Qihui Lyu

Objective. Current Gamma Knife (GK) treatment planning algorithms typically determine isocenter locations using geometric heuristics rather than dose-driven optimization. Isocenters are either placed automatically (using center-of-mass, skeleton-based, or curvature-based algorithms) or manually by planners, followed by a separate sector duration optimization (SDO) with fixed isocenter locations. Since the isocenter selection is separate from the dosimetry optimization, the SDO step is often constrained by a suboptimal isocenter configuration, limiting achievable plan quality and leading to unnecessarily long beam-on time (BOT). Approach. We introduce an integrated isocenter and SDO framework (ISDO) that replaces heuristic isocenter placement with a dose-driven joint ISDO. The framework includes a group-sparsity regularization for isocenter selection and an explicit BOT regularization for delivery efficiency. The selected isocenters are further refined through an SDO module, followed by a post-processing stage that converts the optimized sector–collimator patterns into deliverable shot configuration. The framework was evaluated using 12 GK cases spanning a variety of clinical indications, including arteriovenous malformation, vestibular schwannoma, meningioma, and pituitary adenoma. Main Results. With similar or better plan quality, ISDO plans used fewer or equal shots and substantially reduced the BOT. Across all cases, ISDO reduced the number of shots by 15.06% and BOT by 20.77%. With similar target coverage, the selectivity and Paddick conformity index increased by 5.77% and 5.82%, and R ₅₀ and D _{2 cm} decreased by 6.45% and 5.23%. The maximum and mean doses of organs-at-risk were also reduced by 16.0% and 28.8% respectively. Significance. By incorporating isocenter selection directly into the BOT-aware inverse-planning framework and driving the isocenter selection through a group sparsity term, the proposed ISDO method achieved more efficient and conformal GK plans than current clinical GK treatment planning approaches.

Background: High Dose Rate (HDR) brachytherapy delivers concentrated radiation to the tumor by placing a high‐dose‐rate radioactive source directly inside the target. However, the sharp dose falloff also increases susceptibility to errors and uncertainties. Purpose: To enhance source localization accuracy relative to room coordinates and the implant, we optimize the design of a flat‐panel‐based source localization system for HDR brachytherapy. Methods: Gamma rays from the Ir‐192 Flexisource were simulated with Monte Carlo for image reconstruction. A 50 × 50 cm ² flat‐panel detector was placed 15 cm beneath the couch, in which four tungsten ball bearings (BB) are attached. The source location relative to the couch was determined via triangulation. We assessed source localization accuracy based on couch‐BB positions, sizes, BB‐to‐source distance (BSD), and BB‐to‐detector distance (BDD). Since the implant can shift independently, determining the source location relative to the implant, rather than room coordinates, is critical for target dosimetry. To achieve this, a dummy wire containing six tungsten BBs, aligned linearly with increasing separations, was inserted through one of the posterior implanted catheters. The source position relative to the implant was then determined using a rigid transformation that best matched the wire‐BB projections. Results: Relative to the room coordinates using the couch‐BBs, the proposed system achieves 0.78 mm accuracy. Localization error increases with larger BSD and smaller BDD, ranging from 0.3–2.5 mm (BSD: 10–30 cm) and 0.3–1.2 mm (BDD: 30–10 cm). Introducing implant motion (15 mm translation, 10 pitch) resulted in 15.8 mm source localization error in room coordinates, while the proposed wire‐BB registration method reduces the error to 1.26 mm relative to the implant. Conclusion: The study demonstrates the feasibility of using a flat‐panel detector, couch‐attached markers, and a dummy wire for robust localization of the HDR brachytherapy source relative to both the room coordinates and the implant.

While 4 π radiotherapy has shown dosimetric advantages over coplanar delivery, prior studies have assumed a fixed source-to-isocenter distance (SID), as varying SID is cumbersome to implement on conventional C-arm linacs. Radiotherapy platforms based on articulated robotic arms eliminate this constraint, enabling non-coplanar, non-isocentric delivery with variable SIDs without moving the patient. Larger SIDs offer increased per-field tumor coverage with a larger field-of-view (FOV), while shorter SIDs improve modulation resolution. This study combines multiple SIDs to simultaneously achieve high target coverage and modulation resolution in robotic radiotherapy. A Multi-SID optimization framework was developed for simultaneous beam orientation optimization, isocenter selection, SID selection, and fluence map optimization. The planning cost includes a least-squares dose fidelity objective, a total variation term for fluence smoothness, and a group sparsity term for beam orientation, SID, and isocenter selection. The collision-free candidate beam pool includes over 1000 beam orientations, 10 SID levels (50–95 cm), and 5–9 isocenters. Multi-SID 4 π intensity-modulated radiation therapy (IMRT) plans were compared against fixed-SID (SID-50 cm and SID-100 cm) 4 π IMRT plans on 10 head-and-neck (H&N) cancer cases. Multi-SID consistently achieved superior plan quality across all 10 patients using varying beams (10–30). For 15-beam plans, the planning target volume homogeneity (defined as D95/D5) is (0.898, 0.893, 0.889), dose conformity (using the Paddick Conformity Index) is (0.867, 0.853, 0.837), the mean Organs-at-Risk (OAR) dose is (12.67, 15.05, 14.12), and the R50 is (2.429, 2.575, 2.485) for Multi-SID, SID-100, and SID-50 respectively. Wilcoxon signed-rank testing showed that the improvements in homogeneity, conformity, and OAR sparing with Multi-SID were statistically significant. Our Multi-SID method leverages both high target coverage and modulation resolution to achieve better dosimetry and/or more efficient treatment plans than fixed SID methods.

Background. Preclinical small-animal studies are essential for understanding radiation-induced biological responses and toxicities, ultimately facilitating translation to clinical interventions. However, the promise is hampered by poor dose conformity achieved in small-animal experiments. The gap has been partially closed with recent advances in image guidance and radiation intensity modulation. The vast differences in the sizes between the small animals and humans, however, demand steeper dose gradient to mimic clinical targeting specificity. Objective. Here, we explore the non-coplanar solution space enabled by the Small Animal Radiation Research Platform (SARRP) to enhance dose delivery specificity. Approach. This study integrates a 4π non-coplanar beam orientation optimization framework with 4π rectangular apertures-based beam orientation optimization (4π-RABOO) for automated small-animal intensity-modulated radiotherapy (IMRT) planning. Methods. 4π-RABOO objective function included an L ₂-norm term for dose fidelity, an L ₁-norm term for aperture sparsity, and an L _2,1/2 group sparsity term for beam selection. Solving the large-scale computational problem was accelerated by replacing the Kronecker product with smaller matrix multiplications and by applying the Fast Iterative Shrinkage Thresholding Algorithm. Three preclinical sites (brain, liver tumor, spine) involving five mice were evaluated against two reference coplanar plans: 5-field rectangular aperture plans and fixed-size aperture plans. Plan evaluation metrics included D2, D98, homogeneity index, R ₅₀ for planning target volumes, and mean/max doses to organs at risk (OARs). Main Results. 4π-RABOO improved plan quality by reducing D2 by up to 17.4%, increasing D98 by up to 17.1%, and lowering R ₅₀ by over 30% compared to baseline plans. In spine cases, mean doses to critical OARs (bowel, liver, spleen) were reduced by up to 64.5%, 51.6%, and 93.1%, respectively. The enhanced dosimetric performance came at the cost of increased plan complexity, with 2–3X more apertures per case. Significance. Integrating beam orientation and rectangular aperture optimization into small-animal IMRT on the SARRP generates dosimetrically superior plans, significantly improving target dose homogeneity and adjacent tissue sparing.

Purpose. In radiotherapy, dose distribution conformity and compactness are critical to patient outcomes. Advanced techniques like 4 π radiotherapy leverage non-coplanar beams for superior dosimetry by exploring additional degrees of freedom. However, 4 π planning is computationally intensive due to large dose-loading matrices for candidate beams. This work presents an ultra-high performance parallel (UHPP) framework to accelerate high-dimensional treatment planning. Methods. For dose calculation, we developed: (1) a two-step total energy released per unit mass (TERMA) computation module calculating the TERMA array once per beam, enabling reuse across convolution directions; (2) a synchronized dose calculation module based on collapsed-cone convolution superposition (CCCS), arranging rays in dedicated sequences to preserve thread efficiency and minimize memory access; (3) a scattering-based coordinate transformation mapping dose from beamlet to patient Cartesian coordinates, eliminating aliasing without atomic operations. The framework includes CCCS exponential kernel calculation for varying LINAC spectra. For beam orientation optimization, we employed fast iterative shrinkage-thresholding algorithm with group sparsity regularization, accelerated using cuSPARSE library on GPUs. We benchmarked against Monte Carlo (MC) simulations for dose accuracy and compared computational performance to state-of-the-art (SOTA) methods. Plan quality was evaluated across four approaches: UHPP, SOTA, clinical VMAT plans, and MC calculations based on UHPP plans. Results. Compared to MC simulations, UHPP achieved minimum 98% gamma passing rates under 1.5%/1.5 mm criterion for water and slab phantoms, and average 97.35% and 92.18% under 3%/3 mm criterion for pancreas and head-and-neck patients, respectively. UHPP delivered 8.86× and 6.99× speedups in dose calculation and plan optimization while maintaining comparable or superior plan quality. Both UHPP and SOTA consistently produced 4 π plans outperforming clinical VMAT plans in organ-at-risk sparing and target coverage. Conclusion. The UHPP framework delivers high dose accuracy and substantial computational speedup without sacrificing 4 π planning’s dosimetric advantages, supporting practical adoption of advanced 4 π radiotherapy in clinical workflows.

Measuring the radiation dose reaching a patient’s body is difficult. Here we report a technique for the tomographic reconstruction of the location of photon pairs originating from the annihilation of positron–electron pairs produced by high-energy X-rays travelling through tissue. We used Monte Carlo simulations on pre-recorded data from tissue-mimicking phantoms and from a patient with a brain tumour to show the feasibility of this imaging modality, which we named ‘pair-production tomography’, for the monitoring of radiotherapy dosing. We simulated three image-reconstruction methods, one applicable to a pencil X-ray beam scanning through a region of interest, and two applicable to the excitation of tissue volumes via broad beams (with temporal resolution sufficient to identify coincident photon pairs via filtered back projection, or with higher temporal resolution sufficient for the estimation of a photon’s time-of-flight). In addition to the monitoring of radiotherapy dosing, we show that image contrast resulting from pair-production tomography is highly proportional to the material’s atomic number. The technique may thus also allow for element mapping and for soft-tissue differentiation.

Objective. Intensity-modulated radiotherapy (IMRT) is widely used in clinical radiotherapy, treating varying malignancies with conformal doses. As the test field for clinical translation, preclinical small animal experiments need to mimic the human radiotherapy condition, including IMRT. However, small animal IMRT is a systematic challenge due to the lack of corresponding hardware and software for miniaturized targets. Approach. The sparse orthogonal collimators (SOC) based on the direct rectangular aperture optimization (RAO) substantially simplified the hardware for miniaturization. This study investigates and evaluates a significantly improved RAO algorithm for complex mouse irradiation using SOC. Because the Kronecker product representation of the rectangular aperture is the main limitation of the computational performance, we reformulated matrix multiplication in the data fidelity term using multiplication with small matrices instead of the Kronecker product of the dose loading matrices. Solving the optimization problem was further accelerated using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Main results. Four mouse cases, including a liver, a brain tumor, a concave U-target, and a complex total marrow irradiation (TMI) case, were included in this study with manually delineated targets and OARs. Seven coplanar-field SOC IMRT (sIMRT) plans were compared with idealistic fluence map based IMRT (iIMRT) plans. For the first three cases with simpler and smaller targets, the differences between sIMRT plans and iIMRT plans in the planning target volumes (PTV) statistics are within 1%. For the TMI case, the sIMRT plans are superior in reducing hot spots (also termed D _max ) of PTV, kidneys, lungs, heart, and bowel by 20.5%, 31.5%, 24.67%, 20.13%, and 17.78%, respectively. On average, in four cases in this study, the sIMRT plan conformity is comparable to that of the iIMRT’s with lightly increased R50 and Integral Dose by 2.23% and 2.78%. Significance. The significantly improved sIMRT optimization method allows fast plan creation in under 1 min for smaller targets and makes complex TMI planning feasible while achieving comparable dosimetry to idealistic IMRT with fluence map optimization.

The Plug-and-Play (PnP) framework was recently introduced for low-dose CT reconstruction to leverage the interpretability and the flexibility of model-based methods to incorporate various plugins, such as trained deep learning (DL) neural networks. However, the benefits of PnP vs. state-of-the-art DL methods have not been clearly demonstrated. In this work, we proposed an improved PnP framework to address the previous limitations and develop clinical-relevant segmentation metrics for quantitative result assessment. Compared with the DL alone methods, our proposed PnP framework was slightly inferior in MSE and PSNR. However, the power spectrum of the resulting images better matched that of full-dose images than that of DL denoised images. The resulting images supported higher accuracy in airway segmentation than DL denoised images for all the ten patients in the test set, more substantially on the airways with a cross-section smaller than 0.61cm ² , and outperformed the DL denoised images for 45 out of 50 lung lobes in lobar segmentation. Our PnP method proved to be significantly better at preserving the image texture, which translated to task-specific benefits in automated structure segmentation and detection.

Ultra-high dose rate in radiotherapy (FLASH) has been shown to increase the therapeutic index with markedly reduced normal tissue toxicity and the same or better tumor cell killing. The challenge to achieve FLASH using x-rays, besides developing a high output linac, is to intensity-modulate the high-dose-rate x-rays so that the biological gain is not offset by the lack of physical dose conformity. In this study, we develop the ROtational direct Aperture optimization with a Decoupled ring-collimator (ROAD) to achieve simultaneous ultrafast delivery and complex dose modulation. The ROAD design includes a fast-rotating slip-ring linac and a decoupled collimator-ring with 75 pre-shaped multi-leaf-collimator (MLC) modules. The ring-source rotates at 1 rotation per second (rps) clockwise while the ring-collimator is either static or rotating at 1 rps counterclockwise, achieving 75 (ROAD-75) or 150 (ROAD-150) equal-angular beams for one full arc. The Direct Aperture Optimization (DAO) for ROAD was formulated to include a least-square dose fidelity, an anisotropic total variation term, and a single segment term. The FLASH dose (FD) and FLASH biological equivalent dose (FBED) were computed voxelwise, with the latter using a spatiotemporal model accounting for radiolytic oxygen depletion. ROAD was compared with clinical volumetric modulated arc therapy (VMAT) on a brain, a lung, a prostate, and a head and neck cancer patient. The mean dose rate of ROAD-75 and ROAD-150 are 76.2 Gy s ⁻¹ and 112 Gy s ⁻¹ respectively to deliver 25 Gy single-fraction dose in 1 s. With improved PTV homogeneity, ROAD-150 reduced (max, mean) OAR physical dose by (4.8 Gy, 6.3 Gy). The average R50 and integral dose of (VMAT, ROAD-75, ROAD-150) are (4.8, 3.2, 3.2) and (89, 57, 56) Gy×Liter, respectively. The FD and FBED showed model dependent FLASH effects. The novel ROAD design achieves ultrafast dose delivery and improves physical dosimetry compared with clinical VMAT, providing a potentially viable engineering solution for x-ray FLASH radiotherapy.

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.