27 research outputs found
A Millimeter-scale Single Charged Particle Dosimeter for Cancer Radiotherapy
This paper presents a millimeter-scale CMOS 6464 single charged
particle radiation detector system for external beam cancer radiotherapy. A
11 diode measures energy deposition by a single charged
particle in the depletion region, and the array design provides a large
detection area of 512512 . Instead of sensing the voltage drop
caused by radiation, the proposed system measures the pulse width, i.e., the
time it takes for the voltage to return to its baseline. This obviates the need
for using power-hungry and large analog-to-digital converters. A prototype ASIC
is fabricated in TSMC 65 nm LP CMOS process and consumes the average static
power of 0.535 mW under 1.2 V analog and digital power supply. The
functionality of the whole system is successfully verified in a clinical 67.5
MeV proton beam setting. To our' knowledge, this is the first work to
demonstrate single charged particle detection for implantable in-vivo
dosimetry
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Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms.
Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties
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Multimodal imaging and deep learning-based methods for improved dose calculation accuracy in photon and proton radiotherapy
Radiotherapy is one of the most common techniques used to treat cancer and is administered to over 60% of patients treated in the US. Computed tomography (CT) and magnetic resonance imaging (MRI) are powerful imaging modalities which have widespread applications in radiotherapy. Given the different underlying physics behind how image contrast is generated in kilovoltage CTs (kVCTs), megavoltage CTs (MVCTs) and MRIs, each has their own inherent strengths and limitations. This dissertation includes four primary projects which use advanced image processing and deep learning methods to exploit the unique advantages of MRI, kVCT, and MVCT for improving dose calculation accuracy of photon and proton radiotherapy. The first project includes a novel multimodal imaging method used to estimate proton stopping power ratio using a combination of MRI and CT. Results show that our multimodal imaging approach using MRI and MVCT provided results within 1% of physical measurements. The second project includes the first work to synthesize MVCT images from MRI using a deep learning model in which the feasibility of utilizing MRI-derived synthetic CTs for radiotherapy treatment planning of head and neck cancer was demonstrated. This work lays the foundation for a proposed paradigm shift whereby MRI is utilized for anatomical delineation while MRI-derived synthetic MVCT is used for radiotherapy dose calculation, which can all be performed on the treatment machine and could provide substantial improvements to combined MRI-linear accelerator workflow and accessibility. The third project includes the first known application of deep learning to reduce uncertainty in the relationship between kVCT and electron density and stopping power ratio through learning of MVCT data, promising improvements for dose calculation accuracy. The fourth project includes clinical considerations and recommendations for implementing a commercial algorithm for producing MRI-derived synthetic CTs in a dataset of patients treated for prostate cancer
Structural basis for the assembly of the mitotic motor Kinesin-5 into bipolar tetramers.
Chromosome segregation during mitosis depends upon Kinesin-5 motors, which display a conserved, bipolar homotetrameric organization consisting of two motor dimers at opposite ends of a central rod. Kinesin-5 motors crosslink adjacent microtubules to drive or constrain their sliding apart, but the structural basis of their organization is unknown. In this study, we report the atomic structure of the bipolar assembly (BASS) domain that directs four Kinesin-5 subunits to form a bipolar minifilament. BASS is a novel 26-nm four-helix bundle, consisting of two anti-parallel coiled-coils at its center, stabilized by alternating hydrophobic and ionic four-helical interfaces, which based on mutagenesis experiments, are critical for tetramerization. Strikingly, N-terminal BASS helices bend as they emerge from the central bundle, swapping partner helices, to form dimeric parallel coiled-coils at both ends, which are offset by 90°. We propose that BASS is a mechanically stable, plectonemically-coiled junction, transmitting forces between Kinesin-5 motor dimers during microtubule sliding. DOI: http://dx.doi.org/10.7554/eLife.02217.001
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Generation of synthetic megavoltage CT for MRI-only radiotherapy treatment planning using a 3D deep convolutional neural network.
BACKGROUND: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation. PURPOSE: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning. METHODS: MVCTs and T1-weighted MRIs for 120 patients treated for head-and-neck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs). RESULTS: MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively. CONCLUSIONS: MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow
Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle‐consistent generative machine learning
PurposeMegavoltage computed tomography (MVCT) has been implemented on many radiation therapy treatment machines as a tomographic imaging modality that allows for three-dimensional visualization and localization of patient anatomy. Yet MVCT images exhibit lower contrast and greater noise than its kilovoltage CT (kVCT) counterpart. In this work, we sought to improve these disadvantages of MVCT images through an image-to-image-based machine learning transformation of MVCT and kVCT images. We demonstrated that by learning the style of kVCT images, MVCT images can be converted into high-quality synthetic kVCT (skVCT) images with higher contrast and lower noise, when compared to the original MVCT.MethodsKilovoltage CT and MVCT images of 120 head and neck (H&N) cancer patients treated on an Accuray TomoHD system were retrospectively analyzed in this study. A cycle-consistent generative adversarial network (CycleGAN) machine learning, a variant of the generative adversarial network (GAN), was used to learn Hounsfield Unit (HU) transformations from MVCT to kVCT images, creating skVCT images. A formal mathematical proof is given describing the interplay between function sensitivity and input noise and how it applies to the error variance of a high-capacity function trained with noisy input data. Finally, we show how skVCT shares distributional similarity to kVCT for various macro-structures found in the body.ResultsSignal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were improved in skVCT images relative to the original MVCT images and were consistent with kVCT images. Specifically, skVCT CNR for muscle-fat, bone-fat, and bone-muscle improved to 14.8 ± 0.4, 122.7 ± 22.6, and 107.9 ± 22.4 compared with 1.6 ± 0.3, 7.6 ± 1.9, and 6.0 ± 1.7, respectively, in the original MVCT images and was more consistent with kVCT CNR values of 15.2 ± 0.8, 124.9 ± 27.0, and 109.7 ± 26.5, respectively. Noise was significantly reduced in skVCT images with SNR values improving by roughly an order of magnitude and consistent with kVCT SNR values. Axial slice mean (S-ME) and mean absolute error (S-MAE) agreement between kVCT and MVCT/skVCT improved, on average, from -16.0 and 109.1 HU to 8.4 and 76.9 HU, respectively.ConclusionsA kVCT-like qualitative aid was generated from input MVCT data through a CycleGAN instance. This qualitative aid, skVCT, was robust toward embedded metallic material, dramatically improves HU alignment from MVCT, and appears perceptually similar to kVCT with SNR and CNR values equivalent to that of kVCT images
The effects of multi-tasking on psychological stress reactivity in recreational drug user of cannabis and MDMA
Background: Cannabis and MDMA use is associated with psychobiological and neurocognitive deficits. Assessments of the latter typically include tests of memory and everyday cognitive functioning. However, to date little attention has been paid to effects of drug use on psychological stress reactivity. We report three studies examining the effects of recreational use of cannabis and MDMA on mood and psychological responses to multi-tasking using a cognitively demanding laboratory stressor that provides an analogue for everyday situations involving responses to multiple stimuli. Methods: The effects of the Multi-tasking Framework on mood and perceived workload were assessed in cannabis (N=25), younger (N=18) and older (N=20) MDMA users and compared with non-target drug controls. Results: Compared with respective control groups, cannabis users became less alert and content and both MDMA groups became less calm following acute stress. Unexpectedly the stressor increased ratings of calm in cannabis users. Users also scored higher than their controls with respect to ratings of resources needed to complete the multi-tasking framework. Conclusions: These findings show, for the first time, that recreational use of cannabis and MDMA, beyond the period of intoxication, can negatively influence psychological responses to a multi-tasking stressor and this may have implications for real-life situations which place high demands on cognitive resources