A Three-dimensional Deformable Brain Atlas for DBS Targeting. I. Methodology for Atlas Creation and Artifact Reduction.

BackgroundTargeting in deep brain stimulation (DBS) relies heavily on the ability to accurately localize particular anatomic brain structures. Direct targeting of subcortical structures has been limited by the ability to visualize relevant DBS targets.Methods and resultsIn this work, we describe the development and implementation, of a methodology utilized to create a three dimensional deformable atlas for DBS surgery. This atlas was designed to correspond to the print version of the Schaltenbrand-Bailey atlas structural contours. We employed a smoothing technique to reduce artifacts inherent in the print version.ConclusionsWe present the methodology used to create a three dimensional patient specific DBS atlas which may in the future be tested for clinical utility

Respiration-Induced Intraorgan Deformation of the Liver: Implications for Treatment Planning in Patients Treated With Fiducial Tracking.

Stereotactic body radiation therapy is a well-tolerated modality for the treatment of primary and metastatic liver lesions, and fiducials are often used as surrogates for tumor tracking during treatment. We evaluated respiratory-induced liver deformation by measuring the rigidity of the fiducial configuration during the breathing cycle. Seventeen patients, with 18 distinct treatment courses, were treated with stereotactic body radiosurgery using multiple fiducials. Liver deformation was empirically quantified by measuring the intrafiducial distances at different phases of respiration. Data points were collected at the 0%, 50%, and 100% inspiration points, and the distance between each pair of fiducials was measured at the 3 phases. The rigid body error was calculated as the maximum difference in the intrafiducial distances. Liver disease was calculated with Child-Pugh score using laboratory values within 3 months of initiation of treatment. A peripheral fiducial was defined as within 1.5 cm of the liver edge, and all other fiducials were classified as central. For 5 patients with only peripheral fiducials, the fiducial configuration had more deformation (average maximum rigid body error 7.11 mm, range: 1.89-11.35 mm) when compared to patients with both central and peripheral and central fiducials only (average maximum rigid body error 3.36 mm, range: 0.5-9.09 mm, P = .037). The largest rigid body errors (11.3 and 10.6 mm) were in 2 patients with Child-Pugh class A liver disease and multiple peripheral fiducials. The liver experiences internal deformation, and the fiducial configuration should not be assumed to act as a static structure. We observed greater deformation at the periphery than at the center of the liver. In our small data set, we were not able to identify cirrhosis, which is associated with greater rigidity of the liver, as predictive for deformation. Treatment planning based only on fiducial localization must take potential intraorgan deformation into account

Work-worlds colliding: Self-reflexivity, power and emotion in organizational ethnography

While organizational ethnographers have embraced the concept of self-reflexivity, problems remain. In this article we argue that the prevalent assumption that self-reflexivity is the sole responsibility of the individual researcher limits its scope for understanding organizations. To address this, we propose an innovative method of collective reflection that is inspired by ideas from cultural and feminist anthropology. The value of this method is illustrated through an analysis of two ethnographic case studies, involving a ‘pair interview’ method. This collective approach surfaced self-reflexive accounts, in which aspects of the research encounter that still tend to be downplayed within organizational ethnographies, including emotion, intersubjectivity and the operation of power dynamics, were allowed to emerge. The approach also facilitated a second contribution through the conceptualization of organizational ethnography as a unique endeavour that represents a collision between one ‘world of work’: the university, with a second: the researched organization. We find that this ‘collision’ exacerbates the emotionality of ethnographic research, highlighting the refusal of ‘researched’ organizations to be domesticated by the specific norms of academia. Our article concludes by drawing out implications for the practice of self-reflexivity within organizational ethnography

Determination of mean ionization potential using magnetic resonance imaging for the reduction of proton beam range uncertainties: theory and application.

The accurate determination of mean ionization potential (I m) has the potential to reduce range uncertainty based margins and therefore allow for more focal treatments in proton radiotherapy. Many methods have been proposed to reduce uncertainty in I m and stopping power ratios (SPR), each with varying degrees of accuracy and issues. In this work, we present a simple parameterized model to determine I m in human biological tissue, allowing for the computation of patient-specific I m at the voxel level using magnetic resonance imaging (MRI). The model requires the measurement of three parameters by MRI, with only two parameters, mass percent water content and mass percent hydrogen content in organic molecules, required for the special case of soft tissues. The accuracy of this I m determination method was evaluated in available 'standard' (ICRU Report #44, (ICRU 1989 Tissue Substitutes in Radiation Dosimetry and Measurement (Bethesda, MD: International Commission on Radiation Units and Measurements))) human tissues. The sensitivity of this I m determination method to in vivo perturbations was also tested by calculating the effect of 10% variations in the experimentally measurable parameters on I m and SPR. For the human tissues modeled in this work, a high level of accuracy with low susceptibility to perturbations in measurement error was achieved in the prediction of I m. Root-mean-square errors in I m were within 0.77% and 1.8% for both soft and bony tissues, and were 0.09% and 0.2% for the SPR of soft and bony tissues, respectively, assuming knowledge of electron density. Proof of principle MR measurements and model-based computations of I m and SPR were taken in phantom for a series of hydrogenous solutions and compared against expected I m and SPR calculations from known elemental composition. MR determined I m and SPR values in a known composition solution were determined to within 5% and 0.52%, respectively. We present a novel model to accurately calculate mean ionization potential from measurements acquirable by MRI and show its feasibility in a phantom

On the molecular relationship between Hounsfield Unit (HU), mass density, and electron density in computed tomography (CT).

Accurate determination of physical/mass and electron densities are critical to accurate spatial and dosimetric delivery of radiotherapy for photon and charged particles. In this manuscript, the biology, chemistry, and physics that underly the relationship between computed tomography (CT) Hounsfield Unit (HU), mass density, and electron density was explored. In standard radiation physics practice, quantities such as mass and electron density are typically calculated based off a single kilovoltage CT (kVCT) scan assuming a one-to-one relationship between HU and density. It is shown that, in absence of mass density assumptions on tissues, the relationship between HU and density is not one-to-one with uncertainties as large as 7%. To mitigate this uncertainty, a novel multi-dimensional theoretical approach is defined between molecular (water, lipid, protein, and mineral) composition, HU, mass density, and electron density. Empirical parameters defining this relationship are x-ray beam energy/spectrum dependent and, in this study, two methods are proposed to solve for them including through a tissue mimicking phantom calibration process. As a proof of concept, this methodology was implemented in a separate in-house created tissue mimicking phantom and it is shown that sub 1% accuracy is possible for both mass and electron density. As molecular composition is not always known, the sensitivity of this model to uncertainties in molecular composition was investigated and it was found that, for soft tissue, sub 1% accuracy is achievable assuming nominal organ/tissue compositions. For boney tissues, the uncertainty in mineral content may lead to larger errors in mass and electron density compared with soft tissue. In this manuscript, a novel methodology to directly determine mass and electron density based off CT HU and knowledge of molecular compositions is presented. If used in conjunction with a methodology to determine molecular compositions, mass and electron density can be accurately calculated from CT HU

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

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