Christian V. Guthier

A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning

This work discusses a novel strategy for inverse planning in low dose rate brachytherapy. It applies the idea of compressed sensing to the problem of inverse treatment planning and a new solver for this formulation is developed. An inverse planning algorithm was developed incorporating brachytherapy dose calculation methods as recommended by AAPM TG-43. For optimization of the functional a new variant of a matching pursuit type solver is presented. The results are compared with current state-of-the-art inverse treatment planning algorithms by means of real prostate cancer patient data. The novel strategy outperforms the best state-of-the-art methods in speed, while achieving comparable quality. It is able to find solutions with comparable values for the objective function and it achieves these results within a few microseconds, being up to 542 times faster than competing state-of-the-art strategies, allowing real-time treatment planning. The sparse solution of inverse brachytherapy planning achieved with methods from compressed sensing is a new paradigm for optimization in medical physics. Through the sparsity of required needles and seeds identified by this method, the cost of intervention may be reduced.

A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy

Purpose In this study, we introduce a novel, fast, inverse treatment planning strategy for interstitial high‐dose‐rate (HDR) brachytherapy with multiple regions of interest solely based on dose‐volume‐histogram‐related dosimetric measures (DMs). Methods We present a new problem formulation of the objective function that approximates the indicator variables of the standard DM optimization problem with a smooth logistic function. This problem is optimized by standard gradient‐based methods. The proposed approach is then compared against state‐of‐the‐art optimization strategies. Results All generated plans fulfilled prescribed DMs for all organs at risk. Compared to clinical practice, a statistically significant improvement Symbol in coverage of target structures was achieved. Simultaneously, DMs representing high‐dose regions were significantly reduced Symbol. The novel optimization strategies run‐time was (0.8 ± 0.3) s and thus outperformed the best competing strategies of the state of the art. In addition, the novel DM‐based approach was associated with a statistically significant Symbol increase in the number of active dwell positions and a decrease in the maximum dwell time. Symbol. No Caption available. Symbol. No Caption available. Symbol. No Caption available. Conclusions The generated plans showed a clinically significant increase in target coverage with fewer hot spots, with an optimization time approximately three orders of magnitude shorter than manual optimization currently used in clinical practice. As optimization is solely based on DMs, intuitive, interactive, real‐time treatment planning, which motivated the adoption of manual optimization in our clinic, is possible.

Real-time inverse high-dose-rate brachytherapy planning with catheter optimization by compressed sensing-inspired optimization strategies.

This paper demonstrates that optimization strategies derived from the field of compressed sensing (CS) improve computational performance in inverse treatment planning (ITP) for high-dose-rate (HDR) brachytherapy. Following an approach applied to low-dose-rate brachytherapy, we developed a reformulation of the ITP problem with the same mathematical structure as standard CS problems. Two greedy methods, derived from hard thresholding and subspace pursuit are presented and their performance is compared to state-of-the-art ITP solvers. Applied to clinical prostate brachytherapy plans speed-up by a factor of 56-350 compared to state-of-the-art methods. Based on a Wilcoxon signed rank-test the novel method statistically significantly decreases the final objective function value (p  <  0.01). The optimization times were below one second and thus planing can be considered as real-time capable. The novel CS inspired strategy enables real-time ITP for HDR brachytherapy including catheter optimization. The generated plans are either clinically equivalent or show a better performance with respect to dosimetric measures.

WE-DE-201-01: BEST IN PHYSICS (THERAPY): A Fast Multi-Target Inverse Treatment Planning Strategy Optimizing Dosimetric Measures for High-Dose-Rate (HDR) Brachytherapy

PURPOSE Inverse treatment planning (ITP) for interstitial HDR brachytherapy of gynecologic cancers seeks to maximize coverage of the clinical target volumes (tumor and vagina) while respecting dose-volume-histogram related dosimetric measures (DMs) for organs at risk (OARs). Commercially available ITP tools do not support DM-based planning because it is computationally too expensive to solve. In this study we present a novel approach that allows fast ITP for gynecologic cancers based on DMs for the first time. METHODS This novel strategy is an optimization model based on a smooth DM-based objective function. The smooth approximation is achieved by utilizing a logistic function for the evaluation of DMs. The resulting nonconvex and constrained optimization problem is then optimized with a BFGS algorithm. The model was evaluated using the implant geometry extracted from 20 patient treatment plans under an IRB-approved retrospective study. For each plan, the final DMs were evaluated and compared to the original clinical plans. The CTVs were the contoured tumor volume and the contoured surface of the vagina. Statistical significance was evaluated with a one-sided paired Wilcoxon signed-rank test. RESULTS As did the clinical plans, all generated plans fulfilled the defined DMs for OARs. The proposed strategy showed a statistically significant improvement (p<0.001) in coverage of the tumor and vagina, with absolute improvements of related DMs of (6.9 +/- 7.9)% and (28.2 +/- 12.0)%, respectively. This was achieved with a statistically significant (p<0.01) decrease of the high-dose-related DM for the tumor. The runtime of the optimization was (2.3 +/- 2.0) seconds. CONCLUSION We demonstrated using clinical data that our novel approach allows rapid DM-based optimization with improved coverage of CTVs with fewer hot spots. Being up to three orders of magnitude faster than the current clinical practice, the method dramatically shortens planning time.

Feasibility of using single photon counting X-ray for lung tumor position estimation based on 4D-CT

PURPOSE In stereotactic body radiation therapy of lung tumors, reliable position estimation of the tumor is necessary in order to minimize normal tissue complication rate. While kV X-ray imaging is frequently used, continuous application during radiotherapy sessions is often not possible due to concerns about the additional dose. Thus, ultra low-dose (ULD) kV X-ray imaging based on a single photon counting detector is suggested. This paper addresses the lower limit of photons to locate the tumor reliably with an accuracy in the range of state-of-the-art methods, i.e. a few millimeters. METHOD 18 patient cases with four dimensional CT (4D-CT), which serves as a-priori information, are included in the study. ULD cone beam projections are simulated from the 4D-CTs including Poisson noise. The projections from the breathing phases which correspond to different tumor positions are compared to the ULD projection by means of Poisson log-likelihood (PML) and correlation coefficient (CC), and template matching under these metrics. RESULTS The results indicate that in full thorax imaging five photons per pixel suffice for a standard deviation in tumor positions of less than half a breathing phase. Around 50 photons per pixel are needed to achieve this accuracy with the field of view restricted to the tumor region. Compared to CC, PML tends to perform better for low photon counts and shifts in patient setup. Template matching only improves the position estimation in high photon counts. The quality of the reconstruction is independent of the projection angle. CONCLUSIONS The accuracy of the proposed ULD single photon counting system is in the range of a few millimeters and therefore comparable to state-of-the-art tumor tracking methods. At the same time, a reduction in photons per pixel by three to four orders of magnitude relative to commercial systems with flatpanel detectors can be achieved. This enables continuous kV image-based position estimation during all fractions since the additional dose to the patient is negligible.

Compressed Sensing-Based LDR Brachytherapy Inverse Treatment Planning with Biological Models

New compressed sensing-based planning algorithms allow for fast computations of optimal planning results in low-dose-rate (LDR) brachytherapy. This enables to integrate complex models in the planning process. In this paper, we develop a new strategy for including a biological model on tumor control probability (TCP) and normal tissue complication probability (NTCP) into the objective function for plan optimization. These models were tested on clinical prostate cancer cases for their effects on the planning results relative to standard physical dose constraints for planning as reference. Interestingly, with weighting treatment risks, we observe plans using biological models assign more dose to the urethra since it is less radiation sensitive than the rectum whereby the latter is spared in order to reduce side effects. At the same time, the overall TCP is comparable. We conclude that the standard plan quality evaluation based on physical dose alone does not easily allow correctly assessing treatment risks. Hence, biological models for LDR brachytherapy treatment planning are a promising approach for an optimal management of treatment outcomes of brachytherapy.

A fast inverse treatment planning strategy facilitating optimized catheter selection in image-guided high-dose-rate interstitial gynecologic brachytherapy

Purpose: Interstitial high‐dose rate (HDR) brachytherapy is an important therapeutic strategy for the treatment of locally advanced gynecologic (GYN) cancers. The outcome of this therapy is determined by the quality of dose distribution achieved. This paper focuses on a novel yet simple heuristic for catheter selection for GYN HDR brachytherapy and their comparison against state of the art optimization strategies. The proposed technique is intended to act as a decision‐supporting tool to select a favorable needle configuration. Materials: The presented heuristic for catheter optimization is based on a shrinkage‐type algorithm (SACO). It is compared against state of the art planning in a retrospective study of 20 patients who previously received image‐guided interstitial HDR brachytherapy using a Syed Neblett template. From those plans, template orientation and position are estimated via a rigid registration of the template with the actual catheter trajectories. All potential straight trajectories intersecting the contoured clinical target volume (CTV) are considered for catheter optimization. Retrospectively generated plans and clinical plans are compared with respect to dosimetric performance and optimization time. Results: All plans were generated with one single run of the optimizer lasting 0.6–97.4 s. Compared to manual optimization, SACO yields a statistically significant (P ≤ 0.05) improved target coverage while at the same time fulfilling all dosimetric constraints for organs at risk (OARs). Comparing inverse planning strategies, dosimetric evaluation for SACO and “hybrid inverse planning and optimization” (HIPO), as gold standard, shows no statistically significant difference (P > 0.05). However, SACO provides the potential to reduce the number of used catheters without compromising plan quality. Conclusion: The proposed heuristic for needle selection provides fast catheter selection with optimization times suited for intraoperative treatment planning. Compared to manual optimization, the proposed methodology results in fewer catheters without a clinically significant loss in plan quality. The proposed approach can be used as a decision support tool that guides the user to find the ideal number and configuration of catheters.

Compressed sensing denoising for segmentation of localization microscopy data

Localization microscopy (LM) allows to acquire pointillistic superresolution images of biological structures on the nanoscale. However, current structure reconstruction and segmentation approaches suffer from either exclusion of small structures or strong dependence on a-priori knowledge. We propose reconstruction methods based on compressed sensing (CS) denoising in combination with the isodata threshold for segmentation. The methods are verified on artificial test data. For the denoising, a Haar dictionary and a KSVD dictionary learning on artificial data are used. Both methods perform significantly better than the reference algorithm, a linear density filter, in terms of root-mean-square deviation from the ground truth. Furthermore, exemplary results on real LM data of irradiated cell nuclei with Heterochromatin labeling make small structures visible that are suppressed by the reference method. CS denoising demonstrates promising results for reconstruction of LM data.