Displaying all 9 publications

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  1. Ng KH, Lau S
    Med Phys, 2015 Dec;42(12):7059-77.
    PMID: 26632060 DOI: 10.1118/1.4935141
    Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are "dense" and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.
  2. Tan JH, Acharya UR, Chua KC, Cheng C, Laude A
    Med Phys, 2016 May;43(5):2311.
    PMID: 27147343 DOI: 10.1118/1.4945413
    The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline.
  3. Yahya N, Ebert MA, Bulsara M, House MJ, Kennedy A, Joseph DJ, et al.
    Med Phys, 2016 May;43(5):2040.
    PMID: 27147316 DOI: 10.1118/1.4944738
    Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate.
  4. Safari MJ, Wong JH, Ng KH, Jong WL, Cutajar DL, Rosenfeld AB
    Med Phys, 2015 May;42(5):2550-8.
    PMID: 25979047 DOI: 10.1118/1.4918576
    The MOSkin is a MOSFET detector designed especially for skin dose measurements. This detector has been characterized for various factors affecting its response for megavoltage photon beams and has been used for patient dose measurements during radiotherapy procedures. However, the characteristics of this detector in kilovoltage photon beams and low dose ranges have not been studied. The purpose of this study was to characterize the MOSkin detector to determine its suitability for in vivo entrance skin dose measurements during interventional radiology procedures.
  5. Jamal N
    Med Phys, 2005 Sep;32(9):3057-3058.
    PMID: 28523766 DOI: 10.1118/1.2011087
  6. Kim KM, Lee MS, Suh MS, Selvam HSMS, Tan TH, Cheon GJ, et al.
    Med Phys, 2022 Jan 11.
    PMID: 35014699 DOI: 10.1002/mp.15444
    PURPOSE: Voxel-based dosimetry is potentially accurate than organ-based dosimetry because it considers the anatomical variations in each individual and the heterogeneous radioactivity distribution in each organ. Here, voxel-based dosimetry for 177 Lu-DOTATATE therapy was performed using single and multiple voxel S-value (VSV) methods and compared with Monte Carlo simulations. To verify these methods, we adopted sequential 177 Lu-DOTATATE single-photon emission computed tomography and X-ray computed tomography (SPECT/CT) dataset acquired from Sunway Medical Centre using the major vendor's SPECT/CT scanner (Siemens Symbia Intevo).

    METHODS: The administered activity of 177 Lu-DOTATATE was 7.99 ± 0.36 GBq. SPECT/CT images were acquired 0.5, 4, 24, and 48 h after injection in Sunway Medical Centre. For the multiple VSV method, VSV kernels of 177 Lu in media with various densities were generated by Geant4 Application for Emission Tomography (GATE) simulation first. The second step involved the convolution of the time-integrated activity map with each kernel to produce medium-specific dose maps. Third, each medium-specific dose map was masked using binary medium masks, which were generated from CT-based density maps. Finally, all masked dose maps were summed to generate the final dose map. VSV methods with four different VSV sets (1, 4, 10, and 20 VSVs) were compared. Voxel-wise density correction for the single VSV method was also performed. The absorbed doses in the kidneys, bone marrow, and tumors were analyzed, and the relative errors between the VSV and Monte Carlo simulation approaches were estimated. Organ-based dosimetry using Organ Level INternal Dose Assessment/EXponential Modeling (OLINDA/EXM) was also compared.

    RESULTS: The accuracy of the multiple VSV approach increased with the number of dose kernels. The average dose estimation errors of a single VSV with density correction and 20 VSVs were less than 6% in most cases, although organ-based dosimetry using OLINDA/EXM yielded an error of up to 123%. The advantages of the single VSV method with density correction and the 20 VSVs over organ-based dosimetry were most evident in bone marrow and bone-metastatic tumors with heterogeneous medium properties.

    CONCLUSION: The single VSV method with density correction and multiple VSV method with 20 dose kernels enabled fast and accurate radiation dose estimation. Accordingly, voxel-based dosimetry methods can be useful for managing administration activity and for investigating tumor dose responses to further increase the therapeutic efficacy of 177 Lu-DOTATATE.

  7. Abdul Hadi MFR, Abdullah AN, Hashikin NAA, Ying CK, Yeong CH, Yoon TL, et al.
    Med Phys, 2022 Dec;49(12):7742-7753.
    PMID: 36098271 DOI: 10.1002/mp.15980
    PURPOSE: Monte Carlo (MC) simulation is an important technique that can help design advanced and challenging experimental setups. GATE (Geant4 application for tomographic emission) is a useful simulation toolkit for applications in nuclear medicine. Transarterial radioembolization is a treatment for liver cancer, where microspheres embedded with yttrium-90 (90 Y) are administered intra-arterially to the tumor. Personalized dosimetry for this treatment may provide higher dosimetry accuracy compared to the conventional partition model (PM) calculation. However, incorporation of three-dimensional tomographic input data into MC simulation is an intricate process. In this article, 3D Slicer, free and open-source software, was utilized for the incorporation of patient tomographic images into GATE to demonstrate the feasibility of personalized dosimetry in hepatic radioembolization with 90 Y.

    METHODS: In this article, the steps involved in importing, segmenting, and registering tomographic images using 3D Slicer were thoroughly described, before importing them into GATE for MC simulation. The absorbed doses estimated using GATE were then compared with that of PM. SlicerRT, a 3D Slicer extension, was then used to visualize the isodose from the MC simulation.

    RESULTS: A workflow diagram consisting of all the steps taken in the utilization of 3D Slicer for personalized dosimetry in 90 Y radioembolization has been presented in this article. In comparison to the MC simulation, the absorbed doses to the tumor and normal liver were overestimated by PM by 105.55% and 20.23%, respectively, whereas for lungs, the absorbed dose estimated by PM was underestimated by 25.32%. These values were supported by the isodose distribution obtained via SlicerRT, suggesting the presence of beta particles outside the volumes of interest. These findings demonstrate the importance of personalized dosimetry for a more accurate absorbed dose estimation compared to PM.

    CONCLUSION: The methodology provided in this study can assist users (especially students or researchers who are new to MC simulation) in navigating intricate steps required in the importation of tomographic data for MC simulation. These steps can also be utilized for other radiation therapy related applications, not necessarily limited to internal dosimetry.

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