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Biomedical subjects

Shiva K Das

Publications and source records attributed to Shiva K Das.

8 recordsLinked to original sources

A methodology for using SPECT to reduce intensity-modulated radiation therapy (IMRT) dose to functioning lung.

PURPOSE: Single photon emission computed tomography (SPECT) provides a map of the spatial distribution of lung perfusion. Thus, SPECT guidance can be used to divert dose away from higher-functioning lung, potentially reducing lung toxicity. We present a methodology for achieving this aim and test it in intensity-modulated radiotherapy (IMRT) treatment-planning. METHODS AND MATERIALS: IMRT treatment plans were generated with and without SPECT guidance and compared for 5 patients. Healthy lung was segmented into four regions on the basis of SPECT intensity in the SPECT plan. Dose was sequentially allowed to the target via regions of increasing SPECT intensity. This process results in reduction of dose to functional lung, reflected in the dose-function histogram (DFH). The plans were compared using DFHs and F(20)/F(30) values (F(x) is the functional lung receiving dose above x Gy). RESULTS: In all cases, the SPECT-guided plan produced a more favorable DFH compared with the non-SPECT-guided plan. Additionally, the F(20) and F(30) values were reduced for all patients by an average of 13.6% +/- 5.2% and 10.5% +/- 5.8%, respectively. In all patients, DFHs of the two highest-functioning SPECT regions were reduced, whereas DFHs of the two lower-functioning regions were increased, illustrating the dose "give-take" between SPECT regions during redistribution. CONCLUSIONS: SPECT-guided IMRT shows potential for reducing the dose delivered to highly functional lung regions. This dose reduction could reduce the number of high-grade pneumonitis cases that develop after radiation treatment and improve patient quality of life.

Algorithms↗

Improved magnetic resonance thermal imaging by combining proton resonance frequency shift (PRFS) and apparent diffusion coefficient (ADC) data.

Effective thermal therapy for cancer is dependent on adequate tumor heating. Adequate heating, in turn, is dependent on reliable volumetric measurement of temperature to guide heating. Proton resonance frequency shift (PRFS) magnetic resonance imaging is frequently used for the purpose of three-dimensional temperature imaging. However, this method is susceptible to drift in the imaging magnetic field, leading to a corresponding drift in the measured temperature. A new approach to correcting for this drift is presented. It combines PRFS imaging alternated with water apparent diffusion coefficient (ADC) imaging to yield resulting thermal images that are drift corrected and possess the complementary strengths of the two methods. The drift-corrected PRFS images retain the high resolution and relatively noise-free characteristic of PRFS imaging while adding on the drift-free stability of ADC imaging. This technique is successfully demonstrated in a phantom experiment. This technique is also applicable to correcting motion-induced sudden large discontinuities in PRFS imaging, although not explicitly demonstrated in this work.

Body Temperature↗

Predicting radiotherapy-induced cardiac perfusion defects.

The purpose of this work is to compare the efficacy of mathematical models in predicting the occurrence of radiotherapy-induced left ventricular perfusion defects assessed using single-photon emission computed tomography (SPECT). The basis of this study is data from 73 left-sided breast/ chestwall patients treated with tangential photon fields. The mathematical models compared were three commonly used parametric models [Lyman normal tissue complication probability (LNTCP), relative serialty (RS), generalized equivalent uniform dose (gEUD)] and a nonparametric model (Linear discriminant analysis--LDA). Data used by the models were the left ventricular dose--volume histograms, or SPECT-based dose-function histograms, and the presence/absence of SPECT perfusion defects 6 months postradiation therapy (21 patients developed defects). For the parametric models, maximum likelihood estimation and F-tests were used to fit the model parameters. The nonparametric LDA model step-wise selected features (volumes/function above dose levels) using a method based on receiver operating characteristics (ROC) analysis to best separate the groups with and without defects. Optimistic (upper bound) and pessimistic (lower bound) estimates of each model's predictive capability were generated using ROC curves. A higher area under the ROC curve indicates a more accurate model (a model that is always accurate has area = 1). The areas under these curves for different models were used to statistically test for differences between them. Pessimistic estimates of areas under the ROC curve using dose-volume histogram/ dose-function histogram inputs, in order of increasing prediction accuracy, were LNTCP (0.79/0.75), RS (0.80/0.77), gEUD (0.81/0.78), and LDA (0.84/0.86). Only the LDA model benefited from SPECT-based regional functional information. In general, the LDA model was statistically superior to the parametric models. The LDA model selected as features the left ventricular volumes above approximately 23 Gy (V23), essentially volume in field, and 33 Gy (V33), as best separating the groups with and without defects. In conclusion, the nonparametric LDA model appears to be a more accurate predictor of radiotherapy-induced left ventricular perfusion defects than commonly used parametric models.

Dose-Response Relationship, Radiation↗

A dose-volume-based tool for evaluating and ranking IMRT treatment plans.

External beam radiotherapy is commonly used for patients with cancer. While tumor shrinkage and palliation are frequently achieved, local control and cure remain elusive for many cancers. With regard to local control, the fundamental problem is that radiotherapy-induced normal tissue injury limits the dose that can be delivered to the tumor. While intensity-modulated radiation therapy (IMRT) allows for the delivery of higher tumor doses and the sparing of proximal critical structures, multiple competing plans can be generated based on dosimetric and/or biological constraints that need to be considered/compared. In this work, an IMRT treatment plan evaluation and ranking tool, based on dosimetric criteria, is presented. The treatment plan with the highest uncomplicated target conformity index (TCI+) is ranked at the top. The TCI+ is a dose-volume-based index that considers both a target conformity index (TCI) and a normal tissue-sparing index (NTSI). TCI+ is designed to assist in the process of judging the merit of a clinical treatment plan. To demonstrate the utility of this tool, several competing lung and prostate IMRT treatment plans are compared. Results show that the plan with the highest TCI+ values accomplished the competing goals of tumor coverage and critical structures sparing best, among rival treatment plans for both treatment sites. The study demonstrates, first, that dose-volume-based indices, which summarize complex dose distributions through a single index, can be used to automatically select the optimal plan among competing plans, and second, that this dose-volume-based index may be appropriate for ranking IMRT dose distributions.

Algorithms↗

A preliminary study of the role of modulated electron beams in intensity modulated radiotherapy, using automated beam orientation and modality selection.

PURPOSE: To develop an algorithm for optimal beam arrangement selection in intensity-modulated radiotherapy (IMRT) of mixed photon and electron beams. To apply this algorithm to study the utility of modulated electron beams in the context of IMRT planning. METHODS AND MATERIALS: The optimization algorithm selects, for a user-specified number of beams, the optimal IMRT arrangement (beam orientations, and photon/electron modality for each orientation) using a novel fast heuristic intensity modulation procedure. The algorithm was employed to select optimal beam arrangements for breast (two, four, and six axial beams) and head-and-neck (three, four, five, and seven nonaxial beams) cases. RESULTS: For the two cases, increasing the number of selected beams: (1) increased the number of electron beams for the breast case, but not more than one electron beam was selected for the head-and-neck case; (2) decreased critical structure doses for both cases; and (3) decreased target homogeneity for the breast case, but improved it for the head-and-neck case. CONCLUSIONS: In the two cases analyzed using the selection algorithm, the primary role of modulated electrons differs based on treatment site-normal tissue dose reduction in breast and target homogeneity improvement in head and neck. Although this preliminary study with two cases appears to suggest that the role of intensity-modulated electrons differs based on treatment site, further investigation of large numbers of cases and varied treatment sites are required to establish a definitive conclusion.

Algorithms↗

Incorporation of functional imaging data in the evaluation of dose distributions using the generalized concept of equivalent uniform dose.

Advances in the fields of IMRT and functional imaging have greatly increased the prospect of escalating the dose to highly active or hypoxic tumour sub-volumes and steering the dose away from highly functional critical structure regions. However, current clinical treatment planning and evaluation tools assume homogeneous activity/function status in the tumour/critical structures. A method was developed to incorporate tumour/critical structure heterogeneous functionality in the generalized concept of equivalent uniform dose (EUD). The tumour and critical structures functional EUD (FEUD) values were calculated from the dose-function histogram (DFH), which relates dose to the fraction of total function value at that dose. The DFH incorporates flouro-deoxyglucose positron emission tomography (FDG-PET) functional data for tumour, which describes the distribution of metabolically active tumour clonogens, and single photon emission computed tomography (SPECT) perfusion data for critical structures. To demonstrate the utility of the method, the lung dose distributions of two non-small cell lung cancer patients, who received 3D conformal external beam radiotherapy treatment with curative intent, were evaluated. Differences between the calculated lungs EUD and FEUD values of up to 50% were observed in the 3D conformal plans. In addition, a non-small cell lung cancer patient was inversely planned with a target dose prescription of 76 Gy. Two IMRT plans (plan-A and plan-B) were generated for the patient based on the CT, FDG-PET and SPECT treatment planning images using dose-volume objective functions. The IMRT plans were generated with the goal of achieving more critical structures sparing in plan-B than plan-A. Results show the target volume EUD in plan-B is lower than plan-A by 5% with a value of 73.31 Gy, and the FEUD in plan-B is lower than plan-A by 2.6% with a value of 75.77 Gy. The FEUD plan-B values for heart and lungs were lower than plan-A by 22% and 18%, respectively. While EUD values show plan-A is marginally better than plan-B in terms of target volumetric coverage, the FEUD plan-B values show adequate target function coverage with significant critical structure function sparing. In conclusion, incorporating functional data in the calculation of EUD is important in evaluating the biological merit of treatment plans.

Carcinoma, Non-Small-Cell Lung↗

Penalized likelihood fluence optimization with evolutionary components for intensity modulated radiation therapy treatment planning.

A novel iterative penalized likelihood algorithm with evolutionary components for the optimization of beamlet fluences for intensity modulated radiation therapy (IMRT) is presented. This algorithm is designed to be flexible in terms of the objective function and automatically escalates dose, as long as the objective function increases and all constraints are met. For this study, the objective function employed was the product of target equivalent uniform dose (EUD) and fraction of target tissue within set homogeneity constraints. The likelihood component of the algorithm iteratively attempts to minimize the mean squared error between a homogeneous dose prescription and the actual target dose distribution. The updated beamlet fluences are then adjusted via a quadratic penalty function that is based on the dose-volume histogram (DVH) constraints of the organs at risk. The evolutionary components were included to prevent the algorithm from converging to a local maximum. The algorithm was applied to a prostate cancer dataset, with especially difficult DVH constraints on bladder, rectum, and femoral heads. Dose distributions were generated for manually selected sets of three-, four-, five-, and seven-field treatment plans. Additionally, a global search was performed to find the optimal orientations for an axial three-beam plan. The results from this optimal orientation set were compared to results for manually selected orientation (gantry angle) sets of 3- (0 degrees, 90 degrees, 270 degrees), 4- (0 degrees, 90 degrees, 180 degrees, 270 degrees), 5- (0 degrees, 50 degrees, 130 degrees, 230 degrees, 310 degrees), and 7- (0 degrees, 40 degrees, 90 degrees, 140 degrees, 230 degrees, 270 degrees, 320 degrees) field axial treatment plans. For all the plans generated, all DVH constraints were met and average optimization computation time was approximately 30 seconds. For the manually selected orientations, the algorithm was successful in providing a relatively homogeneous target dose distribution, while simultaneously satisfying dose-volume limits by diverting dose away from proximal critical structures. The global search for an optimal three-beam orientation set yielded gantry angles of 70 degrees, 170 degrees, and 320 degrees. The EUD for this orientation set was 58 Gy, with 96% of the target within the set upper and lower limits. In comparison, optimized EUDs for the manually selected orientation sets of three, four, five and seven beams were 52.3, 52.6, 56.9, and 61.3 Gy, respectively. The orientation optimized three-beam plan yielded higher EUDs than the manually selected three-, four-, and five-beam plans, but lower EUDs than the seven-beam plan. In conclusion, a novel penalized likelihood algorithm with evolutionary components has successfully been implemented to optimize beamlet fluences for IMRT. Initial results are promising for dose conformity and uniformity of dose to target. When combined with optimal beam orientation selection for prostate cancer treatment planning, the results indicate that plans with a small number of optimized beam orientations achieve results comparable to those with a larger number of conventionally oriented beams.

Bone and Bones↗

Optimization of a 90Sr/90Y radiation source train stepping for intravascular brachytherapy.

A steepest-descent gradient algorithm is developed to optimize the stepping of a 90Sr/90Y radiation source train (RST) for intravascular brachytherapy (IVB). The objective function is to deliver a uniform dose in a coronary target vessel and minimize the dose in adjacent normal vessel tissue at the proximal and distal edges of the coronary target vessel. Based on the target length and number of dwell points (number of steps), the algorithm modulates the dwell times and corresponding dwell positions that optimize the weighted addition of staggered EGS4 Monte Carlo (MC) calculated dose distribution from a single RST. Stepping treatment plans are generated for target vessel lengths of 3.0, 3.3, and 3.8 cm. For both the unoptimized and optimized plans, the dose heterogeneity in the target vessel wall, and length of nontarget vessel receiving 3 Gy, is assessed to compare plans. Optimization results show a 14% dose uniformity within the target is achievable for all vessel lengths. Further, the dose in the adjacent normal tissue is lower in the optimized plans than the unoptimized plans. The work presented in this paper provides a model to address the finite length of RST in IVB treatments. While the results presented are specific to the 90Sr/90Y RST, the methods should apply to other finite length RSTs.

Algorithms↗