions, we observe the gamma passing rate is more consistent within each pipeline, indicating the need for standardized analysis methods.

Preclinical SPECT and PET: Joint EANM and ESMI procedure guideline for implementing an efficient quality control programme

The aim of this guideline is to provide recommendations for the implementation of an effective and efficient quality control (QC) programme for SPECT and PET systems in a preclinical imaging lab. These recommendations aim to strengthen the translational power of preclinical imaging results obtained using preclinical SPECT and PET. As for clinical imaging, reliability, reproducibility, and repeatability are essential when groups of animals are used in a longitudinal imaging experiment. The larger the variability of the imaging endpoint, the more animals are needed to be able to observe statistically significant differences between groups. Therefore, preclinical imaging requires quality control procedures to maintain reliability, reproducibility, and repeatability of imaging procedures, and to ensure the accuracy and precision of SPECT and PET quantification. While the Physics Committee of the European Association of Nuclear Medicine (EANM) has already published excellent procedure guidelines for Routine Quality Control Recommendations for Nuclear Medicine Instrumentation that also includes procedures for small animal PET systems, and important steps have already been made concerning preclinical quality control aspects, this new guideline provides a review and update of these previous guidelines such that guidelines are also adapted to new technological developments.

NRG Oncology and Particle Therapy Co-Operative Group Patterns of Practice Survey and Consensus Recommendations on Pencil-Beam Scanning Proton Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies

Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally fractionated PBSPT because of concerns of amplified uncertainties at the larger dose per fraction. The NRG Oncology and Particle Therapy Cooperative Group Thoracic Subcommittee surveyed proton centers in the United States to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Among other points, the recommendations highlight the need for volumetric image guidance and multiple computed tomography-based robust optimization and robustness tools to minimize further the effect of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.

Establishing Updated Safety Standards for Independent 99mTc-MAA SPECT/CT Treatment Planning in Radioembolization

PURPOSE

Significant improvements within radioembolization imaging and dosimetry permit the development of an accurate and personalized pretreatment plan using technetium 99m-labeled macroaggregated albumin (99mTc-MAA) and single-photon emission computed tomography (SPECT) combined with anatomical CT (SPECT/CT). Despite these potential advantages, the clinical transition to pretreatment protocols with SPECT/CT is hindered by their unknown safety constraints. This study aimed to address this issue by establishing novel dose limits for 99mTc-MAA SPECT/CT to enable quantitative pretreatment planning.

METHODS AND MATERIALS

Stratification criteria to determine images most viable for dosimetry analysis were created from a cohort of 85 patients. SPECT/CT, cone beam CT, and activity calculations derived from the local deposition method were used to create an accurate pretreatment protocol. Planar and SPECT/CT images were compared using linear regression and modified Bland-Altman analyses to convert accepted planar dose limits to SPECT/CT. To validate these new dose limits, activity calculations based on SPECT/CT were compared with those calculated with the body surface area and planar methods for three treatment plans.

RESULTS

A total of 38 of 85 patients were deemed viable for dosimetry analysis. SPECT yielded greater lung shunt fractions (LSFs) than planar imaging when LSFs were <4.89%, whereas SPECT yielded lower LSFs than planar imaging when LSFs were >4.89%. Planar to SPECT/CT dose conversions were 0.76×, 0.70×, and 0.55× for the whole liver, normal liver, and lungs, respectively. Patients with SPECT LSFs ≤4.89% were safely treated with the direct application of planar lung dose limits. Activity calculations with the newly established SPECT/CT dose limits were greater than those of the body surface area method by a median range of 33.1% to 61.9% and were lower than planar-based activity calculations by a median range of 12.5% to 13.7% for the whole liver and by 29.4% to 32.2% for the normal liver.

CONCLUSIONS

This study demonstrated a safe method for translating dose limits from 99mTc-MAA planar imaging to SPECT/CT. A robust pretreatment protocol was further developed guided by the current knowledge in the field. Established SPECT/CT dose limits safely treated 97.5% of patients and permitted the application of independent pretreatment planning with 99mTc-MAA SPECT/CT.

Target Volume Optimization for Localized Prostate Cancer

Historically, the treatment of prostate cancer has required little anatomic information beyond the location of the prostate gland and adjacent seminal vesicles. Radiation therapy has classically been prescribed to the whole prostate due to the high frequency of multifocal cancer in surgical specimens and the inability to localize the precise boundaries of individual tumor foci on imaging. The development of prostate magnetic resonance imaging (MRI) and positron emission tomography (PET) using prostate-specific radiotracers has ushered in an era in which radiation oncologists are able to localize and focally dose-escalate high-risk volumes in the prostate gland. Recent phase III data have demonstrated that incorporating focal dose escalation improves biochemical control without significantly increasing toxicity. However, many questions remain regarding the optimal target volume definition and prescription strategy to implement this practice. In this review we summarize the currently available literature on image-based focal target delineation with MRI and PET. Our review includes a summary of the available data on anatomic patterns of spread to inform clinical judgement for the definition of clinical target volumes. Key knowledge gaps are identified and suggestions for novel implementation strategies are provided.

Lithium inelastic cross-sections and their impact on micro and nano dosimetry of boron neutron capture

Objective.To present a new set of lithium-ion cross-sections for (i) ionization and excitation processes down to 700 eV, and (ii) charge-exchange processes down to 1 keV u-1. To evaluate the impact of the use of these cross-sections on micro a nano dosimetric quantities in the context of boron neutron capture (BNC) applications/techniques.Approach.The Classical Trajectory Monte Carlo method was used to calculate Li ion charge-exchange cross sections in the energy range of 1 keV u-1to 10 MeV u-1. Partial Li ion charge states ionization and excitation cross-sections were calculated using a detailed charge screening factor. The cross-sections were implemented in Geant4-DNA v10.07 and simulations and verified using TOPAS-nBio by calculating stopping power and continuous slowing down approximation (CSDA) range against data from ICRU and SRIM. Further microdosimetric and nanodosimetric calculations were performed to quantify differences against other simulation approaches for low energy Li ions. These calculations were: lineal energy spectra (yf(y) andyd(y)), frequency mean lineal energyyF-, dose mean lineal energyyD-and ionization cluster size distribution analysis. Microdosimetric calculations were compared against a previous MC study that neglected charge-exchange and excitation processes. Nanodosimetric results were compared against pure ionization scaled cross-sections calculations.Main results.Calculated stopping power differences between ICRU and Geant4-DNA decreased from 33.78% to 6.9%. The CSDA range difference decreased from 621% to 34% when compared against SRIM calculations. Geant4-DNA/TOPAS calculated dose mean lineal energy differed by 128% from the previous Monte Carlo. Ionization cluster size frequency distributions for Li ions differed by 76%-344.11% for 21 keV and 2 MeV respectively. With a decrease in theN1within 9% at 10 keV and agreeing after the 100 keV. With the new set of cross-sections being able to better simulate low energy behaviors of Li ions.Significance.This work shows an increase in detail gained from the use of a more complete set of low energy cross-sections which include charge exchange processes. Significant differences to previous simulation results were found at the microdosimetric and nanodosimetric scales that suggest that Li ions cause less ionizations per path length traveled but with more energy deposits. Microdosimetry results suggest that the BNC's contribution to cellular death may be mainly due to alpha particle production when boron-based drugs are distributed in the cellular membrane and beyond and by Li when it is at the cell cytoplasm regions.