A Millimeter-scale Single Charged Particle Dosimeter for Cancer Radiotherapy

This paper presents a millimeter-scale CMOS 64$\times$64 single charged particle radiation detector system for external beam cancer radiotherapy. A 1$\times$1 $\mu m^2$ diode measures energy deposition by a single charged particle in the depletion region, and the array design provides a large detection area of 512$\times$512 $\mu m^2$. 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

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

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

Technical Note: A methodology for improved accuracy in stopping power estimation using MRI and CT

PurposeProton therapy is becoming an increasingly popular cancer treatment modality due to the proton's physical advantage in that it deposits the majority of its energy at the distal end of its track where the tumor is located. The proton range in a material is determined from the stopping power ratio (SPR) of the material. However, SPR is typically estimated based on a computed tomography (CT) scan which can lead to range estimation errors due to the difference in x-ray and proton interactions in matter, which can preclude the ability to utilize protons to their full potential. Applications of magnetic resonance imaging (MRI) in radiotherapy have increased over the past decade and using MRI to calculate SPR directly could provide numerous advantages. The purpose of this study was to develop a practical implementation of a novel multimodal imaging method for estimating SPR and compare the results of this method to physical measurements in which values were computed directly using tissue substitute materials fabricated to mimic skin, muscle, adipose, and spongiosa bone.MethodsFor both the multimodal imaging method and physical measurements, SPR was calculated using the Bethe-Bloch equation from values of relative electron density and mean ionization potential determined for each tissue. Parameters used to estimate SPR using the multimodal imaging method were extracted from Dixon water-only and (1 H) proton density-weighted zero echo time MRI sequences and CT, with both kVCT and MVCT used separately to evaluate the performance of each. For comparison, SPR was also computed from kVCT using the stoichiometric method, the current clinical standard.ResultsResults showed that our multimodal imaging approach using MRI with either kVCT or MVCT was in close agreement to SPR calculated from physical measurements for the four tissue substitutes evaluated. Using MRI and MVCT, SPR values estimated using our method were within 1% of physical measurements and were more accurate than the stoichiometric method for the tissue types studied.ConclusionsWe have demonstrated the methodology for improved estimation of SPR using the proposed multimodal imaging framework