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<jats:title>Abstract</jats:title> <jats:p>This study evaluated the machine-dependent three-dimensional geometric distortion images acquired from a 1.5T 700 mm-wide bore MR-simulator based on a large geometric accuracy phantom. With the consideration of radiation therapy (RT) application requirements, every sequence was examined in various combinations of acquisition-orientations and receiver-bandwidths with console-integrated distortion correction enabled. Distortion was repeatedly measured over a six-month period. The distortion measured from the images acquired at the beginning of this period was employed to retrospectively correct the distortion in the subsequent acquisitions. Geometric distortion was analyzed within the largest field-of-view allowed. Six sequences were examined for comprehensive distortion analysis—VIBE, SPACE, TSE, FLASH, BLADE and PETRA. Based on optimal acquisition parameters, their diameter-sphere-volumes (DSVs) of CT-comparable geometric fidelity (where 1 mm distortion was allowed) were 333.6 mm, 315.1 mm, 316.0 mm, 318.9 mm, 306.2 mm and 314.5 mm respectively. This was a significant increase from 254.0 mm, 245.5 mm, 228.9 mm, 256.6 mm, 230.8 mm and 254.2 mm DSVs respectively, when images were acquired using un-optimized parameters. The longitudinal stability of geometric distortion and the efficacy of retrospective correction of console-corrected images, based on prior distortion measurements, were inspected using VIBE and SPACE. The retrospectively corrected images achieved over 500 mm DSVs with 1 mm distortion allowed. The median distortion was below 1 mm after retrospective correction, proving that obtaining prior distortion map for subsequent retrospective distortion correction is beneficial. The systematic evaluation of distortion using various combinations of sequence-type, acquisition-orientation and receiver-bandwidth in a six-month time span would be a valuable guideline for optimizing sequence for various RT applications.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Measurements using an Optical Fiber OFS including an inorganic scintillator placed on the surface of a phantom show that the particle energy distribution inside the phantom remains unchanged. The backscattered intensity measured using an Optical Fiber Sensor (OFS) exhibits a linear relationship with the total radiation dose delivered to the phantom, and this relationship shows that the OFS can be used for indirect dose measurement when located on the surface of the phantom i.e. that arising from the energetic backscattered electrons and photons. Such a device can therefore be used as a clinical <jats:italic>in-vivo</jats:italic> dosimeter, being located on the patient’s body surface. In addition, the measurement results for the same OFS located inside and outside the radiation field of a compound water based phantom are analyzed. The differences in measurement of the fluorescence signal in response to various tissue materials representing bone or tumor tissue in the irradiation field are strongly related to the material’s ability to block the scattered rays from the water phantom, as well as the scattered x-rays generated by the material located within the phantom.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective</jats:italic>. Several lumped and distributed parameter models of the inner ear have been proposed to improve vestibular implant stimulation. The models should account for all significant physical phenomena that influence the current propagation, such as the electrical double layer (EDL) and medium polarization. The electrical properties of the medium are reflected in the electrical impedance; therefore, the study aimed to measure the impedance in the guinea pig inner ear and construct its equivalent circuit. <jats:italic>Approach</jats:italic>. The electrical impedance was measured from 100 Hz to 50 kHz between a pair of platinum electrodes immersed in 0.9% NaCl saline solution using sinusoidal voltage signals. The Randles circuit was fitted to the measured impedance in the saline solution in order to estimate the EDL parameters (<jats:inline-formula> <jats:tex-math> <?CDATA $C,\,W,\,and\,{R}_{ct}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>C</mml:mi> <mml:mo>,</mml:mo> <mml:mspace width=".25em" /> <mml:mi>W</mml:mi> <mml:mo>,</mml:mo> <mml:mspace width=".25em" /> <mml:mi>a</mml:mi> <mml:mi>n</mml:mi> <mml:mi>d</mml:mi> <mml:mspace width=".25em" /> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>c</mml:mi> <mml:mi>t</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c4aieqn1.gif" xlink:type="simple" /> </jats:inline-formula>) of the electrode interface in saline. Then, the electrical impedance was measured between all combinations of the electrodes located in the semicircular canal ampullae and the vestibular nerve in the guinea pig <jats:italic>in vitro</jats:italic>. The extended Randles circuit considering the medium polarization (<jats:inline-formula> <jats:tex-math> <?CDATA ${R}_{i},{R}_{e},{C}_{m}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>i</mml:mi> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>e</mml:mi> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c4aieqn2.gif" xlink:type="simple" /> </jats:inline-formula>) together with EDL parameters (<jats:inline-formula> <jats:tex-math> <?CDATA $C,\,{R}_{ct}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>C</mml:mi> <mml:mo>,</mml:mo> <mml:mspace width=".25em" /> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>c</mml:mi> <mml:mi>t</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c4aieqn3.gif" xlink:type="simple" /> </jats:inline-formula>) obtained from the saline solution was fitted to the measured impedance of the guinea pig inner ear. The Warburg element was assumed negligible and was not considered in the guinea pig model. <jats:italic>Main results</jats:italic>. For the set-up used, the obtained EDL parameters were: <jats:inline-formula> <jats:tex-math> <?CDATA $C=27.09* {10}^{-8}F,\,{R}_{ct}=18.75\,k{\rm{\Omega }}.$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>C</mml:mi> <mml:mo>=</mml:mo> <mml:mn>27.09</mml:mn> <mml:mo>*</mml:mo> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:msup> <mml:mi>F</mml:mi> <mml:mo>,</mml:mo> <mml:mspace width=".25em" /> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>c</mml:mi> <mml:mi>t</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>18.75</mml:mn> <mml:mspace width=".25em" /> <mml:mi>k</mml:mi> <mml:mi mathvariant="normal">Ω</mml:mi> <mml:mo>.</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c4aieqn4.gif" xlink:type="simple" /> </jats:inline-formula> The average values of intra-, extracellular resistances, and membrane capacitance were <jats:inline-formula> <jats:tex-math> <?CDATA ${R}_{i}=4.74\,k{\rm{\Omega }},$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>i</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>4.74</mml:mn> <mml:mspace width=".25em" /> <mml:mi>k</mml:mi> <mml:mi mathvariant="normal">Ω</mml:mi> <mml:mo>,</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c4aieqn5.gif" xlink:type="simple" /> </jats:inline-formula> <jats:inline-formula> <jats:tex-math> <?CDATA ${R}_{e}=45.05\,k{\rm{\Omega }},$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>e</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>45.05</mml:mn> <mml:mspace width=".25em" /> <mml:mi>k</mml:mi> <mml:mi mathvariant="normal">Ω</mml:mi> <mml:mo>,</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c4aieqn6.gif" xlink:type="simple" /> </jats:inline-formula> <jats:inline-formula> <jats:tex-math> <?CDATA ${C}_{m}=9.69* {10}^{-8}\,F,$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>9.69</mml:mn> <mml:mo>*</mml:mo> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:msup> <mml:mspace width=".25em" /> <mml:mi>F</mml:mi> <mml:mo>,</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c4aieqn7.gif" xlink:type="simple" /> </jats:inline-formula> respectively. <jats:italic>Significance</jats:italic>. The obtained values of the model parameters can serve as a good estimation of the EDL for modelling work. The EDL, together with medium polarization, plays a significant role in the electrical impedance of the guinea pig inner ear, therefore, they should be considered in electrical conductivity models to increase the credibility of the simulations.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signals such as event-related potentials (ERPs) have been established as a reliable compromise between spatio-temporal resolution and patient impact, but limitations due to portability and versatility preclude their broad application. Here we describe a deep-learning augmented error-related potential (ErrP) discriminating BCI using a consumer-grade portable headset EEG, the Emotiv EPOC<jats:sup>+</jats:sup>. <jats:italic>Approach.</jats:italic> We recorded and discriminated ErrPs offline and online from 14 subjects during a visual feedback task. <jats:italic>Main results:</jats:italic> We achieved online discrimination accuracies of up to 81%, comparable to those obtained with professional 32/64-channel EEG devices via deep-learning using either a generative-adversarial network or an intrinsic-mode function augmentation of the training data and minimalistic computing resources. <jats:italic>Significance.</jats:italic> Our BCI model has the potential of expanding the spectrum of BCIs to more portable, artificial intelligence-enhanced, efficient interfaces accelerating the routine deployment of these devices outside the controlled environment of a scientific laboratory.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic> <jats:bold>Background and purpose</jats:bold> </jats:italic> <jats:italic>.</jats:italic> This work aims to present the strategy to simulate a clinical linear accelerator based on the geometry provided by the manufacturer and summarize the corresponding experimental validation. Simulations were performed with the Geant4 Monte Carlo code under a grid computing environment. The objective of this contribution is reproducing therapeutic dose distributions in a water phantom with an accuracy less than 2%. <jats:italic> <jats:bold>Materials and methods.</jats:bold> </jats:italic> A Geant4 Monte Carlo model of an Elekta Synergy linear accelerator has been established, the simulations were launched in a large grid computing platform. Dose distributions are calculated for a 6 MV photon beam with treatment fields ranging from 5 × 5 cm<jats:sup>2</jats:sup> to 20 × 20 cm<jats:sup>2</jats:sup> at a source—surface distance of 100 cm. <jats:italic> <jats:bold>Results.</jats:bold> </jats:italic> A high degree of agreement is achieved between the simulation results and the measured data, with dose differences of about 1.03% and 1.96% for the percentage depth dose curves and lateral dose profiles, respectively. This agreement is evaluated by the gamma index comparisons. Over 98% of the points for all simulations meet the restrictive acceptability criteria of 2%/2 mm. <jats:italic> <jats:bold>Conclusion.</jats:bold> </jats:italic> We have demonstrated the possibility to establish an accurate linac head Monte Carlo model for dose distribution simulations and quality assurance. Percentage depth dose curves and beam quality indices are in perfect agreement with the measured data with an accuracy of better than 2%.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>In this study, we investigated the possibility of predicting expression levels of programmed death-ligand 1 (PD-L1) using radiomic features of intratumoral and peritumoral tumors on computed tomography (CT) images. We retrospectively analyzed 161 patients with non-small cell lung cancer. We extracted radiomic features for intratumoral and peritumoral regions on CT images. The null importance, least absolute shrinkage, and selection operator model were used to select the optimized feature subset to build the prediction models for the PD-L1 expression level. LightGBM with five-fold cross-validation was used to construct the prediction model and evaluate the receiver operating characteristics. The corresponding area under the curve (AUC) was calculated for the training and testing cohorts. The proportion of ambiguously clustered pairs was calculated based on consensus clustering to evaluate the validity of the selected features. In addition, Radscore was calculated for the training and test cohorts. For expression level of PD-L1 above 1%, prediction models that included radiomic features from the intratumoral region and a combination of radiomic features from intratumoral and peritumoral regions yielded an AUC of 0.83 and 0.87 and 0.64 and 0.74 in the training and test cohorts, respectively. In contrast, the models above 50% prediction yielded an AUC of 0.80, 0.97, and 0.74, 0.83, respectively. The selected features were divided into two subgroups based on PD-L1 expression levels≥50% or≥1%. Radscore was statistically higher for subgroup one than subgroup two when radiomic features for intratumoral and peritumoral regions were combined. We constructed a predictive model for PD-L1 expression level using CT images. The model using a combination of intratumoral and peritumoral radiomic features had a higher accuracy than the model with only intratumoral radiomic features.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>External beam radiotherapy often includes the use of field sizes 3 × 3 cm<jats:sup>2</jats:sup> or less, which can be defined as small fields. Dosimetry is a difficult, yet important part of the radiotherapy process. The dosimetry of small fields has additional challenges, which can lead to treatment inconsistencies if not done properly. Most important is the use of an appropriate detector, as well as the application of the necessary corrections. The International Atomic Energy Agency and the American Association of Physicists in Medicine provide the International Code of Practice (CoP) TRS-483 for the dosimetry of small static fields used in external MV photon beams. It gives guidelines on how to apply small-field correction factors for small field dosimetry. The purpose of this study was to evaluate the impact of inaccurate small-field output factors on clinical brain stereotactic radiosurgery plans with and without applying the small-field correction factors as suggested in the CoP. Small-field correction factors for a Varian TrueBeam linear accelerator were applied to uncorrected relative dose factors. Uncorrected and corrected clinical plans were created with two different beam configurations, 6 MV with a flattening filter (6 WFF) and 6 MV without a flattening filter (6 FFF). For the corrected plans, the planning target volume mean dose was 1.6 ± 0.9% lower with p &lt; 0.001 for 6 WFF and 1.8 ± 1.5% lower with p &lt; 0.001 for 6 FFF. For brainstem, a major organ at risk, the corrected plans had a dose that was 1.6 ± 0.9% lower with p = 0.03 for 6 WFF and 1.8 ± 1.5% lower with p = 0.10 for 6 FFF. This represents a systematic error that should and can be corrected.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Purpose.</jats:italic> Radiation epidemiology studies of childhood cancer survivors treated in the pre-computed tomography (CT) era reconstruct the patients’ treatment fields on computational phantoms. For such studies, the phantoms are commonly scaled to age at the time of radiotherapy treatment because age is the generally available anthropometric parameter. Several reference size phantoms are used in such studies, but reference size phantoms are only available at discrete ages (e.g.: newborn, 1, 5, 10, 15, and Adult). When such phantoms are used for RT dose reconstructions, the nearest discrete-aged phantom is selected to represent a survivor of a specific age. In this work, we (1) conducted a feasibility study to scale reference size phantoms at discrete ages to various other ages, and (2) evaluated the dosimetric impact of using exact age-scaled phantoms as opposed to nearest age-matched phantoms at discrete ages. <jats:italic>Methods.</jats:italic> We have adopted the University of Florida/National Cancer Institute (UF/NCI) computational phantom library for our studies. For the feasibility study, eight male and female reference size UF/NCI phantoms (5, 10, 15, and 35 years) were downscaled to fourteen different ages which included next nearest available lower discrete ages (1, 5, 10 and 15 years) and the median ages at the time of RT for Wilms’ tumor (3.9 years), craniospinal (8.0 years), and all survivors (9.1 years old) in the Childhood Cancer Survivor Study (CCSS) expansion cohort treated with RT. The downscaling was performed using our in-house age scaling functions (ASFs). To geometrically validate the scaling, Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Euclidean distance (ED) were calculated between the scaled and ground-truth discrete-aged phantom (unscaled UF/NCI) for whole-body, brain, heart, liver, pancreas, and kidneys. Additionally, heights of the scaled phantoms were compared with ground-truth phantoms’ height, and the Centers for Disease Control and Prevention (CDC) reported 50th percentile height. Scaled organ masses were compared with ground-truth organ masses. For the dosimetric assessment, one reference size phantom and seventeen body-size dependent 5-year-old phantoms (9 male and 8 female) of varying body mass indices (BMI) were downscaled to 3.9-year-old dimensions for two different radiation dose studies. For the first study, we simulated a 6 MV photon right-sided flank field RT plan on a reference size 5-year-old and 3.9-year-old (both of healthy BMI), keeping the field size the same in both cases. Percent of volume receiving dose ≥15 Gy (V<jats:sub>15</jats:sub>) and the mean dose were calculated for the pancreas, liver, and stomach. For the second study, the same treatment plan, but with patient anatomy-dependent field sizes, was simulated on seventeen body-size dependent 5- and 3.9-year-old phantoms with varying BMIs. V<jats:sub>15</jats:sub>, mean dose, and minimum dose received by 1% of the volume (D<jats:sub>1</jats:sub>), and by 95% of the volume (D<jats:sub>95</jats:sub>) were calculated for pancreas, liver, stomach, left kidney (contralateral), right kidney, right and left colons, gallbladder, thoracic vertebrae, and lumbar vertebrae. A non-parametric Wilcoxon rank-sum test was performed to determine if the dose to organs of exact age-scaled and nearest age-matched phantoms were significantly different (p &lt; 0.05). <jats:italic>Results.</jats:italic> In the feasibility study, the best DSCs were obtained for the brain (median: 0.86) and whole-body (median: 0.91) while kidneys (median: 0.58) and pancreas (median: 0.32) showed poorer agreement. In the case of MDA and ED, whole-body, brain, and kidneys showed tighter distribution and lower median values as compared to other organs. For height comparison, the overall agreement was within 2.8% (3.9 cm) and 3.0% (3.2 cm) of ground-truth UF/NCI and CDC reported 50th percentile heights, respectively. For mass comparison, the maximum percent and absolute differences between the scaled and ground-truth organ masses were within 31.3% (29.8 g) and 211.8 g (16.4%), respectively (across all ages). In the first dosimetric study, absolute difference up to 6% and 1.3 Gy was found for V<jats:sub>15</jats:sub> and mean dose, respectively. In the second dosimetric study, V<jats:sub>15</jats:sub> and mean dose were significantly different (p &lt; 0.05) for all studied organs except the fully in-beam organs. D<jats:sub>1</jats:sub> and D<jats:sub>95</jats:sub> were not significantly different for most organs (p &gt; 0.05). <jats:italic>Conclusion.</jats:italic> We have successfully evaluated our ASFs by scaling UF/NCI computational phantoms from one age to another age, which demonstrates the feasibility of scaling any CT-based anatomy. We have found that dose to organs of exact age-scaled and nearest aged-matched phantoms are significantly different (p &lt; 0.05) which indicates that using the exact age-scaled phantoms for retrospective dosimetric studies is a better approach.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>In the wake of recent advancements in scintillator, photodetector, and low-noise fast electronics technologies, as well as in fast reconstruction software, positron emission tomography (PET) scanners have seen considerable improvements in spatial resolution, time resolution, and absolute sensitivity. To continue this trend, we present a helmet type PET brain scanner design that combines high solid angle coverage and double-ended readout of 30 mm-thick scintillator crystals to achieve excellent absolute sensitivity, depth of interaction resolution, and time resolution. This scanner comprises 598 detector arrays, each with 8 × 8 Lu<jats:sub>1.8</jats:sub>Y<jats:sub>0.2</jats:sub>SiO<jats:sub>5</jats:sub>:Ce (LYSO:Ce) crystals with dimensions 3.005 × 3.005 × 30 mm<jats:sup>3</jats:sup> one-to-one coupled on either end to silicon photomultipliers (SiPMs). Our Monte Carlo simulations based in the platform Geant4 predict that this scanner would attain an absolute sensitivity to a 35 cm line source placed at the center of the radial field of view of (17.1 ± 0.1)%, a depth of interaction resolution of (3.99 ± 0.05) mm, and a coincidence time resolution of (198 ± 5) ps. Our simulations also predict radial, tangential, and axial spatial resolutions at the center of the field of view of 3.3 mm, 3.1 mm, and 3.3 mm, respectively. As this set of simultaneous parameters compares favorably to today’s most advanced clinical PET scanners and other proposed designs, this scanner has a good chance of becoming a preferred tool for high quality brain imaging.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Despite the improvements in image quality of cone beam computed tomography (CBCT) scans, application remains limited to patient positioning. In this study, we propose to improve image quality by dual energy (DE) imaging and iterative reconstruction using least squares fitting with total variation (TV) regularization. The generalization of TV called total nuclear variation (TNV) was used to generate DE images. We acquired single energy (SE) and DE scans of an image quality phantom (IQP) and of an anthropomorphic human male phantom (HMP). The DE scans were dual arc acquisitions of 70 kV and 130 kV with a variable dose partitioning between low energy (LE) and high energy (HE) arcs. To investigate potential benefits from a larger spectral separation between LE and HE, DE scans with an additional 2 mm copper beam filtration in the HE arc were acquired for the IQP. The DE TNV scans were compared to SE scans reconstructed with FDK and iterative TV with varying parameters. The contrast-to-noise ratio (CNR), spatial frequency, and structural similarity (SSIM) were used as image quality metrics. Results showed largely improved image quality for DE TNV over FDK for both phantoms. DE TNV with the highest dose allocation in the LE arm yielded the highest CNR. Compared to SE TV, these DE TNV results had a slightly lower CNR with similar spatial resolution for the IQP. A decrease in the dose allocated to the LE arm improved the spatial resolution with a trade-off against CNR. For the HMP, DE TNV displayed a lower CNR and/or lower spatial resolution depending on the reconstruction parameters. Regarding the SSIM, DE TNV was superior to FDK and SE TV for both phantoms. The additional beam filtration for the IQP led to improved image quality in all metrics, surpassing the SE TV results in CNR and spatial resolution.</jats:p>