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<jats:title>Abstract</jats:title> <jats:p>The use of field-specific apertures, routine in scattered or uniform-scanned proton fields, are still a necessity in pencil-beam scanned (PBS) fields to sharpen the penumbral edge at low energies and in high fraction dose application beyond that achievable with small spot size. We describe a model implemented in our clinical pencil-beam algorithm that models the insertion of a shaped aperture, including shapes adapted per energy layer such as may be achieved with a multi-leaf collimator. The model decomposes the spot transport into discrete steps. The first step transport a uniform intensity field of high-resolution sub-pencil-beams at the layer energy through the medium. This transport only considers primary scattering in both the patient and an optional range-shifter. The second step models the aperture areas and edge penumbral transition as a modulation of the uniform intensity. The third step convolves individual steps over the uniform-transported field including the aperture-modified intensities. We also introduce an efficient model based on a Clarkson sector integration for nuclear scattered halo protons. This avoids the explicit modeling of long range halo protons to the detriment of computational efficiency in calculation and optimization. We demonstrate that the aperture effect is primarily due to in-patient and shifter scattering with a small contribution from the apparent beam source position. The model provides insight into the primary physics contributions to the penumbra and the nuclear halo. The model allowed us to fully deploy our PBS capacity at our two-gantry center without which PBS treatments would have been inferior compared to scattered fields with apertures. Finally, Monte Carlo calculations have (nearly) replaced phenomenological pencil-beam models for collimated fields. Phenomenological models do, however, allow exposition of underlying clinical phenomena and closer connection to representative clinical observables.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Coincidence timing resolution (CTR) is an important parameter in clinical positron emission tomography (PET) scanners to increase the signal-to-noise ratio of PET images by using time-of-flight (TOF) information. Lutetium (Lu) based scintillators are often used for TOF-PET systems. However, the self-radiation of Lu-based scintillators may influence the image quality for ultra-low activity PET imaging. Recently, a gadolinium fine aluminum gallate (Ce:GFAG) scintillation crystal that features a fast decay time (∼55 ns) and no self-radiation was developed. The present study aimed at optimizing the GFAG crystal surface treatment to enhance both CTR and energy resolution (ER). The TOF-PET detector consisted of a GFAG crystal (3.0 × 3.0 × 20 mm<jats:sup>3</jats:sup>) and a SiPM with an effective area of 3.0 × 3.0 mm<jats:sup>2</jats:sup>. The timing and energy signals were extracted using a high-frequency SiPM readout circuit and then were digitized using a CAMAC DAQ system. The CTR and ER were evaluated with nine different crystal surface treatments such as partial saw-cut and chemical polishing and the 1-side saw-cut was the best choice among the treatments. The respective CTR and ER of 202 ± 2 ps and 9.5 ± 0.1% were obtained with the 1-side saw-cut; the other 5-side mechanically polished GFAG crystals had respective values which were 18 ps (9.0%) and 1.3% better than those of the all-side mechanically polished GFAG crystal. The chemically polished GFAG crystals also offered enhanced CTR and ER of about 17 ps (8.2%) and 2.1%, respectively, over the mechanically polished GFAG crystals.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective</jats:italic>. Synchrony<jats:sup>®</jats:sup> is a motion management system on the Radixact<jats:sup>®</jats:sup> that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. <jats:italic>Approach</jats:italic>. A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to the imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. <jats:italic>Main Results</jats:italic>. The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. <jats:italic>Significance</jats:italic>. This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>By using the statistical techniques of the ANOVA means test and regression, it was found that the <jats:inline-formula> <jats:tex-math> <?CDATA ${N}_{{K}_{R}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn1.gif" xlink:type="simple" /> </jats:inline-formula> calibration factor of Standard Imaging (SI) model HDR 1000 plus chambers presents a quadratic dependence with the Reference air kerma rate <jats:inline-formula> <jats:tex-math> <?CDATA ${K}_{R}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn2.gif" xlink:type="simple" /> </jats:inline-formula> (from 6.9 mGy h<jats:sup>−1</jats:sup> to 43.9 mGy h<jats:sup>−1</jats:sup>). In order to understand and correct this dependency one model is presented for total recombination: <jats:inline-formula> <jats:tex-math> <?CDATA ${k}_{s}=\displaystyle \frac{{I}_{300}}{{I}_{150}}\,=1+{k}_{ini}+{k}_{d}+{k}_{{\rm{vol}}}\,\cdot \,{I}_{300}+{k}_{screen}\,\cdot \,{I}_{300}^{2},$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>s</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mstyle displaystyle="true"> <mml:mfrac> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>300</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>150</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:mfrac> </mml:mstyle> <mml:mspace width="0.25em" /> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi> <mml:mi>i</mml:mi> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>d</mml:mi> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">vol</mml:mi> </mml:mrow> </mml:msub> <mml:mspace width=".25em" /> <mml:mo>·</mml:mo> <mml:mspace width=".25em" /> <mml:msub> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>300</mml:mn> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>s</mml:mi> <mml:mi>c</mml:mi> <mml:mi>r</mml:mi> <mml:mi>e</mml:mi> <mml:mi>e</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> <mml:mspace width=".25em" /> <mml:mo>·</mml:mo> <mml:mspace width=".25em" /> <mml:msubsup> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>300</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msubsup> <mml:mo>,</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn3.gif" xlink:type="simple" /> </jats:inline-formula> where <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{k}}}_{{\rm{ini}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">ini</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn4.gif" xlink:type="simple" /> </jats:inline-formula> is the initial recombination, <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{k}}}_{{\rm{vol}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">vol</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn5.gif" xlink:type="simple" /> </jats:inline-formula> the thermal diffusion recombination, <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{k}}}_{{\rm{vol}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">vol</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn6.gif" xlink:type="simple" /> </jats:inline-formula> the volumetric recombination and <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{k}}}_{{\rm{screen}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">screen</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn7.gif" xlink:type="simple" /> </jats:inline-formula> the screening for the currents/charges collected at the potential differences of 300 and 150 V. In conclusion, the total recombination <jats:inline-formula> <jats:tex-math> <?CDATA ${k}_{s}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>s</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn8.gif" xlink:type="simple" /> </jats:inline-formula> is composed by one <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{k}}}_{{\rm{ini}}}\,$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">ini</mml:mi> </mml:mrow> </mml:msub> <mml:mspace width=".25em" /> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn9.gif" xlink:type="simple" /> </jats:inline-formula>with a constant contribution of 0.019%, one <jats:italic>k</jats:italic> <jats:sub> <jats:italic>d</jats:italic> </jats:sub> contribution of 0.017%, one <jats:inline-formula> <jats:tex-math> <?CDATA ${k}_{{\rm{vol}}}\,\cdot \,{I}_{300}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">vol</mml:mi> </mml:mrow> </mml:msub> <mml:mspace width=".25em" /> <mml:mo>·</mml:mo> <mml:mspace width=".25em" /> <mml:msub> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>300</mml:mn> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn10.gif" xlink:type="simple" /> </jats:inline-formula> contribution from 0.022% to 0.138%, and the <jats:inline-formula> <jats:tex-math> <?CDATA ${k}_{screen}\,\cdot \,{I}_{300}^{2}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>s</mml:mi> <mml:mi>c</mml:mi> <mml:mi>r</mml:mi> <mml:mi>e</mml:mi> <mml:mi>e</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> <mml:mspace width=".25em" /> <mml:mo>·</mml:mo> <mml:mspace width=".25em" /> <mml:msubsup> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>300</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn11.gif" xlink:type="simple" /> </jats:inline-formula> effects from 0.002% to 0.09% in the range of <jats:inline-formula> <jats:tex-math> <?CDATA ${\dot{{\rm{K}}}}_{{\rm{R}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mi mathvariant="normal">K</mml:mi> <mml:mo>̇</mml:mo> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn12.gif" xlink:type="simple" /> </jats:inline-formula> rate above. However, when this model for <jats:inline-formula> <jats:tex-math> <?CDATA ${k}_{s}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>s</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn13.gif" xlink:type="simple" /> </jats:inline-formula> is applied to try to correct the quadratic dependence of the <jats:inline-formula> <jats:tex-math> <?CDATA ${N}_{{K}_{R}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn14.gif" xlink:type="simple" /> </jats:inline-formula> <jats:italic>versus</jats:italic> <jats:inline-formula> <jats:tex-math> <?CDATA ${K}_{R},$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn15.gif" xlink:type="simple" /> </jats:inline-formula> explicitly there is no improvement in the variation range of 0.5% of the <jats:inline-formula> <jats:tex-math> <?CDATA ${N}_{{K}_{R}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn16.gif" xlink:type="simple" /> </jats:inline-formula> <jats:italic>versus</jats:italic> <jats:inline-formula> <jats:tex-math> <?CDATA ${K}_{R}.$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> </mml:msub> <mml:mo>.</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn17.gif" xlink:type="simple" /> </jats:inline-formula> Nonetheless, it allows to obtain <jats:inline-formula> <jats:tex-math> <?CDATA ${N}_{{K}_{R}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn18.gif" xlink:type="simple" /> </jats:inline-formula> values consistent with a <jats:inline-formula> <jats:tex-math> <?CDATA ${u}_{c}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>u</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>c</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac4c2aieqn19.gif" xlink:type="simple" /> </jats:inline-formula> ≤ 0.7%, which is less than 1.25% reported in the literature by ADCLs or SSDLs.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>It is suggested that experience is needed in order to capture valid estimates of muscle size with ultrasound. However, it is unknown whether there is a large degree of skill needed to analyze the images once they are captured. <jats:italic>Objective.</jats:italic> To determine if less experienced raters could accurately analyze ultrasound images of the forearm by comparing their estimates with those of a very experienced ultrasonographer (criterion). <jats:italic>Approach.</jats:italic> 50 muscle thickness images were captured by one experienced ultrasonographer (also Rater 1). Those images were saved and were then measured by four raters with different levels of experience. The rater who captured the images was very experienced (criterion), the second rater was also experienced and provided 5 minutes of instruction for Rater 3 (minimal experience) and Rater 4 (no experience). Test-retest reliability was also determined for Rater 3 and 4. <jats:italic>Main Results.</jats:italic> The average muscle thickness value for the criterion was 3.73 cm. The constant error for Rater 2, 3, and 4 was −0.003 (0.02) cm (<jats:italic>p</jats:italic> = 0.362), −0.07 (0.04) cm (<jats:italic>p</jats:italic> &lt; 0.001), and 0.02 (0.09) cm (<jats:italic>p</jats:italic> = 0.132), respectively. The SD for Rater 4 was greater, resulting in wider limits of agreement compared to Rater 2 and 3. Absolute error was 0.01 cm for Rater 2, whilst it was 0.07 cm and 0.03 cm for the two inexperienced raters (Rater 3 and 4). The error for Rater 3 was systematic and post-hoc assessment found that this rater was using a different border than the other three raters (but consistent across time). For the test-retest reliability, the minimal difference for Rater 3 was 0.08 cm (relative minimal difference of 2%) and 0.17 cm (relative minimal difference of 4%) for Rater 4. <jats:italic>Significance.</jats:italic> Less experienced raters were able to accurately and reliably analyze already captured muscle thickness images of the forearm with low absolute errors.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The objective of this study was to confirm the feasibility of three-dimensionally-printed (3D-printed), personalized whole-body anthropomorphic phantoms for radiation dose measurements in a variety of charged and uncharged particle radiation fields. We 3D-printed a personalized whole-body phantom of an adult female with a height of 154.8 cm, mass of 90.7 kg, and body mass index of 37.8 kg/m<jats:sup>2</jats:sup>. The phantom comprised of a hollow plastic shell filled with water and included a watertight access conduit for positioning dosimeters. It is compatible with a wide variety of radiation dosimeters, including ionization chambers that are suitable for uncharged and charged particles. Its mass was 6.8 kg empty and 98 kg when filled with water. Watertightness and mechanical robustness were confirmed after multiple experiments and transportations between institutions. The phantom was irradiated to the cranium with therapeutic beams of 170-MeV protons, 6-MV photons, and fast neutrons. Radiation absorbed dose was measured from the cranium to the pelvis along the longitudinal central axis of the phantom. The dose measurements were made using established dosimetry protocols and well-characterized instruments. For the therapeutic environments considered in this study, stray radiation from intracranial treatment beams was the lowest for proton therapy, intermediate for photon therapy, and highest for neutron therapy. An illustrative example set of measurements at the location of the thyroid for a square field of 5.3 cm per side resulted in 0.09, 0.59, and 1.93 cGy/Gy from proton, photon, and neutron beams, respectively. In this study, we found that 3D-printed personalized phantoms are feasible, inherently reproducible, and well-suited for therapeutic radiation measurements. The measurement methodologies we developed enabled the direct comparison of radiation exposures from neutron, proton, and photon beam irradiations.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objectives</jats:italic>. Increased radiation doses could improve local control and overall survival of lung cancer patients, however, this could be challenging without exceeding organs at risk (OAR) dose constraints, especially for patients with advanced-stage disease. Increasing OAR doses could reduce the therapeutic ratio and quality of life. It is therefore important to investigate methods to increase the dose to target volume without exceeding OAR dose constraints. <jats:italic>Methods</jats:italic>. Gross tumour volume (GTV) was contoured on synthetic computerised tomography (sCT) datasets produced using the Velocity adaptive radiotherapy software for eleven patients. The fractions where GTV volume decreased compared to that prior to radiotherapy (reference plan) were considered for personalised progressive dose escalation. The dose to the adapted GTV (GTV<jats:sub>Adaptive</jats:sub>) was increased until OAR doses were affected (as compared to the original clinical plan). Planning target volume (PTV) coverage was maintained for all plans. Doses were also escalated to the reference plan (GTV<jats:sub>Clinical</jats:sub>) using the same method. Adapted, dose-escalated, plans were combined to estimate accumulated dose, D<jats:sub>99</jats:sub> (dose to 99%) of GTV<jats:sub>Adapted</jats:sub>, PTV D<jats:sub>99</jats:sub> and OAR doses and compared with those in the original clinical plans. Knowledge-based planning (KBP) model was developed to predict D<jats:sub>99</jats:sub> of the adapted GTV with OAR doses and PTV coverage kept similar to the original clinical plans; prediction accuracy and model verification were performed using further data sets. <jats:italic>Results</jats:italic>. Compared to the original clinical plan, the dose to GTV was significantly increased without exceeding OAR doses. Adaptive dose-escalation increased the average D<jats:sub>99</jats:sub> to GTV<jats:sub>Adaptive</jats:sub> by 15.1Gy and 8.7Gy compared to the clinical plans. The KBP models were verified and demonstrated prediction accuracy of 0.4% and 0.7% respectively. <jats:italic>Conclusion</jats:italic>. Progressive adaptive dose escalation can significantly increase the dose to GTV without increasing OAR doses or compromising the dose to microscopic disease. This may increase overall survival without increasing toxicities.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>For nerve tissue engineering (NTE), scaffolds with the ability to release drugs under control and support the rapid proliferation of cells are very important for the repair of nerve defects. This study aimed to fabricate a conductive drug-loaded fiber mat by electrospinning and assess its potential as a scaffold for Schwann cells proliferation. The conductive polypyrrole (PPy) was coated on an electrospun poly (D, L-lactide) (PLA) fibrous mat, which was simultaneously embedded with protein-loaded chitosan nanoparticles and ibuprofen as a model small molecule drug. The fibrous mat shows suitable conductivity, mechanical properties, and hydrophilicity for NTE. For drug release and degradation studies, the fibrous mat can achieve sustained release of bovine serum albumin (BSA) and ibuprofen, and the PPy coating can increase the surface wettability and conductivity while slowing down the degradation of the fibrous mat. The application of electrical stimulation (ES) to the fibrous mat can accelerate the release of ibuprofen, but there was no significant effect on the release rate of the protein. The fibrous mat showed no cytotoxicity <jats:italic>in vitro</jats:italic>, and Schwann cells (SCs) can adhere, grow, and proliferate well on mats. At the 120th hour of culture <jats:italic>in vitro</jats:italic>, the relative growth rate of SCs on the conductive drug-loaded fibrous mat reached 198.22 ± 2.34%, which was an increase of 37.93% compared to the SCs on the drug-loaded fibrous mat with ES. The density and elongation of SCs on the conductive drug-loaded fibrous mat were greater than those on the PLA fibrous mat, indicating that the conductive polypyrrole-coated electrospun chitosan nanoparticles/PLA fibrous mat has good potential for application in nerve regeneration.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Multi-leaf collimator dose leakage in intensity-modulated radiotherapy (IMRT) plans causes higher low dose volume which increases the long-term risks of radiotherapy. We have developed Fluence Map Analyzer (FMA) program that suggests the ideal field geometry to reduce low dose volume in locally advanced breast cancer IMRT plans. In this comparative experimental study, FMA has been applied to standard IMRT plans (STD-IMRT) of randomly selected 15 left and 15 right-sided locally advanced breast cancer patients. All patients underwent a modified radical mastectomy. The chest wall, IMN, axillary, and supraclavicular lymph nodes are included in planning target volume (PTV). The heart, lungs, contralateral breast, and medulla spinalis were delineated as organs at risk (OARs). Two sets of plans, namely STD-IMRT and FMA-IMRT, were generated for each patient. The dosimetric analysis was performed using dose-volume histogram (DVH) and standard evaluation parameters of PTV and OARs. No differences could be observed among the two techniques for PTV coverage. However, FMA-IMRT plans achieved significantly lower V<jats:sub>5</jats:sub> volumes and mean doses of the heart, lungs, contralateral breast, and body contours. FMA-IMRT used a smaller number of sub-fields and fewer monitor units (MU). FMA automizes the field geometry determination process for locally advanced breast cancer IMRT planning while reducing low dose volume significantly.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> The selective multiple quantum coherence (Sel-MQC) sequence is a magnetic resonance spectroscopy (MRS) technique used to detect lactate and suppress co-resonant lipid signals <jats:italic>in vivo</jats:italic>. The coherence pathways of J-coupled lipids upon the sequence, however, have not been studied, hindering a logical design of the sequence to fully attenuate lipid signals. The objective of this study is to elucidate the coherence pathways of J-coupled lipids upon the Sel-MQC sequence and find a strategy to effectively suppress lipid signals from these pathways while keeping the lactate signal. <jats:italic>Approach.</jats:italic> The product operator formalism was used to express the evolutions of the J-coupled spins of lipids and lactate. The transformations of the product operators by the spectrally selective pulses of the sequence were calculated by using the off-resonance rotation matrices. The coherence pathways and the conversion rates of the individual pathways were derived from them. Experiments were performed on phantoms and two human subjects at 3 T. <jats:italic>Main results.</jats:italic> The coherence pathways contributing to the various lipid resonance signals by the Sel-MQC sequence depending on the gradient ratios and RF pulse lengths were identified. Theoretical calculations of the signals from the determined coherence pathways and signal attenuations by gradients matched the experimental data very well. Lipid signals from fatty tissues of the subjects were successfully suppressed to the noise level by using the gradient ratio −0.8:−1:2 or 1:0.8:2. The new gradient ratios kept the lactate signal the same as with the previously used gradient ratio 0:−1:2. <jats:italic>Significance.</jats:italic> The study has elucidated the coherence pathways of J-coupled lipids upon the Sel-MQC sequence and demonstrated how lipid signals can be effectively suppressed while keeping lactate signals by using information from the coherence pathway analysis. The findings enable applying the Sel-MQC sequence to lactate detection in an environment of high concentrations of lipids.</jats:p>