Physics in Medicine & Biology

PurposeTo develop and evaluate a novel double bowtie filter integrating a K-edge material layer with a conventional Teflon filter for pediatric spectral computed tomography (CT). The proposed design aims to enhance spectral signal-to-noise ratio (SNR) and spectral separation while maintaining radiation dose levels suitable for pediatric imaging. MethodsA simulation framework was set up and used to model a rapid kVp-switching CT system operating at 70/110 kVp with realistic tube power and geometry constraints. Pediatric phantoms of three sizes (100- 200 mm anterior-posterior width) were used to evaluate performance. Five accessible and safe filter materials-gadolinium (Gd), holmium (Ho), erbium (Er), silver (Ag), and tin (Sn)-were tested in combination with a Teflon bowtie. System performance was quantified using virtual monoenergetic image (VMI) SNR at 40 keV and 70 keV, and the area under the monoenergetic SNR curve (AUMC) as a comprehensive spectral image quality metric. Dose consistency with a traditional Teflon bowtie reference was enforced. ResultsThe Teflon + Gd configuration achieved the highest performance, improving AUMC by 47.5 % on average and up to 56 % for the largest phantom. VMI SNR increased by approximately 49 % at 40 keV and 42 % at 70 keV. ConclusionsThe double-bowtie concept substantially enhances spectral performance. The Teflon + Gd design provides a manufacturable, pediatric-optimized solution adaptable to kVp-switching and other spectral CT architectures, offering improved diagnostic quality at low dose levels.

ObjectiveThe positron range is a fundamental, detector-independent physical limitation to special resolution in positron emission tomography (PET) as it causes a significant blurring of the reconstructed PET images. A major challenge for positron range correction methods is to provide accurate range kernels that inherently incorporate the generally inhomogeneous stopping power, especially at tissue boundaries. In this work, we propose a novel approach to generate accurate three-dimensional (3-D) blurring kernels both in homogenous and heterogeneous media to improve PET spatial resolution. ApproachIn the proposed approach, positron energy deposition was approximately tracked along straight paths, depending on the positron stopping power of the underlying material. The positron stopping power was derived from the attenuation coefficient of 511keV gamma photons according to the available PET attenuation maps. Thus, the history of energy deposition is taken into account within the range of kernels. Special emphasis was placed on facilitating the very fast computation of the positron annihilation probability in each voxel. ResultsPositron path distributions of 18F in low-density polyurethane were in high agreement with Geant4 simulation at an annihilation probability larger than 10-2[~]10-3 of the maximum annihilation probability. The Geant4 simulation was further validated with measured 18F depth profiles in these polyurethane phantoms. The tissue boundary of water with cortical bone and lung was correctly modeled. Residual artifacts from the numerical computations were in the range of 1%. The calculated annihilation probability in voxels shows an overall difference of less than 20% compared to the Geant4 simulation. SignificanceThe proposed method significantly improves spatial resolution for non-standard isotopes by providing accurate range kernels, even in the case of significant tissue inhomogeneities.

Microcomputed tomography (micro-CT) is a frequently used imaging tool for a wide spectrum of in-vivo mouse models in basic and translational research. To allow an accurate interpretation of micro-CT images, high spatial resolution is necessary. However, this may also lead to a high radiation exposure of the animals. Therefore, animal welfare requires exact information about the expected radiation doses for experimental planning. To gain this, a mouse cadaver was herein used for micro-CT analyses under different conditions. For each radiation dose measurement, the cadaver was labeled with thermoluminescent dosimeter chips around the thoracic skin surface. Micro-CT scans of the thorax were performed with spatial resolutions of 35 {micro}m, 18 {micro}m and 9 {micro}m in combination with Al0.5, Al1.0, CuAl and Cu filters. As a surrogate of image quality, the number of identifiable lung vessels was counted on a transversal micro-CT slice. Measured radiation doses varied from 0.09 Gy up to 5.18 Gy dependent on resolution and filter settings. A significant dose reduction of > 75% was achieved by a Cu filter when compared to an Al0.5 filter. However, this resulted in a markedly reduced image quality and interpretability of microstructures due to higher radiation shielding and lower spatial resolution. Thus, the right combination of distinct filters and several scan protocol settings adjusted to the individual requirements can significantly reduce the radiation dose of micro-CT leading to a higher animal welfare standard.

BackgroundSpectral computed tomography (CT) is increasingly used for quantitative imaging, yet accurate prediction of spectral quantitative bias remains challenging and computationally expensive with conventional approaches. Bias manifests as systematic deviations in reconstructed quantities (e.g., Hounsfield units, iodine density) from their true physical values. It arises from a combination of model mismatch, hardware/processing imperfections, exam-dependent factors, and noise-induced effects amplified by nonlinear operations such as the logarithmic transformation and material decomposition. PurposeWe present a practical, projection-based statistical framework to estimate noise-induced spectral bias efficiently, without the runtime burden of Monte Carlo (MC) simulation. MethodsTo demonstrate the bias estimator, we modeled the central-ray of a clinical X-ray tube attenuating through a 300 mm patient-equivalent path with a 10 mm insert containing 10 mg/mL iodine. A 120 kVp tube voltage and tube currents from 100-350 mA were used. Ideal and realistic photon-counting detector responses were simulated across 50 bin threshold settings. Quantum Poisson noise was modeled, and Bayesian probabilities of material decomposition outputs centered on ground truth iodine and water bases were computed. Expected material decomposition outputs [Formula] were derived from a 2D probability map, and bias was measured. A simple Python Monte Carlo (MC) simulation served as a reference. ResultsThe proposed bias estimator closely matched MC-derived bias, with an average relative iodine bias percent difference between the estimators of 0.44% across all tube currents and bin thresholds. Average runtime of the bias estimator was only 0.5% of the MC simulation. Optimal thresholds for minimizing iodine noise (via the Cramer-Rao lower bound) differed from those minimizing iodine bias, highlighting key noise-bias tradeoffs. ConclusionEfficient spectral bias and noise estimation are essential for quantitative CT system design. This modular framework enables rapid, bias-aware optimization of spectral acquisition parameters and is adaptable to alternative spectral CT technologies beyond photon counting. Novelty and Significance of StudyPlease briefly (150 words or less) describe the novelty and/or significance of your study. Bias estimation is paramount for designing accurate spectral CT systems that deliver improved diagnostic performance. Traditional approaches rely on computationally intensive Monte Carlo simulations. We propose an efficient and practical bias estimator that uses Bayesian statistics and expected material decomposition values derived from a flexible, modular CT forward model. Unlike conventional Monte Carlo approaches, this framework enables rapid exploration of spectral design tradeoffs between bias and noise. We demonstrate both the accuracy and speed of this bias estimator relative to Monte Carlo approaches.

Background and PurposeThe use of MRI-based fat quantification can be applied to automatically identify red bone marrow which is highly sensitive to radiation and systemic therapies and could be used as an organ-of-interest for adaptive radiation therapy. Currently, the tradeoff of scan time and PDFF/R2* quantification accuracy from the 2-/3-/6-point methods, particularly for the time-constrained MR-Linac, remain unanswered. Therefore, the purpose of this study was to investigate the technical feasibility and quantitative performance of quantitative Dixon-based imaging for scanners within the radiation oncology department. Materials and MethodsA 2-/3-/6-point version of the quantitative Dixon sequence was developed and scanned on a 1.5T MR-Simulation, 3T MR-Simulation, and 1.5T MR-Linac scanner for five repetitions using the Calimetrix Model 725 PDFF-R2* phantom as a nominal reference for quantitative PDFF/R2* values. The image geometric distortion as well as the quantitative concordance, Bland-Altman agreement, repeatability, and reproducibility of both the PDFF/R2* values were determined. Each sequence was evaluated in both the pelvis and head and neck across both healthy volunteers and patients. ResultsThe most severe geometric distortion was less than 2 mm except for the 1.5T MR-Linac when using the 2-point Dixon sequence with distortions exceeding 5 mm. The 6-point Dixon sequence showed the highest concordance at above 0.97 across all scanners for both PDFF and R2* followed by the 3-point and 2-point sequence. The 2-point Dixon sequence exhibited significant PDFF biases particularly at the higher R2* values since it did not correct for it during reconstruction. For the Bland-Altman analysis, the 2-point Dixon sequence had the widest 95% limits of agreement followed by the 3-point and 6-point Dixon sequence with the narrowest bands. The goodness-of-fit is generally lowest at higher PDFF values and lower R2* values. Both repeatability and reproducibility were the lowest for the 6-point Dixon sequence. DiscussionThe 6-point quantitative Dixon sequence demonstrated superiority for the chosen evaluation metrics. The results of this work can be used to determine the threshold for true quantitative changes of PDFF/R2* while considering acquisition variabilities, enabling future biomarker studies and clinical trials. Further, this work provides validation for future investigations into quantitative bone marrow characterization. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/26347965v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@8b2139org.highwire.dtl.DTLVardef@322a97org.highwire.dtl.DTLVardef@18a3a46org.highwire.dtl.DTLVardef@1f7ef62_HPS_FORMAT_FIGEXP M_FIG C_FIG

Radiopharmaceutical therapies are poised to enhance patient care for several currently untreatable metastatic cancers. Radium-223 dichloride citrate is indicated for treatment of bone metastatic castrate resistant prostate cancer, serving as the primary use of alpha particle emitting radium to irradiate bone lesions. Improvements and refinement of such therapies relies on patient centric and micro scale quantification to assess and compare efficacy. Computational modeling and Monte Carlo simulations provide a valuable tool for understanding micro scale phenomena of radiopharmaceutical therapies. Via simulation, we undertake and illuminate dose profiles of radium-223 dichloride treatment, evaluating energy and dose distributions based on primary, patient-derived specimens. A set of four activity distributions were simulated on three patient bone lesion biopsy samples. These simulations validate the novel tool for micron-scale modeling with patient-derived specimens. Ablative dose profiles are shown to be driven by uptake distributions as well as the target tissues microstructure.

BackgroundProton and ion therapy have gained significant importance in radiation therapy cancer treatment due to their favorable dose distribution and tissue-sparing properties. In conventional gamma radiation therapy some methods of in vivo dose verification are possible with current medical devices. Proton and ion therapy dose verification is limited, mainly using PET for particle range. Prompt gamma methods offer low spatial resolution. This study presents initial results for in-vivo dose verification with PET imaging of F-18 during proton therapy. Although the activity concentration of F-18 generated by typical clinical doses (several Gy) is low, PET imaging performed approximately one hour post-irradiation yields sufficient image quality to derive dose-volume histograms (DVH), enabling spatial dose verification. PurposeTo verify the applied dose in proton therapy in vivo using Positron Emission Tomography with millimetric precision. Materials and MethodsWe simulated proton treatment in a brain phantom using Gate and RayStation platforms to assess the production of several positron emitting isotopes. We focused on the production of fluorine-18 (F-18), given its low positron energy, which enables accurate reproduction of the dose distribution. To evaluate the detectability of the anticipated low activity concentrations (on the order of a few Bq/mL) following a 3 Gy proton irradiation, we tested three PET systems: two preclinical scanners based on LYSO detectors and one clinical scanner based on BGO crystals. Finally, we have analyzed the dose-volume histograms for simulated and measured dose and activity distributions and compared them with the planned ones. ResultsF-18 PET imaging in proton therapy correlates with delivered dose within 5% error and matches the planned dose fall-off edge within 1 mm, enabling accurate and precise in vivo dose verification. ConclusionThe dose verification in proton therapy using F-18 Positron Emission Tomography allows higher precision of dose than other positron emitters like C-11, N-12 or O-15. 1 Key ResultsKey Results: In proton therapy, in-vivo F-18 production correlates with the deposited dose in the patient with a 5% margin of error. The leading-edge position of the F-18 activity distribution agrees with the planned dose fall off within 1 mm. Furthermore, the feasibility of detecting low activity concentrations typical of proton therapy (Bq/ml range) has been demonstrated using both preclinical and clinical PET scanners. 2 Required Summary StatementIn proton therapy the delivered dose to the patient can be measured in vivo using Positron Emission Tomography by imaging the production of F-18 isotope, achieving millimetric spatial accuracy.

PurposeRadiological cancer imaging features, or radiomics features, can be derived to diagnose disease or predict treatment response. However, variability between vendors, scanners, protocols, and even reconstruction software versions is an obstacle to the clinical use of radiomics features. This study aimed to characterize the impact of kernel reconstruction differences and integral tube current settings on radiomic features extracted from computed tomography (CT) scans. MethodsRadiomic features were extracted from CT scans of a 3D-printed phantom with five imprinted tumors using the CERR software system, resulting in 282 features. Batch effects were assessed via principal component analysis (PCA) and correlation measures. Robustness was measured using the concordance correlation coefficient (CCC) and Pearson correlation coefficient. Statistical analysis was performed using R software. ResultsPCA identified two clusters comprised of Standard, ASIRs, ASIRV, and soft kernels in one, and Lung and Bone Kernels in the other. Features displayed a gradient from ASIR10 to ASIR50 and ASIRV1 to ASIRV5 in terms of nearness to Standard Kernel feature values. Feature correlation matrices revealed little change in ASIRs, ASIRVs, and the Standard Kernel, but showed significant changes in Bone and Lung Kernel results. Combat-algorithm correction improved robustness, particularly in first-order statistic features, and mitigated batch effects due to the ASIRs and the standard kernel. Forty (40) out of 282 features were identified as robust. However, Combat-based correction performed poorly in harmonizing Bone and Lung reconstruction kernels. ConclusionsThe robustness of means and median radiomic features across kernel reconstruction choices, in contrast to the lack of robustness in many other radiomic features, suggests that kernel reconstruction effects are not well-addressed by current harmonization methods.

PurposeSingle-energy computed tomography (CT) often suffers from poor contrast, yet it remains critical for effec-tive radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) x-ray sources and thus already possess the hardware needed for dual-energy (DE) CT. There exists an unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts. ApproachA toy model comprising a single-line integral through a two-material object was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (three kV, two MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated fan-beam CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images. ResultsThe 80kV-140kV pair typically yielded the best SNRs, but for bone thicknesses greater than 8 cm, the detunedMV-80kV pair surpassed it. Peak MV-kV SNR was achieved with approximately 90% dose allocated to the MV spectrum. For the CT simulations, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT at specific monoenergies. With the metal implant, MV-kV produced a higher maximum CNR and lower minimum root-mean-square-error than kV-kV. ConclusionsThis work quantitatively analyzes MV-kV DE-CT imaging and assesses its potential advantages. This technique may yield improved contrast and accuracy relative to dose-matched single-energy CT or kV-kV DE-CT, depending on object composition.

Spectral CT has been increasingly implemented clinically for its better characterization and quantification of materials through its multi-energy results. It also facilitates calculation of physical density utilizing the Alvarez-Macovski model without approximations. These spectral physical density quantifications allow for non-invasive mass measurements and temperature evaluations by manipulating the definition of physical density and thermal volumetric expansion, respectively. To develop the model, original and parametrized versions of the Alvarez-Macovski model and electron density-physical density model were validated with a phantom. The best physical density model was then implemented on clinical spectral CT scans of ex vivo bovine muscle to determine the accuracy and effect of acquisition parameters on mass measurements. In addition, the relationship between physical density and changes in temperature was evaluated by scanning and subjecting the tissue to a range of temperatures. A linear fit utilizing the thermal volumetric expansion was performed to assess the correlation. The parametrized Alvarez-Macovski model performed best in both model development and validation with errors within {+/-}0.02 g/mL. As observed with muscle, physical density was not significantly affected by dose and acquisition mode but was slightly affected by collimation. These effects were also reflected in mass measurements, which demonstrated accuracy with a maximum percent error of 0.34%, further validating the physical density model. Furthermore, physical density was strongly correlated (R of 0.9781) to temperature changes through thermal volumetric expansion. Accurate and precise spectral physical density quantifications enable non-invasive mass measurements for pathological detection and temperature evaluation for thermal therapy monitoring in interventional oncology.

Background: Independent end-to-end verification of adaptive radiotherapy on MR-Linac systems is limited by the lack of patient-specific phantoms able to reproduce imaging and dosimetric properties from CT and MRI scanners. We present a method for automated generation of 4D, patient-specific, multi-material 3D-printable phantoms for quality assurance of adaptive radiotherapy on a 1.5T MR-Linac. Methods: Patient images were automatically segmented using a pretrained deep learning model. The segmented structures were converted into high-resolution 3D meshes and assembled into printable phantoms. A dosimeter holder was inserted at user-defined anatomical locations, with orientation optimized to avoid traversal across heterogeneous tissue interfaces. Physiological motion was incorporated by generating phantoms from images at different timepoints and interpolating deformation fields to create continuous 4D models. Multi-material organs designed by mixing a set of six polymers at various proportions were used to reproduce tissue-specific imaging properties. The properties of material mixtures were evaluated in a clinical CT simulator and a 1.5T MR-Linac. Results: The proposed workflow enables automated generation of anatomically realistic phantoms with several types of embedded dosimeters. A discrete search method was designed for placement and immobilization of OSLD, film, and ion chamber dosimeters. Calibration curves for Hounsfield units were derived through variations in radiopaque material content, while MR signal intensity was modulated by gel and tissue matrix mixtures. Patient-derived abdominal phantoms were fabricated at multiple scales while replicating internal anatomical detail. Multi-dimensional phantom generation enabled continuous representation of motion states with consistent mesh topology across phases. Conclusions: We demonstrate an end-to-end workflow for automated generation of 4D patient-specific phantoms for MR-Linac quality assurance. The method combines realistic anatomy, embedded dosimetry, multimodal imaging properties, and physiological motion within a single fabrication framework. This approachmay enable an improved validation of adaptive radiotherapy workflows in MR-guided treatment devices.

ObjectivesTo evaluate the clinical performance of a cadmium-zinc-telluride-(CZT-) based photon-counting computed tomography (PCCT) system for low-dose lung cancer screening (LCS-LDCT) using patient-specific 3D-printed lung phantoms, and to compare its image quality and radiomics consistency with a conventional energy-integrating detector CT (EIDCT) system. MethodsSix 3D-printed lung phantoms, derived from patient CT datasets and representing various lesion types (solid, part-solid, and ground-glass), were imaged on PCCT and EIDCT scanners at matched dose levels (1.6 - 20.4 mGy). Quantitative image metrics, Hounsfield unit (HU) accuracy, image noise, and contrast-to-noise ratio (CNR), were assessed across dose levels. Radiomic features were extracted for each lesion and analyzed via principal component analysis to quantify feature consistency (within-cluster distance) and lesion type separability. ResultsPCCT demonstrated significantly lower image noise and higher CNR compared with EIDCT, particularly at lower dose levels. HU values were consistent across doses for both systems, with reduced variability in PCCT (coefficient of variation < 0.004). Radiomics analysis revealed tighter clustering (reduced within-cluster distances) and comparable lesion type separability between PCCT and EIDCT, indicating enhanced feature stability and lesion differentiation. Qualitative review confirmed superior lesion conspicuity and margin delineation with PCCT. ConclusionsCZT-based PCCT outperforms conventional EIDCT in quantitative and qualitative imaging metrics for LCS-LDCT, enabling superior image quality and radiomics reproducibility at reduced radiation doses. These findings support the clinical translation of PCCT for lung cancer screening and radiomics-based lesion characterization. Key pointsO_LIPCCT offers reduced image noise and improved CNR performance, especially at ultra-low doses. C_LIO_LIHU stability and radiomics reproducibility are enhanced with PCCT. C_LI Clinical relevance statementO_LIPCCT may enable further dose reduction without compromising diagnostic accuracy in LCS-LDCT. C_LI

PurposeThis study investigates the robustness of quantitative radiomic features derived from computed tomography (CT) images of a novel patient informed 3-D printed phantom, which captures the morphological heterogeneity of tumors and normal tissue observed on CT scans.\n\nMethodsUsing a novel voxel-based multi-material three-dimensional (3D) printer, an anthropomorphic phantom that was modeled after diseased tissue seen on 6 patient CT scans was manufactured. Four patients presented with pancreatic adenocarcinoma tumors (PDAC), 1 with non-small cell lung carcinoma (NSCLC) and 1 with advanced stage hepatic cirrhosis. The 5 tumors were segmented, extracted and then imbedded into CT images of the heterogenous portion of the cirrhotic liver. The composite scan of the implanted tumor within the background cirrhotic liver was then 3D printed. The resultant phantom was scanned sequentially, 30 times with a clinical CT scanner using a reference CT protocol. One hundred and four quantitative radiomic features were then extracted from images of each lesion to determine their repeatability. Repeatability of each radiomic feature was evaluated using the within subject coefficient of variation (wCV, %). A feature with a wCV (%) > 10% was considered as being unrepeatable. A subset of the repeatable features that were also found to be prognostic for lung and pancreatic cancers were then assessed for their percent deviation (pDV, %) from reference values. The reference values were those derived from the repeatability portion of this study. The assessment was conducted by re-scanning the phantom with 11 different clinically relevant sets of scanning parameters. Deviation of radiomic features derived from images of each tumor across all sets of scanning parameters was assessed using the percent deviation relative to the reference values.\n\nResultsTwenty nine of the 104 features presented with wCV (%) > 10%. The lack of repeatability was found to depend on tumor type. The only class of radiomic features with a wCV (%) < 10% were those calculated using the neighboring grey level dependence-based matrices (NGLDM). Notably, skewness, first information correlation, cluster shade, Haralick correlation, autocorrelation, busyness, complexity, high gray level zone emphasis, small area high gray level emphasis, large area low gray level emphasis, large area high gray level emphasis, short run high grey level emphasis, and valley radiomic features had wCV (%) values > 10% for select tumors within the phantom. Two radiomic features prognostic for NSCLC, energy and grey level non-uniformity, had pDVs (%) that exceeded 30% across all scanning techniques. The pDV (%) for the 4 radiomic features prognostic for PDAC tumors depended on tumor type and selected scanning parameter. Application of the lung kernel caused the largest pDVs (%). Scans acquired with the reduced tube current of 100 mA and reconstructed with the bone kernel yielded pDVs (%) within {+/-} 10%.\n\nConclusionWe demonstrated the feasibility with which patient informed 3D printed phantoms can be manufactured directly from lesions seen on CT scans, and demonstrate their potential use for the assessment of robust quantitative radiomic features.

Accurate temperature monitoring during cryoablation, a minimally invasive technique that destroys tissue locally by forming an ice ball around an inserted cryoprobe, is vital for achieving complete ablation while protecting surrounding tissue. We present a dense slowness-shift imaging method that estimates local speed-of-sound changes from ultrasound B-mode images using optical flow. This single-transducer, image-based approach enables mapping of spatial temperature change without requiring additional hardware. Cryoablation experiments in a tissue-mimicking phantom and ex vivo turkey breast demonstrated that slowness deviation increases with decreasing temperature. In the phantom, the dependence was linear (a = -20.70 s{middle dot}m-1C{degrees}-1), while in turkey breast it presented an exponential relationship (t = 34.04xexp(0.075(-{Delta}T)) s{middle dot}m-1C{degrees}-1). The algorithm detected sub-degree temperature variations and accurately tracked cooling down to -39.4 {+/-} 5.6 {degrees}C. This work demonstrates the feasibility of ultrasound-based, noninvasive temperature monitoring during cryoablation, providing a scalable, real-time alternative to existing invasive or high-cost thermal assessment techniques.

ObjectiveEvaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system. MethodsA multi-energy CT phantom (20 cm diameter/small) was imaged with and without an extension ring (30 by 40 cm/large). It was equipped with various iodine inserts (0.2, 0.5, 1.0, 2.0, 5.0, 10.0, 15.0 mg/ml) and scanned over a range of radiation dose levels (CTDIvol 0.5, 0.8, 1.2, 1.6, 2.0, 4.0, 6.0, 10.0, 15.0 mGy) using four different acquisition modes: single source 120 kVp (SS120), 140 kVp (SS140) and dual-source 120 kVp (DS120), 140 kVp (DS140). Iodine density maps were produced with different levels of iterative reconstruction (QIR 0, 2, 4). To assess the agreement between nominal and measured iodine concentrations, root mean square error (RMSE) and Bland-Altman plots were generated by grouping different radiation dose levels (ultra-low: < 1.5 mGy; low: 1.5 - 5 mGy; medium: 5 - 15 mGy) and iodine concentrations (low: < 5 mg/ml; high: 5 - 15 mg/ml). ResultsOverall, quantification of iodine concentrations was accurate and reliable even at ultra-low radiation dose levels. With low and high iodine concentrations, RMSE ranged from 0.25 to 0.37, 0.20 to 0.38, and 0.25 to 0.37 mg/ml for ultra-low, low, and medium radiation dose levels, respectively. Similarly, for the three acquisition modes (SS120, SS140, DS 120, DS140), RMSE was stable at 0.31, 0.28, 0.33 and 0.30 mg/ml, respectively. Considering all levels of radiation dose, acquisition mode, and iodine concentration, the accuracy of iodine quantification was higher for the phantom without extension ring (RMSE 0.21 mg/ml) and did not vary across different levels of iterative reconstruction. ConclusionsThe first-generation PCCT allows for accurate iodine quantification over a wide range of iodine concentrations and radiation dose levels. Even very small concentrations of iodine can be quantified accurately at different simulated patient sizes. Stable accuracy across iterative reconstruction levels may allow further radiation exposure reductions without affecting quantitative results. SummaryClinical photon-counting CT provides excellent iodine quantification performance for a wide range of parameters (patient habitus, acquisition parameters, and iterative reconstruction modes) due to its excellent ultra-low dose performance. Key ResultsFirst-generation PCCTs are capable of accurately quantifying iodine over a wide range of radiation dose levels and iodine concentrations. Further radiation exposure reductions may be possible given stable accuracy across iterative reconstruction levels. In the future, accurate and precise iodine quantification will allow for the development of spectral-based biomarkers.

Proton beam therapy (PBT) offers a unique potential for dose conformity to tumors while sparing surrounding healthy tissues. Current PBT accuracy, however, is fundamentally limited by range uncertainties from tissue density variations and anatomical changes, yet no clinically viable methods exist for localizing the dose delivery pulse-by-pulse inside patients during pencil beam scanning (PBS). We developed and clinically demonstrated a first-of-its-kind radiation acoustic beam localization (iRABL) system for real-time tracking PBS trajectory and mapping dose deposition deep in patients body during PBT. A clinical-grade compact iRABL system featuring high speed, super-resolution, and high sensitivity was specifically designed for PBT applications. Its clinical feasibility was validated through the first-in-human study on prostate cancer patients, demonstrating the capability for in vivo proton dose mapping without interfering with treatment delivery. System performance, including spatial resolution, imaging speed for tracking beam trajectory and temporal dose accumulation, and dosimetric accuracy, was quantitatively characterized using tissue-equivalent phantoms and clinical treatment plans. This iRABL system achieved displacement resolution of 0.1 mm laterally and 0.2 mm axially, exceeding the acoustic diffraction limit by an order of magnitude and surpassing typical proton beam spot sizes. This super-resolution capability, combined with GPU-accelerated image reconstruction and processing, enabled single-pulse detection at a frame rate of 1 kHz, matching the proton systems pulse repetition rate. Dosimetric validation using clinical M-shaped treatment plans met clinical criteria with gamma index passing rates exceeding 90% at 3 mm/3% tolerance, confirming high accuracy for mapping delivered dose distributions. For the first time, by leveraging the high sensitivity and the high speed of our newly developed iRABL system, we are able to localize proton beam and map the proton dose deposition during PBS with sub-diffraction-limit spatial resolution, pulse-by-pulse imaging speed, and clinical grade accuracy. This capability, which addresses fundamental limitations in current treatment monitoring, holds promise for advancing PBT toward image-guided "proton surgery".

PurposeTo construct robust and validated radiomic predictive models, the development of a reliable method that can identify reproducible radiomic features robust to varying image acquisition methods and other scanner parameters should be preceded with rigorous validation. Due to the property of high correlation present between radiomic features, we hypothesize that reproducible radiomic features across different datasets that are obtained from different image acquisition settings preserve some level of connectivity between features in the form of a network.\n\nMethodsWe propose a regularized partial correlation network to identify robust and reproducible radiomic features. This approach was tested on two radiomic feature sets generated with two different reconstruction methods from a cohort of 47 lung cancer patients. The commonality of the resulting two networks was assessed. A largest common network component from the two networks was tested on phantom data consisting of 5 cancer samples. We further propose a novel K-means algorithm coupled with the optimal mass transport (OMT) theory to cluster samples. This approach following the regularized partial correlation analysis was tested on computed tomography (CT) scans from 77 head and neck cancer patients that were downloaded from The Cancer Imaging Archive (TCIA) and validated on CT scans from 83 head and neck cancer patients treated at our institution.\n\nResultsCommon radiomic features were found in relatively large network components between the resulting two partial correlation networks from a cohort of 47 lung cancer patients. The similarity of network components in terms of the common number of radiomic features was statistically significant. For phantom data, the Wasserstein distance on a largest common network component from the lung cancer data was much smaller than the Wasserstein distance on the same network using random radiomic features, implying the reliability of those radiomic features present in the network. Further analysis using the proposed Wasserstein K-means algorithm on TCIA head and neck cancer data showed that the resulting clusters separate tumor subsites and this was validated on our institution data.\n\nConclusionsWe showed that a network-based analysis enables identifying reproducible radiomic features. This was validated using phantom data and external data via the Wasserstein distance metric and the proposed Wasserstein K-means method.

PurposeRadiopharmaceutical therapies (RPTs) are showing significant value in targeting various forms of cancer; meanwhile, as an emerging paradigm there is significant room for optimization of RPTs. We developed a computational model towards improving therapeutic strategies via modification of dosage and timing of RPT injections. MethodsOur model simulates tumor growth, the pharmacokinetics of RPTs, and the radiobiological impact of radionuclide decay through energy deposition on tumor tissue. Specifically, we use the Hybrid Automata Library (HAL) to simulate tissue containing a heterogeneous tumor and its vasculature, along with oxygen and radiopharmaceutical concentration. Therapeutic interventions are modeled using a multiscale approach that connects whole-body compartmental modeling of radiopharmaceutical concentrations to the linear-quadratic survival model for tumor cells. We evaluated several treatment schedules applied to tumors with different conditions resulting in insights on how to improve RPTs. ResultsTumors with varying vascular densities responded differently to identical therapeutic regimens. We also found that the timing and frequency of therapeutic interventions played important roles in the effectiveness of RPTs. Overall, we demonstrated that a one-size-fits-all approach is inadequate for achieving optimal therapies. ConclusionsOur model provided insights for improving treatment schedules that highlight the potential for personalized approaches to achieve better outcomes. The code for the model is publicly available on GitHub: github.com/Elahe-hmh/HAL_RPT

PurposeThis study aims to characterize dose variations from the original plan for a cohort of head-and-neck cancer (HNC) patients using high-quality computed tomography on rails (CTOR) datasets and evaluate a predictive model for identifying patients needing re-planning. Material and methods74 HNC patients treated on our CTOR equipped machine were evaluated in this retrospective study. Patients were treated at our facility using in-room, CTOR Image Guidance -- acquiring CTOR kV fan beam CT (FBCT) images on a weekly to near-daily basis. For each patient, a particular days delivered treatment dose was calculated by applying the approved, planned beam set to the post image-guided alignment CT image-of-the-day. Total accumulated delivered dose distributions were calculated and compared to the planned dose distribution and differences were characterized by comparison of dose and biological response statistics. ResultsThe majority of patients in the study saw excellent agreement between planned and delivered dose distribution in targets -- the mean deviations of D95 and D98 of the planning target volumes (PTVs) of the cohort are -0.7% and -1.3%, respectively. In critical organs, we saw a +6.5% mean deviation of mean dose in parotid glands, -2.3% mean deviation of maximum dose in brainstem, and +0.7% mean deviation of maximum dose in spinal cord. 10 of 74 patients experienced nontrivial variation of delivered parotid dose which resulted in a normal tissue complication probability (NTCP) increase compared to the anticipated NTCP in the original plan, ranging from 11% to 44%. ConclusionWe determined that a mid-course evaluation of dose deviation was not effective in predicting the need of re-planning for our patient cohorts. The observed non-trivial dose difference to parotid gland delivered dose suggest that even when rigorous, high quality image guidance is performed, clinically concerning variations to predicted dose delivery can still occur.

ObjectiveTo evaluate the dose efficiency of cadmium-zinc-telluride (CZT) based photon-counting CT (PCCT) compared to energy-integrating detector CT (EID-CT) across phantom sizes. MethodsA patient-specific 3D-printed pancreas phantom and a phantom with tissue mimicking inserts were placed in extension rings corresponding to the 50th, 75th, 85th, and 95th percentile adult waist circumferences. Phantoms were scanned on both PCCT and EID-CT with CTDIvol ranging from 0.5 to 19.4 mGy. Noise was measured in both phantoms to evaluate dose efficiency. Non-Poisson noise at low doses (<2 mGy) was quantified using root mean square error from linear fits of the noise-dose relationship. Potential dose reduction was then assessed by matching noise levels between scanners across phantom sizes. ResultsPCCT demonstrated reduced noise compared to EID-CT across all phantom sizes and doses with average noise reductions of 22%, 23%, 25%, and 28% for the 50th, 75th, 85th, and 95th percentile phantoms, respectively. Noise reduction intensified at lower doses and larger phantom sizes, reaching 88 HU at 1 mGy for the 95th percentile phantom. Non-Poisson noise decreased significantly with PCCT compared to EID-CT for all phantom sizes (p < 0.013). At matched noise levels, PCCT enabled dose reductions of 33% and 44% for the 50th and 95th percentile phantoms, respectively. ConclusionsPCCT exhibited superior dose efficiency compared to EID-CT across a range of phantom sizes. The enhanced dose efficiency enables both noise reduction and potential dose reduction for the imaging of obese patients and low-dose imaging applications. Key PointsO_ST_ABSQuestionC_ST_ABSPhoton-counting CT (PCCT) enables improved quantum detection and eliminates electronic noise, but its benefits have not been evaluated for obese patient sizes. FindingsCompared to EID-CT, PCCT enhanced dose efficiency with noise reduction and potential dose reduction across all phantom sizes and doses. Clinical RelevanceThe improved dose efficiency of PCCT facilitates noise reduction that enables diagnostic image quality, and thereby the diagnostic accuracy, for obese patients.