Physics in Medicine and Biology (Phys Med Biol)

Publisher: Institute of Physics (Great Britain), IOP Publishing

Journal description

Subject coverage. The application of theoretical and practical physics to medicine, physiology and biology. Papers on physics with no obvious medical or biological applications, or papers which are almost entirely clinical or biological in their approach are not acceptable.

Current impact factor: 2.92

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 2.922
2012 Impact Factor 2.701
2011 Impact Factor 2.829
2010 Impact Factor 3.056
2009 Impact Factor 2.781
2008 Impact Factor 2.784
2007 Impact Factor 2.528
2006 Impact Factor 2.873
2005 Impact Factor 2.683
2004 Impact Factor 2.368
2003 Impact Factor 2.128
2002 Impact Factor 2.342
2001 Impact Factor 1.805
2000 Impact Factor 2.013
1999 Impact Factor 1.888
1998 Impact Factor 1.768
1997 Impact Factor 1.542
1996 Impact Factor 1.401
1995 Impact Factor 1.193
1994 Impact Factor 1.386
1993 Impact Factor 1.246
1992 Impact Factor 1.117

Impact factor over time

Impact factor

Additional details

5-year impact 2.92
Cited half-life 6.80
Immediacy index 0.45
Eigenfactor 0.04
Article influence 0.84
Website Physics in Medicine and Biology website
Other titles Physics in medicine & biology (Online), Physics in medicine and biology
ISSN 1361-6560
OCLC 34482128
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

IOP Publishing

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    • Pre-print on author's personal website, repository or arXiv.
    • Pre-print can not be updated after submission
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    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged with citation
    • Must link to publisher version with DOI
    • Set statements to accompany different versions (see policy)
    • This policy is an exception to the default policies of 'IOP Publishing'
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: The response of the alanine dosimeter to kilovoltage x-rays with respect to the dose to water was measured, relative to the response to Co-60 radiation.Two series of x-ray qualities were investigated, one ranging from 30 kV to 100 kV tube voltage (TW series), the other one ranging from 70 kV to 280 kV (TH series). Due to the use of the water calorimeter as a primary standard, the uncertainty of the delivered dose is significantly lower than for other published data. The alanine response was measured as described in a previous publication (Anton et al 2013 Phys. Med. Biol. 58 3259-82). The uncertainty component due to the alanine measurement and analysis is ⩽0.4%, the major part of the combined uncertainty of the relative response originates from the uncertainty of the delivered dose. The relative uncertainties of the relative response vary from ⩽2% for the TW series to ⩽1.1% for the TH series.Different from the behaviour of the alanine dosimeter for megavoltage x-rays or electrons, the relative response drops significantly from unity for Co-60 radiation to less than 64% for the TW quality with a tube voltage of 30 kV. In order to reproduce this behaviour through Monte Carlo simulations, not only the ratio of the absorbed dose to alanine to the absorbed dose to water has to be known, but also the intrinsic efficiency, i.e. the dependence of the number of free radicals generated per unit of absorbed dose on the photon energy. This quantity is not yet accessible for the TW series.For a possible use of the alanine dosimeter for kilovoltage x-rays, for example in electronic brachytherapy, users should rely on the measured data for the relative response which have become available with this publication.
    Physics in Medicine and Biology 08/2015; 60(15):6113-6129. DOI:10.1088/0031-9155/60/15/6113
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    ABSTRACT: We have compared two methods of estimating the cellular radiosensitivity of a heterogeneous tumour, namely, via cell-survival and via tumour control probability (TCP) pseudo-experiments. It is assumed that there exists intra-tumour variability in radiosensitivity and that the tumour consists predominantly of radiosensitive cells and a small number of radio-resistant cells.Using a multi-component, linear-quadratic (LQ) model of cell kill, a pseudo-experimental cell-survival versus dose curve is derived. This curve is then fitted with a mono-component LQ model describing the response of a homogeneous cell population. For the assumed variation in radiosensitivity it is shown that the composite pseudo-experimental survival curve is well approximated by the survival curve of cells with uniform radiosensitivity.For the same initial cell radiosensitivity distribution several pseudo-experimental TCP curves are simulated corresponding to different fractionation regimes. The TCP model used accounts for clonogen proliferation during a fractionated treatment. The set of simulated TCP curves is then fitted with a mono-component TCP model. As in the cell survival experiment the fit with a mono-component model assuming uniform radiosensitivity is shown to be highly acceptable.However, the best-fit values of cellular radiosensitivity produced via the two methods are very different. The cell-survival pseudo-experiment yields a high radiosensitivity value, while the TCP pseudo-experiment shows that the dose-response is dominated by the most resistant sub-population in the tumour, even when this is just a small fraction of the total.
    Physics in Medicine and Biology 08/2015; 60(15):N293-9. DOI:10.1088/0031-9155/60/15/N293
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    ABSTRACT: Monte-Carlo (MC) simulations are considered to be the most accurate method for calculating dose distributions in radiotherapy. Its clinical application, however, still is limited by the long runtimes conventional implementations of MC algorithms require to deliver sufficiently accurate results on high resolution imaging data. In order to overcome this obstacle we developed the software-package PhiMC, which is capable of computing precise dose distributions in a sub-minute time-frame by leveraging the potential of modern many- and multi-core CPU-based computers. PhiMC is based on the well verified dose planning method (DPM). We could demonstrate that PhiMC delivers dose distributions which are in excellent agreement to DPM. The multi-core implementation of PhiMC scales well between different computer architectures and achieves a speed-up of up to 37[Formula: see text] compared to the original DPM code executed on a modern system. Furthermore, we could show that our CPU-based implementation on a modern workstation is between 1.25[Formula: see text] and 1.95[Formula: see text] faster than a well-known GPU implementation of the same simulation method on a NVIDIA Tesla C2050. Since CPUs work on several hundreds of GB RAM the typical GPU memory limitation does not apply for our implementation and high resolution clinical plans can be calculated.
    Physics in Medicine and Biology 08/2015; 60(15):6097-6111. DOI:10.1088/0031-9155/60/15/6097
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    ABSTRACT: Patient-specific image-based dosimetry is considered to be a useful tool to limit toxicity associated with peptide receptor radionuclide therapy (PRRT). To facilitate the establishment and reliability of absorbed-dose response relationships, it is essential to assess the accuracy of dosimetry in clinically realistic scenarios. To this end, we developed pharmacokinetic digital phantoms corresponding to patients treated with (177)Lu-DOTATATE. Three individual voxel phantoms from the XCAT population were generated and assigned a dynamic activity distribution based on a compartment model for (177)Lu-DOTATATE, designed specifically for this purpose. The compartment model was fitted to time-activity data from 10 patients, primarily acquired using quantitative scintillation camera imaging. S values for all phantom source-target combinations were calculated based on Monte-Carlo simulations. Combining the S values and time-activity curves, reference values of the absorbed dose to the phantom kidneys, liver, spleen, tumours and whole-body were calculated. The phantoms were used in a virtual dosimetry study, using Monte-Carlo simulated gamma-camera images and conventional methods for absorbed-dose calculations. The characteristics of the SPECT and WB planar images were found to well represent those of real patient images, capturing the difficulties present in image-based dosimetry. The phantoms are expected to be useful for further studies and optimisation of clinical dosimetry in (177)Lu PRRT.
    Physics in Medicine and Biology 08/2015; 60(15):6131-49. DOI:10.1088/0031-9155/60/15/6131
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    ABSTRACT: Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline). A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions. For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients' studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification.
    Physics in Medicine and Biology 08/2015; 60(15):5853-5871. DOI:10.1088/0031-9155/60/15/5853
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    ABSTRACT: Radium-223 dichloride ((223)Ra) is an alpha particle emitter and a natural bone-seeking radionuclide that is currently used for treating osteoblastic bone metastases associated with prostate cancer. The stochastic nature of alpha emission, hits and energy deposition poses some challenges for estimating radiation damage. In this paper we investigate the distribution of hits to cells by multiple alpha particles corresponding to a typical clinically delivered dose using a Monte Carlo model to simulate the stochastic effects. The number of hits and dose deposition were recorded in the cytoplasm and nucleus of each cell. Alpha particle tracks were also visualized. We found that the stochastic variation in dose deposited in cell nuclei ([Formula: see text]40%) can be attributed in part to the variation in LET with pathlength. We also found that [Formula: see text]18% of cell nuclei receive less than one sigma below the average dose per cell ([Formula: see text]15.4 Gy). One possible implication of this is that the efficacy of cell kill in alpha particle therapy need not rely solely on ionization clustering on DNA but possibly also on indirect DNA damage through the production of free radicals and ensuing intracellular signaling.
    Physics in Medicine and Biology 08/2015; 60(15):6087-6096. DOI:10.1088/0031-9155/60/15/6087
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    ABSTRACT: This work investigates and compares CT image metallic artifact reduction (MAR) methods and tissue assignment schemes (TAS) for the development of virtual patient models for permanent implant brachytherapy Monte Carlo (MC) dose calculations. Four MAR techniques are investigated to mitigate seed artifacts from post-implant CT images of a homogeneous phantom and eight prostate patients: a raw sinogram approach using the original CT scanner data and three methods (simple threshold replacement (STR), 3D median filter, and virtual sinogram) requiring only the reconstructed CT image. Virtual patient models are developed using six TAS ranging from the AAPM-ESTRO-ABG TG-186 basic approach of assigning uniform density tissues (resulting in a model not dependent on MAR) to more complex models assigning prostate, calcification, and mixtures of prostate and calcification using CT-derived densities. The EGSnrc user-code BrachyDose is employed to calculate dose distributions. All four MAR methods eliminate bright seed spot artifacts, and the image-based methods provide comparable mitigation of artifacts compared with the raw sinogram approach. However, each MAR technique has limitations: STR is unable to mitigate low CT number artifacts, the median filter blurs the image which challenges the preservation of tissue heterogeneities, and both sinogram approaches introduce new streaks. Large local dose differences are generally due to differences in voxel tissue-type rather than mass density. The largest differences in target dose metrics (D90, V100, V150), over 50% lower compared to the other models, are when uncorrected CT images are used with TAS that consider calcifications. Metrics found using models which include calcifications are generally a few percent lower than prostate-only models. Generally, metrics from any MAR method and any TAS which considers calcifications agree within 6%. Overall, the studied MAR methods and TAS show promise for further retrospective MC dose calculation studies for various permanent implant brachytherapy treatments.
    Physics in Medicine and Biology 08/2015; 60(15):6039-6062. DOI:10.1088/0031-9155/60/15/6039
  • Huafeng Wang, Zhengrong Liang, Lihong C. Li, Hao Han, Bowen Song, Perry J. Pickhardt, Matthew A. Barish, Chris E. Lascarides
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    ABSTRACT: Most previous efforts in developing computer-aided detection (CADe) of colonic polyps apply similar measures or parameters to detect polyps regardless their locations under an implicit assumption that all the polyps reside in a similar local environment, e.g., on a relatively flat colon wall. In reality, this implicit assumption is frequently invalid, because the haustral folds can have a very different local environment from that of the relatively flat colon wall. We conjecture that this assumption may be a major cause of missing detection of polyps, especially small polyps (<10mm linear size) located on the haustral folds. In this paper, we take the concept of adaptiveness and present an adaptive paradigm for CADe of colonic polyps. Firstly, we decompose the complicated colon structure into two simplified sub-structures, each of which has similar properties, of (1) relatively flat colon wall and (2) ridge-shaped haustral folds. Then we develop local environment descriptions to adaptively reflect each of these two simplified sub-structures. To show the impact of the adaptive-ness of the local environment descriptions upon the polyp detection task, we focus on the local geometrical measures of the volume data for both the detection of initial polyp candidates (IPCs) and the reduction of false positives (FPs) in the IPC pool. The experimental outcome using the local geometrical measures is very impressive such that not only the previously-missed small polyps on the folds are detected, but also the previously miss-removed small polyps on the folds during FP reduction are retained. It is expected that this adaptive paradigm will have a great impact on detecting the small polyps, measuring their volumes and volume changes over time, and optimizing their management plan.
    Physics in Medicine and Biology 08/2015;
  • W Z Xie, W Friedland, W B Li, C Y Li, U Oeh, R Qiu, J L Li, C Hoeschen
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    ABSTRACT: Abundant studies have focused on the radiosensitization effect of gold nanoparticles (GNPs) in the cellular environment with x-ray irradiation. To better understand the physical foundation and to initially study the molecular radiosensitization effect within the nucleus, a simple cell model with detailed DNA structure in the central nucleus was set up and complemented with different distributions of single and multiple GNPs in this work. With the biophysical Monte Carlo simulation code PARTRAC, the radiosensitization effects on both physical quantities and primary biological responses (DNA strand breaks) were simulated. The ratios of results under situations with GNPs compared to those without GNPs were defined as the enhancement factors (EFs). The simulation results show that the presence of GNP can cause a notable enhancement effect on the energy deposition within a few micrometers from the border of GNP. The greatest upshot appears around the border and is mostly dominated by Auger electrons. The enhancement effect on the DNA strand breakage becomes smaller because of the DNA distribution inside the nucleus, and the corresponding EFs are between 1 and 1.5. In the present simulation, multiple GNPs on the nucleus surface, the 60 kVp x-ray spectrum and the diameter of 100 nm are relatively more effective conditions for both physical and biological radiosensitization effects. These results preliminarily indicate that GNP can be a good radiosensitizer in x-ray radiotherapy. Nevertheless, further biological responses (repair process, cell survival, etc) need to be studied to give more accurate evaluation and practical proposal on GNP's application in clinical treatment.
    Physics in Medicine and Biology 07/2015; 60(16):6195-6212. DOI:10.1088/0031-9155/60/16/6195
  • H Dang, J W Stayman, A Sisniega, J Xu, W Zbijewski, X Wang, D H Foos, N Aygun, V E Koliatsos, J H Siewerdsen
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    ABSTRACT: Non-contrast CT reliably detects fresh blood in the brain and is the current front-line imaging modality for intracranial hemorrhage such as that occurring in acute traumatic brain injury (contrast ~40-80 HU, size > 1 mm). We are developing flat-panel detector (FPD) cone-beam CT (CBCT) to facilitate such diagnosis in a low-cost, mobile platform suitable for point-of-care deployment. Such a system may offer benefits in the ICU, urgent care/concussion clinic, ambulance, and sports and military theatres. However, current FPD-CBCT systems face significant challenges that confound low-contrast, soft-tissue imaging. Artifact correction can overcome major sources of bias in FPD-CBCT but imparts noise amplification in filtered backprojection (FBP). Model-based reconstruction improves soft-tissue image quality compared to FBP by leveraging a high-fidelity forward model and image regularization. In this work, we develop a novel penalized weighted least-squares (PWLS) image reconstruction method with a noise model that includes accurate modeling of the noise characteristics associated with the two dominant artifact corrections (scatter and beam-hardening) in CBCT and utilizes modified weights to compensate for noise amplification imparted by each correction. Experiments included real data acquired on a FPD-CBCT test-bench and an anthropomorphic head phantom emulating intra-parenchymal hemorrhage. The proposed PWLS method demonstrated superior noise-resolution tradeoffs in comparison to FBP and PWLS with conventional weights (viz. at matched 0.50 mm spatial resolution, CNR = 11.9 compared to CNR = 5.6 and CNR = 9.9, respectively) and substantially reduced image noise especially in challenging regions such as skull base. The results support the hypothesis that with high-fidelity artifact correction and statistical reconstruction using an accurate post-artifact-correction noise model, FPD-CBCT can achieve image quality allowing reliable detection of intracranial hemorrhage.
    Physics in Medicine and Biology 07/2015; 60(16):6153-6175. DOI:10.1088/0031-9155/60/16/6153
  • Brendan Barraclough, Jonathan G Li, Sharon Lebron, Qiyong Fan, Chihray Liu, Guanghua Yan
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    ABSTRACT: The ionization chamber volume averaging effect is a well-known issue without an elegant solution. The purpose of this study is to propose a novel convolution-based approach to address the volume averaging effect in model-based treatment planning systems (TPSs). Ionization chamber-measured beam profiles can be regarded as the convolution between the detector response function and the implicit real profiles. Existing approaches address the issue by trying to remove the volume averaging effect from the measurement. In contrast, our proposed method imports the measured profiles directly into the TPS and addresses the problem by reoptimizing pertinent parameters of the TPS beam model. In the iterative beam modeling process, the TPS-calculated beam profiles are convolved with the same detector response function. Beam model parameters responsible for the penumbra are optimized to drive the convolved profiles to match the measured profiles. Since the convolved and the measured profiles are subject to identical volume averaging effect, the calculated profiles match the real profiles when the optimization converges. The method was applied to reoptimize a CC13 beam model commissioned with profiles measured with a standard ionization chamber (Scanditronix Wellhofer, Bartlett, TN). The reoptimized beam model was validated by comparing the TPS-calculated profiles with diode-measured profiles. Its performance in intensity-modulated radiation therapy (IMRT) quality assurance (QA) for ten head-and-neck patients was compared with the CC13 beam model and a clinical beam model (manually optimized, clinically proven) using standard Gamma comparisons. The beam profiles calculated with the reoptimized beam model showed excellent agreement with diode measurement at all measured geometries. Performance of the reoptimized beam model was comparable with that of the clinical beam model in IMRT QA. The average passing rates using the reoptimized beam model increased substantially from 92.1% to 99.3% with 3%/3 mm and from 79.2% to 95.2% with 2%/2 mm when compared with the CC13 beam model. These results show the effectiveness of the proposed method. Less inter-user variability can be expected of the final beam model. It is also found that the method can be easily integrated into model-based TPS.
    Physics in Medicine and Biology 07/2015; 60(16):6213-6226. DOI:10.1088/0031-9155/60/16/6213
  • Steffen Ketelhut, Ralf-Peter Kapsch
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    ABSTRACT: The spatial response functions in lateral and longitudinal directions of four cylindrical ionization chambers of the types NE 2561, FC65-G, PTW 31010, and PTW 31016, two plane-parallel ionization chambers of the types PTW 34001 and PTW 34045, and one diode of the type PTW 60012 were measured in air in high-energy photon beams with nominal accelerating voltages of 4 MV, 8 MV, and 25 MV, and electron beams with nominal energies of 6 MeV, 15 MeV, and 20 MeV. The measurements were performed by moving the detectors in small steps across the edge of a lead block for the photon beams, and across a thin slit between two lead blocks for the electron beams. Monte-Carlo calculations were used to analyze the measurements and to identify contributions of the different parts of the chamber. Finally, a simple empirical model for describing the spatial response functions is established.
    Physics in Medicine and Biology 07/2015; 60(16):6177-6194. DOI:10.1088/0031-9155/60/16/6177
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    ABSTRACT: Proton therapy promises higher dose conformality in comparison with regular radiotherapy techniques. Also, image guidance has an increasing role in radiotherapy and MRI is a prime candidate for this imaging. Therefore, in this paper the dosimetric feasibility of Intensity Modulated Proton Therapy (IMPT) in a magnetic field of 1.5 T and the effect on the generated dose distributions compared to those at 0 T is evaluated, using the Monte Carlo software TOol for PArticle Simulation (TOPAS). For three different anatomic sites IMPT plans are generated. It is shown that the generation of an IMPT plan in a magnetic field is feasible, the impact of the magnetic field is small, and the resulting dose distributions are equivalent for 0 T and 1.5 T. Also, the framework of Monte Carlo simulation combined with an inverse optimization method can be used to generate IMPT plans. These plans can be used in future dosimetric comparisons with e.g. IMRT and conventional IMPT. Finally, this study shows that IMPT in a 1.5 T magnetic field is dosimetrically feasible.
    Physics in Medicine and Biology 07/2015; 60(15):5955-5969. DOI:10.1088/0031-9155/60/15/5955
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    ABSTRACT: Accurate and efficient scatter correction is essential for acquisition of high-quality x-ray cone-beam CT (CBCT) images for various applications. This study was conducted to demonstrate the feasibility of using the data consistency condition (DCC) as a criterion for scatter kernel optimization in scatter deconvolution methods in CBCT. As in CBCT, data consistency in the mid-plane is primarily challenged by scatter, we utilized data consistency to confirm the degree of scatter correction and to steer the update in iterative kernel optimization. By means of the parallel-beam DCC via fan-parallel rebinning, we iteratively optimized the scatter kernel parameters, using a particle swarm optimization algorithm for its computational efficiency and excellent convergence. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by experimental studies using the ACS head phantom and the pelvic part of the Rando phantom. The results showed that the proposed method can effectively improve the accuracy of deconvolution-based scatter correction. Quantitative assessments of image quality parameters such as contrast and structure similarity (SSIM) revealed that the optimally selected scatter kernel improves the contrast of scatter-free images by up to 99.5%, 94.4%, and 84.4%, and of the SSIM in an XCAT study, an ACS head phantom study, and a pelvis phantom study by up to 96.7%, 90.5%, and 87.8%, respectively. The proposed method can achieve accurate and efficient scatter correction from a single cone-beam scan without need of any auxiliary hardware or additional experimentation.
    Physics in Medicine and Biology 07/2015; 60(15):5971-5994. DOI:10.1088/0031-9155/60/15/5971
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    ABSTRACT: The aim of this work was to evaluate the influence of anatomical variability between subjects and of the partial volume effect (PVE) on the standardized Specific Uptake Ratio (SUR) in [(123)I]FP-bib SPECT studies. To this end, magnetic resonance (MR) images of 23 subjects with differences in the striatal volume of up to 44% were segmented and used to generate a database of 138 Monte Carlo simulated SPECT studies. Data included normal uptakes and pathological cases. Studies were reconstructed by filtered back projection (FBP) and the ordered-subset expectation-maximization algorithm. Quantification was carried out by applying a reference method based on regions of interest (ROIs) derived from the MR images and ROIs derived from the Automated Anatomical Labelling map. Our results showed that, regardless of anatomical variability, the relationship between calculated and true SUR values for caudate and putamen could be described by a multiple linear model which took into account the spill-over phenomenon caused by PVE ([Formula: see text] for caudate and ≥0.980 for putamen) and also by a simple linear model (R(2) ≥ 0.952 for caudate and ≥0.973 for putamen). Calculated values were standardized by inverting both linear systems. Differences between standardized and true values showed that, although the multiple linear model was the best approach in terms of variability ([Formula: see text] ≥ 11.79 for caudate and ≤7.36 for putamen), standardization based on a simple linear model was also suitable ([Formula: see text] ≥ 12.44 for caudate and ≤12.57 for putamen).
    Physics in Medicine and Biology 07/2015; 60(15):5925-5938. DOI:10.1088/0031-9155/60/15/5925
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    ABSTRACT: Several ultrasound-based imaging modalities have been proposed for image guidance and monitoring of high-intensity focused ultrasound (HIFU) treatment. However, accurate localization and characterization of the effective region of treatment (focal spot) remain important obstacles in the clinical implementation of HIFU ablation. Harmonic motion imaging for focused ultrasound (HMIFU) is a HIFU monitoring technique that utilizes radiation-force-induced localized oscillatory displacement. HMIFU has been shown to correctly identify the formation and extent of HIFU thermal ablation lesions. However a significant problem remains in identifying the location of the HIFU focus, which is necessary for treatment planning. In this study, the induced displacement was employed to localize the HIFU focal spot inside the tissue prior to treatment. Feasibility was shown with two separate systems. The 1D HMIFU system consisted of a HIFU transducer emitting an amplitude-modulated HIFU beam for mechanical excitation and a confocal single-element, pulse-echo transducer for simultaneous RF acquisition. The 2D HIFU system consists of a HIFU phased array, and a co-axial imaging phased array for simultaneous imaging. Initial feasibility was first performed on tissue-mimicking gelatin phantoms and the focal zone was defined as the region corresponding to the -3dB full width at half maximum of the HMI displacement. Using the same parameters, in vitro experiments were performed in canine liver specimens to compare the defined focal zone with the lesion. In vitro measurements showed good agreement between the HMI predicted focal zone and the induced HIFU lesion location. HMIFU was experimentally shown to be capable of predicting and tracking the focal region in both phantoms and in vitro tissues. The accuracy of focal spot localization was evaluated by comparing with the lesion location in post-ablative tissues, with a R(2) = 0.821 at p < 0.002 in the 2D HMI system. We demonstrated the feasibility of using this HMI-based technique to localize the HIFU focal spot without inducing thermal changes during the planning phase. The focal spot localization method has also been applied on ex vivo human breast tissue ablation and can be fully integrated into any HMI system for planning purposes.
    Physics in Medicine and Biology 07/2015; 60(15):5911-5924. DOI:10.1088/0031-9155/60/15/5911
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    ABSTRACT: Monte Carlo simulations are used to calculate the relative biological effectiveness (RBE) of 300 MeV u(-1) carbon-ion beams at different depths in a cylindrical water phantom of 10 cm radius and 30 cm long. RBE values for the induction of DNA double strand breaks (DSB), a biological endpoint closely related to cell inactivation, are estimated for monoenergetic and energy-modulated carbon ion beams. Individual contributions to the RBE from primary ions and secondary nuclear fragments are simulated separately. These simulations are based on a multi-scale modelling approach by first applying the FLUKA (version 2011.2.17) transport code to estimate the absorbed doses and fluence energy spectra, then using the MCDS (version 3.10A) damage code for DSB yields. The approach is efficient since it separates the non-stochastic dosimetry problem from the stochastic DNA damage problem. The MCDS code predicts the major trends of the DSB yields from detailed track structure simulations. It is found that, as depth is increasing, RBE values increase slowly from the entrance depth to the plateau region and change substantially in the Bragg peak region. RBE values reach their maxima at the distal edge of the Bragg peak. Beyond this edge, contributions to RBE are entirely from nuclear fragments. Maximum RBE values at the distal edges of the Bragg peak and the spread-out Bragg peak are, respectively, 3.0 and 2.8. The present approach has the flexibility to weight RBE contributions from different DSB classes, i.e. DSB0, DSB+ and DSB++.
    Physics in Medicine and Biology 07/2015; 60(15):5995-6012. DOI:10.1088/0031-9155/60/15/5995
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    ABSTRACT: The purpose of this study is to investigate feasibility of a novel real-time dosimetry method for fluoroscopically guided interventions utilizing thin-film detector arrays in several potential locations with respect to the patient and x-ray equipment. We employed Monte Carlo (MC) simulation to establish the fluoroscopic beam model to determine dosimetric quantities directly from measured doses in thin-film detector arrays at three positions: A-attached to the x-ray source, B-on the couch under the patient and C-attached to the fluoroscopic imager. Next, we developed a calibration method to determine skin dose at the entry of the beam ([Formula: see text]) as well as the dose distribution along each ray of the beam in a water-equivalent patient model. We utilized the concept of water-equivalent thickness to determine the dose inside the patient based on doses measured outside of the patient by the thin-film detector array layers: (a) A, (b) B, or (c) B and C. In the process of calibration we determined a correction factor that characterizes the material-specific response of the detector, backscatter factor and attenuation factor for slab water phantoms of various thicknesses. Application of this method to an anthropomorphic phantom showed accuracy of about 1% for [Formula: see text] and up to about 10% for integral dose along the beam path when compared to a direct simulation of dose by MC.
    Physics in Medicine and Biology 07/2015; 60(15):5891-5909. DOI:10.1088/0031-9155/60/15/5891
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    ABSTRACT: Multimodal medical imaging is gaining increased popularity in the clinic. This stems from the fact that data acquired from different physical phenomena may provide complementary information resulting in a more comprehensive picture of the pathological state. In this context, nano-sized contrast agents may augment the potential sensitivity of each imaging modality and allow targeted visualization of physiological points of interest (e.g. tumours). In this study, 7 nm copper oxide nanoparticles (CuO NPs) were synthesized and characterized. Then, in vitro and phantom specimens containing CuO NPs ranging from 2.4 to 320 μg · mL(-1) were scanned, using both 9.4 T MRI and through-transmission ultrasonic imaging. The results show that the CuO NPs induce shortening of the magnetic T1 relaxation time on the one hand, and increase the speed of sound and ultrasonic attenuation coefficient on the other. Moreover, these visible changes are NP concentration-dependent. The change in the physical properties resulted in a substantial increase in the contrast-to-noise ratio (3.4-6.8 in ultrasound and 1.2-19.3 in MRI). In conclusion, CuO NPs are excellent candidates for MRI-ultrasound dual imaging contrast agents. They offer radiation-free high spatial resolution scans by MRI, and cost-effective high temporal resolution scans by ultrasound.
    Physics in Medicine and Biology 07/2015; 60(15):5767-5783. DOI:10.1088/0031-9155/60/15/5767