Observing early metabolic changes in positron emission tomography (PET) is an essential tool to assess treatment efficiency in radiotherapy. However, for thoracic regions, the use of three-dimensional (3D) PET imaging is unfeasible because the radiotracer activity is smeared by the respiratory motion and averaged during the imaging acquisition process. This motion-induced degradation is similar in magnitude with the treatment-induced changes, and the two occurrences become indiscernible. We present a customized temporal-spatial deformable registration method for quantifying respiratory motion in a four-dimensional (4D) PET dataset. Once the motion is quantified, a motion-corrected (MC) dataset is created by tracking voxels to eliminate breathing-induced changes in the 4D imaging scan. The 4D voxel-tracking data is then summed to yield a 3D MC-PET scan containing only treatment-induced changes. This proof of concept is exemplified on both phantom and clinical data, where the proposed algorithm tracked the trajectories of individual points through the 4D datasets reducing motion to less than 4 mm in all phases. This correction approach using deformable registration can discern motion blurring from treatment-induced changes in treatment response assessment using PET imaging.

Deformable (non-rigid) registration is an essential tool in both adaptive radiation therapy and image-guided radiation therapy to account for soft-tissue changes during the course of treatment. The evaluation method most commonly used to assess the accuracy of deformable image registration is qualitative human evaluation. Here, we propose a method for systematically measuring the accuracy of an algorithm in recovering artificially introduced deformations in cases of rigid geometry, and we use that method to quantify the ability of a modified basis spline (B-Spline) registration algorithm to recover artificially introduced deformations. The evaluation method is entirely computer-driven and eliminates biased interpretation associated with human evaluation; it can be applied to any chosen method of image registration. Our method involves using planning computed tomography (PCT) images acquired with a conventional CT simulator and cone-beam computed tomography (CBCT) images acquired daily by a linear accelerator-mounted kilovoltage image system in the treatment delivery room. The deformation that occurs between the PCT and daily CBCT images is obtained using a modified version of the B-Spline deformable model designed to overcome the low soft-tissue contrast and the artifacts and distortions observed in CBCT images. Clinical CBCT images and contours of phantom and central nervous system cases were deformed (warped) with known random deformations. In registering the deformed with the non-deformed image sets, we tracked the algorithm's ability to recover the original, non-deformed set. Registration error was measured as the mean and maximum difference between the original and the registered surface contours from outlined structures. Using this approach, two sets of tests can be devised. To measure the residual error related to the optimizer's convergence performance, the warped CBCT image is registered to the unwarped version of itself, eliminating unknown factors such as noise and positioning errors. To study additional errors introduced by artifacts and noise in the CBCT image, the warped CBCT image is registered to the original PCT image. Using a B-Spline deformable image registration algorithm, mean residual error introduced by the algorithm's performance on noise-free images was less than 1 mm, with a maximum of 2 mm. The chosen deformable image registration model was capable of accommodating significant variability in structures over time, because the artificially introduced deformation magnitude did not significantly influence the residual error. On the second type of test, noise and artifacts reduced registration accuracy to a mean of 1.33 mm and a maximum of 4.86 mm. The accuracy of deformable image registration can be easily and consistently measured by evaluating the algorithm's ability to recover artificially introduced deformations in rigid cases in which the true solution is known a priori. The method is completely automated, applicable to any chosen registration algorithm, and does not require user interaction of any kind.

Purpose: Glioblastoma (GBM) neurosurgical resection relies on contrast-enhanced MRI-based neuronavigation. However, it is well-known that infiltrating tumor extends beyond contrast enhancement. Fluorescence-guided surgery (FGS) using 5-aminolevulinic acid (5-ALA) was evaluated to improve extent of resection (EOR) of GBMs. Preoperative morphological tumor metrics were also assessed. Procedures: Thirty patients from a phase II trial evaluating 5-ALA FGS in newly diagnosed GBM were assessed. Tumors were segmented preoperatively to assess morphological features as well as postoperatively to evaluate EOR and residual tumor volume (RTV). Results: Median EOR and RTV were 94.3 % and 0.821 cm3, respectively. Preoperative surface area to volume ratio and RTV were significantly associated with overall survival, even when controlling for the known survival confounders. Conclusions: This study supports claims that 5-ALA FGS is helpful at decreasing tumor burden and prolonging survival in GBM. Moreover, morphological indices are shown to impact both resection and patient survival.

Histone deacetylases regulate a wide variety of cellular functions and have been implicated in redifferentiation of various tumors. Histone deacetylase inhibitors (HDACi) are potential pharmacologic agents to improve outcomes for patients with gliomas. We assessed the therapeutic efficacy of belinostat (PXD-101), an HDACi with blood-brain barrier permeability. Belinostat was first tested in an orthotopic rat glioma model to assess in vivo tumoricidal effect. Our results showed that belinostat was effective in reducing tumor volume in the orthotopic rat glioma model in a dose-dependent manner. We also tested the antidepression activity of belinostat in 2 animal models of depression and found it to be effective. Furthermore, we confirmed that myo-inositol levels improved by belinostat treatment in vitro. In a human pilot study, it was observed that belinostat in combination with chemoradiation may delay initial recurrence of disease. Excitingly, belinostat significantly improved depressive symptoms in patients with glioblastoma compared with control subjects. Finally, spectroscopic magnetic resonance imaging of 2 patient cases from this pilot study are presented to indicate how spectroscopic magnetic resonance imaging can be used to monitor metabolite response and assess treatment effect on whole brain. This study highlights the potential of belinostat to be a synergistic therapeutic agent in the treatment of gliomas.

Glioblastoma has poor prognosis with inevitable local recurrence despite aggressive treatment with surgery and chemoradiation. Radiation therapy (RT) is typically guided by contrast-enhanced T1-weighted magnetic resonance imaging (MRI) for defining the high-dose target and T2-weighted fluid-attenuation inversion recovery MRI for defining the moderate-dose target. There is an urgent need for improved imaging methods to better delineate tumors for focal RT. Spectroscopic MRI (sMRI) is a quantitative imaging technique that enables whole-brain analysis of endogenous metabolite levels, such as the ratio of choline-to-N-acetylaspartate. Previous work has shown that choline-to-N-acetylaspartate ratio accurately identifies tissue with high tumor burden beyond what is seen on standard imaging and can predict regions of metabolic abnormality that are at high risk for recurrence. To facilitate efficient clinical implementation of sMRI for RT planning, we developed the Brain Imaging Collaboration Suite (BrICS; https://brainimaging.emory.edu/brics-demo), a cloud platform that integrates sMRI with standard imaging and enables team members from multiple departments and institutions to work together in delineating RT targets. BrICS is being used in a multisite pilot study to assess feasibility and safety of dose-escalated RT based on metabolic abnormalities in patients with glioblastoma (Clinicaltrials.gov NCT03137888). The workflow of analyzing sMRI volumes and preparing RT plans is described. The pipeline achieved rapid turnaround time by enabling team members to perform their delegated tasks independently in BrICS when their clinical schedules allowed. To date, 18 patients have been treated using targets created in BrICS and no severe toxicities have been observed.

Purpose 18 F-Fluciclovine (anti-1-amino-3-[ 18 F]fluorocyclobutane-1-carboxylic acid) is a novel positron emission tomography (PET)/computed tomography (CT) radiotracer that has demonstrated utility for detection of prostate cancer. Our goal is to report the initial results from a randomized controlled trial of the integration of 18 F-fluciclovine PET-CT into treatment planning for defining prostate bed and lymph node target volumes. Methods and Materials We report our initial findings from a cohort of 41 patients, of the first enrolled on a randomized controlled trial, who were randomized to the 18 F-fluciclovine arm. All patients underwent 18 F-fluciclovine PET-CT for the detection of metabolic abnormalities and high-resolution CT for treatment planning. The 2 datasets were registered first by use of a rigid registration. If soft tissue displacement was observable, the rigid registration was improved with a deformable registration. Each 18 F-fluciclovine abnormality was segmented as a percentage of the maximum standard uptake value (SUV) within a small region of interest around the lesion. The percentage best describing the SUV falloff was integrated in planning by expanding standard target volumes with the PET abnormality. Results In 21 of 55 abnormalities, a deformable registration was needed to map the 18 F-fluciclovine activity into the simulation CT. The most selected percentage was 50% of maximum SUV, although values ranging from 15% to 70% were used for specific patients, illustrating the need for a per-patient selection of a threshold SUV value. The inclusion of 18 F-fluciclovine changed the planning volumes for 46 abnormalities (83%) of the total 55, with 28 (51%) located in the lymph nodes, 11 (20%) in the prostate bed, 10 (18%) in the prostate, and 6 (11%) in the seminal vesicles. Only 9 PET abnormalities were fully contained in the standard target volumes based on the CT-based segmentations and did not necessitate expansion. Conclusions The use of 18 F-fluciclovine in postprostatectomy radiation therapy planning was feasible and led to augmentation of the target volumes in the majority (30 of 41) of the patients studied.

Purpose: Stereotactic radiosurgery (SRS) is increasingly used in the management of patients with resected brain metastases (rBMs). A significant complication of this therapy can be radiation necrosis (RN). Despite radiation therapy dose de-escalation and the delivery of several rather than a single dose fraction, rates of RN after SRS for rBMs remain high. We evaluated the dosimetric parameters associated with radiographic RN for rBMs. Methods and Materials: From 2008 to 2016, 55 rBMs at a single institution that were treated postoperatively with 5-fraction linear accelerator–based SRS (25-35 Gy) with minimum 3 months follow-up were evaluated. For each lesion, variables recorded included radiation therapy dose to normal brain, location and magnitude of hotspots, clinical target volume (CTV), and margin size. Hotspot location was stratified as within the tumor bed alone (CTV) or within the planning target volume (PTV) expansion margin volume (PTV minus CTV). Cumulative incidence with competing risks was used to estimate rates of RN and local recurrence. Optimal cut-points predicting for RN for hotspot magnitude based on location were identified via maximization of the log-rank test statistic. Results: Median age for all patients was 58.5 years. For all targets, the median CTV was 17.53 cm3, the median expansion margin to PTV was 2 mm, and the median max hotspot was 111%. At 1 year, cumulative incidence of radiographic RN was 18.2%. Univariate analysis showed that max hotspots with a hazard ratio of 3.28 (P = .045), hotspots within the PTV expansion margin with relative magnitudes of 105%, 110%, and 111%, and an absolute dose of 33.5 Gy predicted for RN (P = .029, P = .04, P = .038, and P = .0488, respectively), but hotspots within the CTV did not. Conclusions: To our knowledge, this is the first study that investigated dosimetric factors that predict for RN after 5-fraction hypofractionated SRS for rBM. Hotspot location and magnitude appear important for predicting RN risk, thus these parameters should be carefully considered during treatment planning.

A system for automated quality assurance in radiotherapy of a therapist's registration was designed and tested in clinical practice. The approach compliments the clinical software's automated registration in terms of algorithm configuration and performance, and constitutes a practical approach for ensuring safe patient setups. Per our convergence analysis, evolutionary algorithms perform better in finding the global optima of the cost function with discrepancies from a deterministic optimizer seen sporadically.

Purpose: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. Methods: A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. Results: When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. Conclusion: The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.

Introduction: Cardiac radioablation (CR) is a noninvasive treatment option for patients with refractory ventricular tachycardia (VT) during which high doses of radiation, typically 25 Gy, are delivered to myocardial scar. In this study, we investigate motion from cardiac cycle and evaluate the dosimetric impact in a cohort of patients treated with CR. Methods: This retrospective study included eight patients treated at our institution who had respiratory-correlated and ECG-gated 4DCT scans acquired within 2 weeks of CR. Deformable image registration was applied between maximum systole (SYS) and diastole (DIAS) CTs to assess cardiac motion. The average respiratory-correlated CT (AVGresp) was deformably registered to the average cardiac (AVGcardiac), SYS, and DIAS CTs, and contours were propagated using the deformation vector fields (DVFs). Finally, the original treatment plan was recalculated on the deformed AVGresp CT for dosimetric assessment. Results: Motion magnitudes were measured as the mean (SD) value over the DVFs within each structure. Displacement during the cardiac cycle for all chambers was 1.4 (0.9) mm medially/laterally (ML), 1.6 (1.0) mm anteriorly/posteriorly (AP), and 3.0 (2.8) mm superiorly/inferiorly (SI). Displacement for the 12 distinct clinical target volumes (CTVs) was 1.7 (1.5) mm ML, 2.4 (1.1) mm AP, and 2.1 (1.5) SI. Displacements between the AVGresp and AVGcardiac scans were 4.2 (2.0) mm SI and 5.8 (1.4) mm total. Dose recalculations showed that cardiac motion may impact dosimetry, with dose to 95% of the CTV dropping from 27.0 (1.3) Gy on the AVGresp to 20.5 (7.1) Gy as estimated on the AVGcardiac. Conclusions: Cardiac CTV motion in this patient cohort is on average below 3 mm, location-dependent, and when not accounted for in treatment planning may impact target coverage. Further study is needed to assess the impact of cardiac motion on clinical outcomes.