Search

Relative biological effectiveness (RBE) is an important quantity in planning particle beam cancer therapy. In general, the RBE describes the biological effectiveness of a given primary beam with respect to a reference photon irradiation. RBE varies not only for different primary beams but also with depth in the target for a given beam modality. It is not a quantity that easily lends itself to measurements or computation as it depends on many biological and physical quantities. Numerous experiments in vitro using various cell lines and irradiation modalities have shown that a general relationship between RBE and the physical quantity Linear Energy Transfer (LET) exists. Several groups have proposed including LET in the radiation therapy treatment planning instead of the more complicated and elusive RBE. It has been shown that LET is an important quantity to consider in treating radio-resistant tumors. The concept of LET painting has been proposed with the goal of improving tumor control probability (TCP) for hypoxic tumors by focusing high LET radiation on the hypoxic region of the tumor while restricting the surrounding normal tissue to low LET radiation. In order to properly incorporate LET in clinical treatment, it is important to be able to experimentally measure and verify LET distribution. We propose a novel method for measuring LET using a dual chamber methodology exploiting the difference in the observed recombination between air filled ionization chambers (IC) and liquid filled ionization chambers (LIC). The resulting difference in the measured signals will be used to directly extract the relative LET of an actual treatment beam in real time. This paper describes our initial studies of this method, presents preliminary results, and discusses further improvements toward a practical real-time LET measuring device.

Gait analysis is an important process to gauge human motion. Recently, longitudinal gait analysis received much attention from the medical and healthcare domains. The challenge in studies over extended time periods is the battery life. Due to the continuous sensing and computing, wearable gait devices cannot fulfill a full-day work schedule. In this paper, we present an energy-efficient adaptive sensing framework to address this problem. Through presampling for content understanding, a selective sensing and sparsity-based signal reconstruction method is proposed. In particular, we develop and implement the new sensing scheme in a smart insole system to reduce the number of samples, while still preserving the information integrity of gait parameters. Experimental results show the effectiveness of our method in data point reduction. Our proposed method improves the battery life to 10.47 h, while normalized mean square error is within 10%.

Purpose: The dosimetric advantage of particle therapy comes with a much higher infrastructure investment and operation costs. Increasing patient throughput is a key factor to manage operation costs. We investigate the impact of variable beam spot sizes on treatment time and discuss the tradeoffs involved. Methods: The following realistic assumptions were used. (1) The beam traveling speed is independent of the beam spot size. (2) The beam spot is a 2D Gaussian. Changing the beam spot size implies varying the standard deviation. (3) The maximum beam intensity is a constant independent of the beam spot size. Increasing the beam spot reduces the fluence. (4) Varying the beam spot size incurs in a reset time penalty.A 2D tumor was used in the study. Dose calculations were based on pencil beam kernels from GEANT4.The total treatment time is divided into the beam travel time, the beam‐on time, andthe time for changing the spot size. Results: We found that: (1) Changing the beam spot size has no impact on the beam‐on time, because the maximum beam intensity is independentof the beam spot and increasing the beam spot only reduces the fluence. (2) Larger beam spot size shortens the total travel time inversely proportional to the radius of the beam spot. (3) Plans with different beam spot sizes have similar dosimetric qualities. (4) If higher beam intensity could be used for larger beam spot size, savings in beam‐on time would be inversely proportional to the intensity available. Conclusions: We have studied the interplay among beam intensity, travel time, and beam size reset time for a scanning beam with variable beam spot size. Our initial studies show necessary conditions for and limitations on savings in total treatment times. Further studies are being carried out to find additional time saving sources. Supported in part by NSF CBET‐0853157.

Purpose: Charged particle beams are of great interest, because they can achieve highly conformal radiationdose distributions. Despite this, some scattered radiation is inevitably present outside of the target volumes, and is of concern because of risks such as radiogenic cancer. Accurately calculating the secondary dose in regions far from the target volume is very difficult due to extremely low particle fluence and the effect of heterogeneities on particle ranges, making calculations possible only with CPU‐week long Monte Carlo runs. By using a modified track repeating method, we demonstrate fast and accurate estimation of secondary dose appropriate for clinical use. Methods: Primary and secondary particle track databases (including protons, electrons, photons,neutrons, and positrons) were generated with the Geant4 Monte Carlo toolkit. Several new strategies were developed or employed to improve the performance of non‐ primary particle propagation, including: (1) processing the databases such that only primary tracks producing deep penetrating photons or neutrons were kept and particles falling below transport thresholds were discarded, (2) a search algorithm that can locate a sub‐track for a given energy in constant time, (3) multiplying photon and neutron tracks during propagation and scoring using particle ‘splitting1. Results: Performance and accuracy were benchmarked against full Monte Carlo calculations (Geant4 and FLUKA). Filtering out tracks that did not produce deep penetrating photons or neutrons did not affect the accuracy of the secondary dose calculation. Preliminary performance analysis indicated 60– 100X speed up over Fluka and 700–1000X speed up over Geant4 with well maintained accuracy. Conclusions: Estimation of secondary dose from particle therapy has so far been largely an academic exercise. This method for fast estimation of secondary dose brings patient / plan specific information within reach, allowing clinicians to make informed decisions on the potential long‐term risks associated with specific dose delivery plans. Partial funding from NSF grant CBET‐0853157.

A clock generation circuit is proposed based on CMOSFET technology, which is comprised of a reference voltage source, a common source voltage amplifier, voltage controlled oscillator and timing circuit. By which a particular clock is produced, whose duty cycle is less than 50%. It is used 0.5 μ s CMOS process, HSPICE simulation results indicates that the average frequency of clock signal is 2.071MHz, and the average duty cycle is 31.565% .

This paper describes the morphology and mechanical properties of novel electrospun zein based fibrous membranes. From the results, due to adding PVA, it was found that the electrospun fibers of zein can turn into a new strong membrane. The fibrous membranes were characterized by tensile testing, scanning electron microscopy(SEM) and differential scanning calorimetry(DSC). Effect of PVA was analyzed as one of the most significant factors affecting the mechanical characterization of fibrous membranes. The compatibility of zein/PVA nanofibrous were also analyzed by using DSC method.

Composites of poly(L-lactide) (PLA) with hemp linter fibers, prepared by batch mixing and plasticized with poly(ethylene glycol) (PEG; weight-average molecular weight 1000 g/mol), were examined by differential scanning calorimetry, thermogravimetric analysis, and mechanical tests. The properties of samples of PLA/hemp and PLA–PEG/hemp composites were analyzed as a function of the fiber amount. The thermogravimetric analysis of the composites, carried out in air, showed that the degradation process of fiber-filled systems started earlier than that of plain PLA, independently of the presence of the plasticizer. Mechanical tests showed that the property of the composites have been enhanced increased with the hemp content. The mechanical performance high-point of composites appear with count of hemp linter fiber at 1%.(sample count of hemp fiber form 1% to 7%). Plasticization with PEG did not improve the tensile properties of the composites, but improve its break stress.

Gamma knife has been the treatment of choice for various brain tumors and functional disorders. Current gamma knife radiosurgery is planned in a ‘ball-packing’ approach and delivered in a ‘step-and-shoot’ manner, i.e. it aims to ‘pack’ the different sized spherical high-dose volumes (called ‘shots’) into a tumor volume. We have developed a dynamic scheme for gamma knife radiosurgery based on the concept of ‘dose-painting’ to take advantage of the new robotic patient positioning system on the latest Gamma Knife C™ and Perfexion™ units. In our scheme, the spherical high dose volume created by the gamma knife unit will be viewed as a 3D spherical ‘paintbrush’, and treatment planning reduces to finding the best route of this ‘paintbrush’ to ‘paint’ a 3D tumor volume. Under our dose-painting concept, gamma knife radiosurgery becomes dynamic, where the patient moves continuously under the robotic positioning system. We have implemented a fully automatic dynamic gamma knife radiosurgery treatment planning system, where the inverse planning problem is solved as a traveling salesman problem combined with constrained least-square optimizations. We have also carried out experimental studies of dynamic gamma knife radiosurgery and showed the following. (1) Dynamic gamma knife radiosurgery is ideally suited for fully automatic inverse planning, where high quality radiosurgery plans can be obtained in minutes of computation. (2) Dynamic radiosurgery plans are more conformal than step-and-shoot plans and can maintain a steep dose gradient (around 13% per mm) between the target tumor volume and the surrounding critical structures. (3) It is possible to prescribe multiple isodose lines with dynamic gamma knife radiosurgery, so that the treatment can cover the periphery of the target volume while escalating the dose for high tumor burden regions. (4) With dynamic gamma knife radiosurgery, one can obtain a family of plans representing a tradeoff between the delivery time and the dose distributions, thus giving the clinician one more dimension of flexibility of choosing a plan based on the clinical situations.

Dose calculations for radiation arc therapy are traditionally performed by approximating continuous delivery arcs with multiple static beams. For 3D conformal arc treatments, the shape and weight variation per degree is usually small enough to allow arcs to be approximated by static beams separated by 5 degrees -10 degrees . But with intensity-modulated arc therapy (IMAT), the variation in shape and dose per degree can be large enough to require a finer angular spacing. With the increase in the number of beams, a deterministic dose calculation method, such as collapsed-cone convolution/superposition, will require proportionally longer computational times, which may not be practical clinically. We propose to use a homegrown Monte Carlo kernel-superposition technique (MCKS) to compute doses for rotational delivery. The IMAT plans were generated with 36 static beams, which were subsequently interpolated into finer angular intervals for dose calculation to mimic the continuous arc delivery. Since MCKS uses random sampling of photons, the dose computation time only increased insignificantly for the interpolated-static-beam plans that may involve up to 720 beams. Ten past IMRT cases were selected for this study. Each case took approximately 15-30 min to compute on a single CPU running Mac OS X using the MCKS method. The need for a finer beam spacing is dictated by how fast the beam weights and aperture shapes change between the adjacent static planning beam angles. MCKS, however, obviates the concern by allowing hundreds of beams to be calculated in practically the same time as for a few beams. For more than 43 beams, MCKS usually takes less CPU time than the collapsed-cone algorithm used by the Pinnacle(3) planning system.

Intensity-modulated arc therapy (IMAT) is a rotational IMRT technique. It uses a set of overlapping or nonoverlapping arcs to create a prescribed dose distribution. Despite its numerous advantages, IMAT has not gained widespread clinical applications. This is mainly due to the lack of an effective IMAT leaf-sequencing algorithm that can convert the optimized intensity patterns for all beam directions into IMAT treatment arcs. To address this problem, we have developed an IMAT leaf-sequencing algorithm and software using graph algorithms in computer science. The input to our leaf-sequencing software includes (1) a set of (continuous) intensity patterns optimized by a treatment planning system at a sequence of equally spaced beam angles (typically 10 deg. apart), (2) a maximum leaf motion constraint, and (3) the number of desired arcs, k. The output is a set of treatment arcs that best approximates the set of optimized intensity patterns at all beam angles with guaranteed smooth delivery without violating the maximum leaf motion constraint. The new algorithm consists of the following key steps. First, the optimized intensity patterns are segmented into intensity profiles that are aligned with individual MLC leaf pairs. Then each intensity profile is segmented into k MLC leaf openings using amore » k-link shortest path algorithm. The leaf openings for all beam angles are subsequently connected together to form 1D IMAT arcs under the maximum leaf motion constraint using a shortest path algorithm. Finally, the 1D IMAT arcs are combined to form IMAT treatment arcs of MLC apertures. The performance of the implemented leaf-sequencing software has been tested for four treatment sites (prostate, breast, head and neck, and lung). In all cases, our leaf-sequencing algorithm produces efficient and highly conformal IMAT plans that rival their counterpart, the tomotherapy plans, and significantly improve the IMRT plans. Algorithm execution times ranging from a few seconds to 2 min are observed on a laptop computer equipped with a 2.0 GHz Pentium M processor.« less

As intensity modulated radiation therapy (IMRT) becomes routine clinical practice, its advantages and limitations are better understood. With these new understandings, some new developments have emerged in an effort to alleviate the limitations of the current IMRT practice. This article describes a few of these efforts made at the University of Maryland, including: i) improving IMRT efficiency with direct aperture optimization; ii) broadening the scope of optimization to include the mode of delivery and beam angles; and iii) new planning methods for intensity modulated arc therapy (IMAT).

The 3-D static leaf sequencing (SLS) problem arises in radiation therapy for cancer treatments, aiming to deliver a prescribed radiation dose to a target tumor accurately and efficiently. The treatment time and machine delivery error are two crucial factors to the solution (i.e., a treatment plan) for the SLS problem. In this paper, we prove that the 3-D SLS problem is NP-hard, and present the first ever algorithm for the 3-D SLS problem that can determine a tradeoff between the treatment time and machine delivery error (also called the "tongue-and-groove" error in medical literature). Our new 3-D SLS algorithm with error control gives the users (e.g., physicians) the option of specifying a machine delivery error bound, and subject to the given error bound, the algorithm computes a treatment plan with the minimum treatment time. We formulate the SLS problem with error control as computing a k-weight shortest path in a directed graph and build the graph by computing g-matchings and minimum cost flows. Further, we extend our 3-D SLS algorithm to all the popular radiotherapy machine models with different constraints. In our extensions, we model the SLS problems for some of the radiotherapy systems as computing a minimum g-path cover of a directed acyclic graph. We implemented our new 3-D SLS algorithm suite and conducted an extensive comparison study with commercial planning systems and well-known algorithms in medical literature. Some of our experimental results based on real medical data are presented.