Animal studies implicate meningeal lymphatic dysfunction in the pathogenesis of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease (PD). However, there is no direct evidence in humans to support this role1-5. In this study, we used dynamic contrast-enhanced magnetic resonance imaging to assess meningeal lymphatic flow in cognitively normal controls and patients with idiopathic PD (iPD) or atypical Parkinsonian (AP) disorders. We found that patients with iPD exhibited significantly reduced flow through the meningeal lymphatic vessels (mLVs) along the superior sagittal sinus and sigmoid sinus, as well as a notable delay in deep cervical lymph node perfusion, compared to patients with AP. There was no significant difference in the size (cross-sectional area) of mLVs in patients with iPD or AP versus controls. In mice injected with α-synuclein (α-syn) preformed fibrils, we showed that the emergence of α-syn pathology was followed by delayed meningeal lymphatic drainage, loss of tight junctions among meningeal lymphatic endothelial cells and increased inflammation of the meninges. Finally, blocking flow through the mLVs in mice treated with α-syn preformed fibrils increased α-syn pathology and exacerbated motor and memory deficits. These results suggest that meningeal lymphatic drainage dysfunction aggravates α-syn pathology and contributes to the progression of PD.

Impaired meningeal lymphatic drainage in patients with idiopathic Parkinson's disease.

/pdf/unsupervised-inter-frame-motion-correction-for-whole-body-50xag9ci.pdf

Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network

Abstract Purpose To retrospectively analyze and estimate the dosimetric benefit of online and offline motion mitigation strategies for prostate IMRT. Methods Intrafractional motion data of 21 prostate patients receiving intensity‐modulated radiotherapy was acquired with an electromagnetic tracking system. Target trajectories of 734 fractions were analyzed per delivered multileaf‐collimator segment in five motion metrics: three‐dimensional displacement, distance from beam axis (DistToBeam), and three orthogonal components. Time‐resolved dose calculations have been performed by shifting the target according to the sampled motion for the following scenarios: without adaptation, online‐repositioning with a minimum threshold of 3 mm, and an offline approach using a modified field order applying horizontal before vertical beams. Change of D95 (targets) or V65 (organs at risk) relative to the static case, that is, ΔD95 or ΔV65, was extracted per fraction in percent. Correlation coefficients (CC) between the motion metrics and the dose metrics were extracted. Mean of patient‐wise CC was used to evaluate the correlation of motion metric and dosimetric changes. Mean and standard deviation of the patient‐wise correlation slopes (in %/mm) were extracted. Results For ΔD95 of the prostate, mean DistToBeam per fraction showed the highest correlation for all scenarios with a relative change of −0.6 ± 0.7%/mm without adaptation and −0.4 ± 0.5%/mm for the repositioning and field order strategies. For ΔV65 of the bladder and the rectum, superior–inferior and posterior–anterior motion components per fraction showed the highest correlation, respectively. The slope of bladder (rectum) was 14.6 ± 5.8 (15.1 ± 6.9) %/mm without adaptation, 14.0 ± 4.9 (14.5 ± 7.4) %/mm for repositioning with 3 mm, and 10.6 ± 2.5 (8.1 ± 4.6) %/mm for the field order approach. Conclusions The correlation slope is a valuable concept to estimate dosimetric deviations from static plan quality directly based on the observed motion. For the prostate, both mitigation strategies showed comparable benefit. For organs at risk, the field order approach showed less sensitive response regarding motion and reduced interpatient variation.

Correlation between intrafractional motion and dosimetric changes for prostate IMRT

During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real-time image-guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)-based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker-based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker-based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.

A review of artificial intelligence applications for motion tracking in radiotherapy.

Abstract Accurate detection of patient shift is essential during radiation therapy such that optimal dose is delivered to the tumor while minimizing radiation to surrounding normal tissues. The shift detectability of a newly developed optical surface and thermal tracking system, which was known as ExacTrac Dynamic (EXTD), was evaluated by comparing its performance with the image guidance under cone‐beam computed tomography (CBCT). Anthropomorphic cranial and pelvis phantoms with internal bone‐like structures and external heat pad were utilized to study the shift detection discrepancy between EXTD system and CBCT. Random displacements within the range of ± 2 cm for translations and ± 2 degrees for rotations were intentionally applied to the phantom. Positional shifts detected by optical surface and thermal tracking (EXTD_Thml), stereoscopic X‐ray (EXTD_Xray), and CBCT were compared in 6 degrees of freedom. The translational difference between EXTD_Thml and CBCT was 0.57 ± 0.41 mm and 0.66 ± 0.40 mm for cranial and pelvis phantom, respectively, while it was 0.60 ± 0.43 mm and 0.76 ± 0.49 mm between EXTD_Xray and CBCT, respectively. For rotational movement, the difference between EXTD_Thml and CBCT was 0.19 ± 0.16° and 0.19 ± 0.22° for cranial and pelvis phantom, respectively, while it was 0.13 ± 0.18° and 0.65 ± 0.46° between EXTD_Xray and CBCT, respectively. This study demonstrated that the EXTD system with thermal mapping ability could offer comparable accuracy for shift detection with CBCT on both cranial and pelvis phantoms.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/acm2.13567

Shift detection discrepancy between ExacTrac Dynamic system and cone‐beam computed tomography

Surface-guided radiation therapy can be used to continuously monitor a patient’s surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context. Seven groups of interrelated external/internal respiratory liver motion samples lasting from 5 to 6 min collected simultaneously were used as a dataset, Dv. Long short-term memory (LSTM) and support vector regression (SVR) networks were then used to establish external respiratory signal prediction models (LSTMpred/SVRpred) and external/internal respiratory motion correlation models (LSTMcorr/SVRcorr). These external prediction and external/internal correlation models were then combined into an integrated model. Finally, the LSTMcorr model was used to perform five groups of model updating experiments to confirm the necessity of continuously updating the external/internal correlation model. The root-mean-square error (RMSE), mean absolute error (MAE), and maximum absolute error (MAX_AE) were used to evaluate the performance of each model. The models established using the LSTM neural network performed better than those established using the SVR network in the tasks of predicting external respiratory signals for latency-compensation (RMSE < 0.5 mm at a latency of 450 ms) and predicting internal liver motion using external signals (RMSE < 0.6 mm). The prediction errors of the integrated model (RMSE ≤ 1.0 mm) were slightly higher than those of the external prediction and external/internal correlation models. The RMSE/MAE of the fifth model update was approximately ten times smaller than that of the first model update. The LSTM networks outperform SVR networks at predicting external respiratory signals and internal liver motion because of LSTM’s strong ability to deal with time-dependencies. The LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450 ms. It is necessary to update the external/internal correlation model continuously.

/pdf/real-time-liver-tracking-algorithm-based-on-lstm-and-svr-1i91kye540.pdf

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy.

PURPOSE Applying statistical process control (SPC) to intensity-modulated radiotherapy (IMRT)/volumetric modulated arc therapy (VMAT) patient-specific quality assurance (PSQA) program was recommended by the American Association of Physics in Medicine Task Group 218 report, but a comprehensive analysis of PSQA processes with non-normal distributions is lacking. This study investigates SPC and process capability analysis (PCA) methods for non-normal IMRT/VMAT PSQA processes. METHODS 1119 VMAT PSQAs were performed on three beam-matched linear accelerators (linacs), using gamma analysis. The Anderson-Darling statistic was used to test normality. The control charts for each PSQA process were obtained using three non-normal-based methods and compared with the conventional Shewhart method. The ability of each PSQA process to produce an output within the specification limit was measured using the Cpk index; in this study, the Cpk index was calculated using two transformation methods and compared with that calculated using the conventional method. The performances of the three linacs were assessed using SPC and PCA methods. RESULTS All three PSQA processes were non-normal (P < 0.005). Compared to the non-normal-based SPC and PCA methods, the false alarm rates of the conventional method for linac1, linac2, and linac3 were 0.83%, 3.77%, and 4.95% respectively; the minimum overestimated Cpk values were 0.59, 0.87, and 1.49, respectively. The process capabilities of the three beam-matched linacs were at different levels. CONCLUSION For non-normal VMAT PSQA processes, the conventional SPC and PCA methods increase the false alarm rates and overestimate process capabilities. Instead, non-normal-based SPC and PCA methods are more reliable and accurate in non-normal PSQA processes. Statistical process control and PCA are useful tools for assessing the performance of beam-matched linacs.

Statistical process control and process capability analysis for non-normal volumetric modulated arc therapy patient-specific quality assurance processes

Surface-guided radiation therapy (SGRT) employs a non-invasive real-time optical surface imaging (OSI) technique for patient surface motion monitoring during radiotherapy. The main purpose of this study is to verify the real-time tracking accuracy of SGRT for respiratory motion and provide a fitting method to detect the time delay of gating. A respiratory motion phantom was utilized to simulate respiratory motion using 17 cosine breathing pattern curves with various periods and amplitudes. The motion tracking of the phantom was performed by the Catalyst™ system. The tracking accuracy of the system (with period and amplitude variations) was evaluated by analyzing the adjusted coefficient of determination (A_R2) and root mean square error (RMSE). Furthermore, 13 actual respiratory curves, which were categorized into regular and irregular patterns, were selected and then simulated by the phantom. The Fourier transform was applied to the respiratory curves, and tracking accuracy was compared through the quantitative analyses of curve similarity using the Pearson correlation coefficient (PCC). In addition, the time delay of amplitude-based respiratory-gating radiotherapy based on the OSI system with various beam hold times was tested using film dosimetry for the Elekta Versa-HD and Varian Edge linacs. A dose convolution-fitting method was provided to accurately measure the beam-on and beam-off time delays. A_R2 and RMSE for the cosine curves were 0.9990–0.9996 and 0.110–0.241 mm for periods ranging from 1 s to 10 s and 0.9990–0.9994 and 0.059–0.175 mm for amplitudes ranging from 3 mm to 15 mm. The PCC for the actual respiratory curves ranged from 0.9955 to 0.9994, which was not significantly affected by breathing patterns. For gating radiotherapy, the average beam-on and beam-off time delays were 1664 ± 72 and 25 ± 30 ms for Versa-HD and 303 ± 45 and 34 ± 25 ms for Edge, respectively. The time delay was relatively stable as the beam hold time increased. The OSI technique provides high accuracy for respiratory motion tracking. The proposed dose convolution-fitting method can accurately measure the time delay of respiratory-gating radiotherapy. When the OSI technique is used for respiratory-gating radiotherapy, the time delay for the beam-on is considerably longer than the beam-off.

/pdf/accuracy-of-real-time-respiratory-motion-tracking-and-time-4n10o2d75s.pdf

Accuracy of real-time respiratory motion tracking and time delay of gating radiotherapy based on optical surface imaging technique.

Dosimetric characteristics of a 2D silicon diode array for stereotactic radiotherapy end-to-end patient-specific QA

PURPOSE Establishing the tolerance limits of patient-specific quality assurance (PSQA) processes based on the gamma passing rate (GPR) by using normal statistical process control (SPC) methods involves certain problems. The aim of this study was threefold: (a) to show that the heuristic SPC method can replace the quantile method for establishing tolerance limits in PSQA processes and is more robust, (b) to introduce an iterative procedure of "Identify-Eliminate-Recalculate" for establishing the tolerance limits in PSQA processes with unknown states based on retrospective GPRs, and (c) to recommend a workflow to define tolerance limits based on actual clinical retrospective GPRs. MATERIALS AND METHODS A total of 1671 volumetric-modulated arc therapy (VMAT) pretreatment plans were measured on four linear accelerators (linacs) and analyzed by treatment sites using the GPRs under the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. Normality testing was performed using the Anderson-Darling (AD) statistic and the optimal distributions of GPRs were determined using the Fitter Python package. The iterative "Identify-Eliminate-Recalculate" procedure was used to identify the PSQA outliers. The tolerance limits of the initial PSQAs, remaining PSQAs after elimination, and in-control PSQAs after correction were calculated using the conventional Shewhart method, two transformation methods, three heuristic methods, and two quantile methods. The tolerance limits of PSQA processes with different states for the respective methods, linacs, and treatment sites were comprehensively compared and analyzed. RESULTS It was found that 75% of the initial PSQA processes and 63% of the in-control processes were non-normal (AD test, p < 0.05). The optimal distributions of GPRs for the initial and in-control PSQAs varied with different linacs and treatment sites. In the implementation of the "Identify-Eliminate-Recalculate" procedure, the quantile methods could not identify the out-of-control PSQAs effectively due to the influence of outliers. The tolerance limits of the in-control PSQAs, calculated using the quantile of optimal fitting distributions, represented the ground truth. The tolerance limits of the in-control PSQAs and remaining PSQAs after elimination calculated using the heuristic methods were considerably close to the ground truth (the maximum average absolute deviations were 0.50 and 1.03%, respectively). Some transformation failures occurred under both transformation methods. For the in-control PSQAs at 3%/2 mm gamma criteria, the maximum differences in the tolerance limits for four linacs and different treatment sites were 3.10 and 5.02%, respectively. CONCLUSIONS The GPR distributions of PSQA processes vary with different linacs and treatment sites but most are skewed. In applying SPC methodologies to PSQA processes, heuristic methods are robust. For in-control PSQA processes, the tolerance limits calculated by heuristic methods are in good agreement with the ground truth. For unknown PSQA processes, the tolerance limits calculated by the heuristic methods after the iterative "Identify-Eliminate-Recalculate" procedure are closest to the ground truth. Setting linac- and treatment site-specific tolerance limits for PSQA processes is necessary for clinical applications.

A robust approach to establish tolerance limits for the gamma passing rate-based patient-specific quality assurance using the heuristic control charts.

Long Bai

Papers

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy.

Statistical process control and process capability analysis for non-normal volumetric modulated arc therapy patient-specific quality assurance processes

Accuracy of real-time respiratory motion tracking and time delay of gating radiotherapy based on optical surface imaging technique.

Dosimetric characteristics of a 2D silicon diode array for stereotactic radiotherapy end-to-end patient-specific QA

A robust approach to establish tolerance limits for the gamma passing rate-based patient-specific quality assurance using the heuristic control charts.