Publications by authors named "Taiki Magome"

31 Publications

Evaluation of a Monte Carlo-based algorithm for the influence of totally implantable venous access ports in external radiation therapy.

Jpn J Radiol 2021 Apr 2;39(4):387-394. Epub 2020 Nov 2.

Division of Medical Quantum Science, Department of Health Sciences, Kyushu University Graduate School of Medical Sciences School of Medicine, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.

Purpose: This study aimed to assess whether a Monte Carlo (MC)-based algorithm reflects the influence of totally implantable venous access ports (TIVAPs) in external radiation therapy.

Materials And Methods: The present study comprised two steps: experimental measurements of depth doses and surface doses with and without TIVAPs and calculation with an MC-based algorithm.

Results: The TIVAP-associated maximum dose reduction compared with the dose at the same depths without TIVAPs was 7.8% at 4 MV, 6.9% at 6 MV, and 5.7% at 10 MV in measurement, and 7.4% at 4 MV, 6.6% at 6 MV, and 5.5% at 10 MV in calculation. Relative surface doses were higher with TIVAPs made of titanium, due to a higher fluence of backscattered electrons from the TIVAPs, than with plastic TIVAPs. There were no significant differences in the relative differences between the measured and calculated doses of the titanium TIVAP group and the plastic TIVAP group at 4 MV (p = 0.99), 6 MV (p = 0.67), and 10 MV (p = 0.54).

Conclusion: TIVAPs caused target dose reductions and dose increase near the TIVAP, especially when made of titanium. The influences are reflected in the MC-based algorithm.
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http://dx.doi.org/10.1007/s11604-020-01062-9DOI Listing
April 2021

[12. English Expression Tips for Specialized Fields].

Nihon Hoshasen Gijutsu Gakkai Zasshi 2020 ;76(8):848-854

University of Fukui Hospital.

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http://dx.doi.org/10.6009/jjrt.2020_JSRT_76.8.848DOI Listing
November 2020

[Introduction of Medical Physics Course in Komazawa University].

Igaku Butsuri 2020;40(2):68-70

Graduate Division of Health Sciences, Komazawa University.

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http://dx.doi.org/10.11323/jjmp.40.2_68DOI Listing
September 2020

Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography.

J Med Invest 2020 ;67(1.2):30-39

Department of Radiology, The University of Tokyo Hospital, Japan.

Statistical iterative reconstruction is expected to improve the image quality of computed tomography (CT). However, one of the challenges of iterative reconstruction is its large computational cost. The purpose of this review is to summarize a fast iterative reconstruction algorithm by optimizing reconstruction parameters. Megavolt projection data was acquired from a TomoTherapy system and reconstructed using in-house statistical iterative reconstruction algorithm. Total variation was used as the regularization term and the weight of the regularization term was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual assessment of spatial resolution using Gammex and Cheese phantoms. Gradient decent with an adaptive convergence parameter, ordered subset expectation maximization (OSEM), and CPU/GPU parallelization were applied in order to accelerate the present reconstruction algorithm. The SNR and CNR of the iterative reconstruction were several times better than that of filtered back projection (FBP). The GPU parallelization code combined with the OSEM algorithm reconstructed an image several hundred times faster than a CPU calculation. With 500 iterations, which provided good convergence, our method produced a 512 × 512 pixel image within a few seconds. The image quality of the present algorithm was much better than that of FBP for patient data. J. Med. Invest. 67 : 30-39, February, 2020.
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http://dx.doi.org/10.2152/jmi.67.30DOI Listing
June 2021

Fully automated dose prediction using generative adversarial networks in prostate cancer patients.

PLoS One 2020 4;15(5):e0232697. Epub 2020 May 4.

Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Ariake, Koto-ku, Tokyo, Japan.

Purpose: Although dose prediction for intensity modulated radiation therapy (IMRT) has been accomplished by a deep learning approach, delineation of some structures is needed for the prediction. We sought to develop a fully automated dose-generation framework for IMRT of prostate cancer by entering the patient CT datasets without the contour information into a generative adversarial network (GAN) and to compare its prediction performance to a conventional prediction model trained from patient contours.

Methods: We propose a synthetic approach to translate patient CT datasets into a dose distribution for IMRT. The framework requires only paired-images, i.e., patient CT images and corresponding RT-doses. The model was trained from 81 IMRT plans of prostate cancer patients, and then produced the dose distribution for 9 test cases. To compare its prediction performance to that of another trained model, we created a model trained from structure images. Dosimetric parameters for the planning target volume (PTV) and organs at risk (OARs) were calculated from the generated and original dose distributions, and mean differences of dosimetric parameters were compared between the CT-based model and the structure-based model.

Results: The mean differences of all dosimetric parameters except for D98% and D95% for PTV were within approximately 2% and 3% of the prescription dose for OARs in the CT-based model, while the differences in the structure-based model were within approximately 1% for PTV and approximately 2% for OARs, with a mean prediction time of 5 seconds per patient.

Conclusions: Accurate and rapid dose prediction was achieved by the learning of patient CT datasets by a GAN-based framework. The CT-based dose prediction could reduce the time required for both the iterative optimization process and the structure contouring, allowing physicians and dosimetrists to focus their expertise on more challenging cases.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232697PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197852PMC
August 2020

[8. How to Write Abstract].

Authors:
Taiki Magome

Nihon Hoshasen Gijutsu Gakkai Zasshi 2020 ;76(4):433-436

Department of Radiological Sciences, Komazawa University.

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http://dx.doi.org/10.6009/jjrt.2020_JSRT_76.4.433DOI Listing
November 2020

A convolution neural network for higher resolution dose prediction in prostate volumetric modulated arc therapy.

Phys Med 2020 Apr 1;72:88-95. Epub 2020 Apr 1.

Department of Radiation Oncology, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita, Osaka 565-0871 Japan.

Purpose: This study aims to investigate the feasibility of using convolutional neural networks to predict an accurate and high resolution dose distribution from an approximated and low resolution input dose.

Methods: Sixty-six patients were treated for prostate cancer with VMAT. We created the treatment plans using the Acuros XB algorithm with 2 mm grid size, followed by the dose calculated using the anisotropic analytical algorithm with 5 mm grid with the same plan parameters. U-net model was used to predict 2 mm grid dose from 5 mm grid dose. We investigated the two models differing for the training data used as input, one used just the low resolution dose (D model) and the other combined the low resolution dose with CT data (DC model). Dice similarity coefficient (DSC) was calculated to ascertain how well the shape of the dose-volume is matched. We conducted gamma analysis for the following: DVH from the two models and the reference DVH for all prostate structures.

Results: The DSC values in the DC model were significantly higher than those in the D model (p < 0.01). For the CTV, PTV, and bladder, the gamma passing rates in the DC model were significantly higher than those in the D model (p < 0.002-0.02). The mean doses in the CTV and PTV for the DC model were significantly better matched to those in the reference dose (p < 0.0001).

Conclusions: The proposed U-net model with dose and CT image used as input predicted more accurate dose.
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http://dx.doi.org/10.1016/j.ejmp.2020.03.023DOI Listing
April 2020

Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy.

J Radiat Res 2019 Nov;60(6):818-824

Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.

The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual patient by using a machine learning model. A total of 35 patients with malignant glioma were included in this study. The candidate features included 12 clinical features and 192 dose-volume histogram (DVH) features. The appropriate input features and parameters of the support vector machine (SVM) were selected using the genetic algorithm based on Akaike's information criterion, i.e. clinical, DVH, and both clinical and DVH features. The prediction accuracy of the SVM models was evaluated through a leave-one-out cross-validation test with residual error, which was defined as the absolute difference between the actual and predicted survival times after radiotherapy. Moreover, the influences of various values of prescription dose and treatment duration on the predicted survival time were evaluated. The prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of both features (P < 0.01, Wilcoxon signed rank test). Mean ± standard deviation of the leave-one-out cross-validation using the combined clinical and DVH features, only clinical features and only DVH features were 104.7 ± 96.5, 144.2 ± 126.1 and 204.5 ± 186.0 days, respectively. The prediction accuracy could be improved with the combination of clinical and DVH features, and our results show the potential to optimize the treatment strategy for individual patients based on a machine learning model.
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http://dx.doi.org/10.1093/jrr/rrz066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7357235PMC
November 2019

Evaluation of organ-at-risk dose reduction with jaw tracking technique in flattening filter-free beams in lung stereotactic body radiation therapy.

Phys Med 2019 May 3;61:70-76. Epub 2019 May 3.

Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan. Electronic address:

Purpose: (i) to investigate the capability of organ-at-risk (OAR) dose reduction with the jaw tracking (JT) technique in flattening filter-free (FFF) beams in lung stereotactic body radiation therapy (SBRT), (ii) to propose a novel metric to quantify the jaw movements during JT, and (iii) to examine the relationships between the quantified jaw movements and reduction rate of OAR doses.

Methods: The individual SBRT plans with volumetric modulated arc therapy using the JT technique (JT-VMAT) and VMAT plans with a fixed jaw (FJ-VMAT) were created for 15 patients, and dosimetric parameters were compared. A jaw tracking complexity score (JTCS) was defined and compared with the multi-leaf collimator (MLC) modulation complexity score (MCS). The correlations between the JTCS and reduction rate of OAR doses were examined.

Results: The decrease of OARs doses was statistically significant in the JT-VMAT plans (1.2% in V20 of the lung and <1% in all other OARs). The correlations between the JTCS and MCS were not significant. There were significant correlations between the JTCS and the reduction rates in V, V, and D of the lung, D of the spinal cord, and D of the body.

Conclusions: A significant decrease of dosimetric parameters of OARs was found with JT-VMAT in FFF beams. This reduction is very small and probably not clinically relevant. JTCS, a novel metric to quantify the jaw movements during JT, was proposed, and the complexity of jaw movements did not correlate with that of the movements of MLC leaves. There were significant correlations between the JTCS and some dosimetric parameters of OARs.
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http://dx.doi.org/10.1016/j.ejmp.2019.04.018DOI Listing
May 2019

Deep convolutional neural network for reduction of contrast-enhanced region on CT images.

J Radiat Res 2019 Oct;60(5):586-594

Department of Radiation Oncology, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita, Osaka, Japan.

This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The transverse images were divided into 64 × 64 pixels. This resulted in 14 723 patches in total for both non-contrast and contrast-enhanced CT image pairs. The proposed CNN model comprises five two-dimensional (2D) convolution layers with one shortcut path. For comparison, the U-net model, which comprises five 2D convolution layers interleaved with pooling and unpooling layers, was used. Training was performed in 24 patients and, for testing of trained models, another 5 patients were used. For quantitative evaluation, 50 regions of interest (ROIs) were selected on the reference contrast-enhanced image of the test data, and the mean pixel value of the ROIs was calculated. The mean pixel values of the ROIs at the same location on the reference non-contrast image and the predicted non-contrast image were calculated and those values were compared. Regarding the quantitative analysis, the difference in mean pixel value between the reference contrast-enhanced image and the predicted non-contrast image was significant (P < 0.0001) for both models. Significant differences in pixels (P < 0.0001) were found using the U-net model; in contrast, there was no significant difference using the proposed CNN model when comparing the reference non-contrast images and the predicted non-contrast images. Using the proposed CNN model, the contrast-enhanced region was satisfactorily reduced.
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http://dx.doi.org/10.1093/jrr/rrz030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805976PMC
October 2019

[Report of ASTRO2017].

Igaku Butsuri 2018;38(1):24-26

Department of Radiological Sciences, Faculty of Health Sciences, Komazawa University.

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http://dx.doi.org/10.11323/jjmp.38.1_24DOI Listing
March 2019

Whole-Body Distribution of Leukemia and Functional Total Marrow Irradiation Based on FLT-PET and Dual-Energy CT.

Mol Imaging 2017 Jan-Dec;16:1536012117732203

2 Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA.

This report describes a multimodal whole-body 3'-deoxy-3'[(18)F]-fluorothymidine positron emission tomography (FLT-PET) and dual-energy computed tomography (DECT) method to identify leukemia distribution within the bone marrow environment (BME) and to develop disease- and/or BME-specific radiation strategies. A control participant and a newly diagnosed patient with acute myeloid leukemia prior to induction chemotherapy were scanned with FLT-PET and DECT. The red marrow (RM) and yellow marrow (YM) of the BME were segmented from DECT using a basis material decomposition method. Functional total marrow irradiation (fTMI) treatment planning simulations were performed combining FLT-PET and DECT imaging to differentially target irradiation to the leukemia niche and the rest of the skeleton. Leukemia colonized both RM and YM regions, adheres to the cortical bone in the spine, and has enhanced activity in the proximal/distal femur, suggesting a potential association of leukemia with the BME. The planning target volume was reduced significantly in fTMI compared with conventional TMI. The dose to active disease (standardized uptake value >4) was increased by 2-fold, while maintaining doses to critical organs similar to those in conventional TMI. In conclusion, a hybrid system of functional-anatomical-physiological imaging can identify the spatial distribution of leukemia and will be useful to both help understand the leukemia niche and develop targeted radiation strategies.
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http://dx.doi.org/10.1177/1536012117732203DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624344PMC
July 2018

Cone-beam CT reconstruction for non-periodic organ motion using time-ordered chain graph model.

Radiat Oncol 2017 Sep 4;12(1):145. Epub 2017 Sep 4.

Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, 113-8655, Japan.

Purpose: The purpose of this study is to introduce the new concept of a four-dimensional (4D) cone-beam computed tomography (CBCT) reconstruction approach for non-periodic organ motion in cooperation with the time-ordered chain graph model (TCGM) and to compare it with previously developed methods such as total variation-based compressed sensing (TVCS) and prior-image constrained compressed sensing (PICCS).

Materials And Methods: Our proposed reconstruction is based on a model including the constraint originating from the images of neighboring time phases. Namely, the reconstructed time-series images depend on each other in this TCGM scheme, and the time-ordered images are concurrently reconstructed in the iterative reconstruction approach. In this study, iterative reconstruction with the TCGM was carried out with 90° projection ranges. The images reconstructed by the TCGM were compared with the images reconstructed by TVCS (200° projection ranges) and PICCS (90° projection ranges). Two kinds of projection data sets-an elliptic-cylindrical digital phantom and two clinical patients' data-were used. For the digital phantom, an air sphere was contained and virtually moved along the longitudinal axis by 3 cm/30 s and 3 cm/60 s; the temporal resolution was evaluated by measuring the penumbral width of the air sphere. The clinical feasibility of the non-periodic time-ordered 4D CBCT image reconstruction was examined with the patient data in the pelvic region.

Results: In the evaluation of the digital-phantom reconstruction, the penumbral widths of the TCGM yielded the narrowest result; the results obtained by PICCS and TCGM using 90° projection ranges were 2.8% and 18.2% for 3 cm/30 s, and 5.0% and 23.1% for 3 cm/60 s narrower than that of TVCS using 200° projection ranges. This suggests that the TCGM has a better temporal resolution, whereas PICCS seems similar to TVCS. These reconstruction methods were also compared using patients' projection data sets. Although all three reconstruction results showed motion related to rectal gas or stool, the result obtained by the TCGM was visibly clearer with less blurring.

Conclusion: The TCGM is a feasible approach to visualize non-periodic organ motion. The digital-phantom results demonstrated that the proposed method provides 4D image series with a better temporal resolution compared to TVCS and PICCS. The clinical patients' results also showed that the present method enables us to visualize motion related to rectal gas and flatus in the rectum.
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http://dx.doi.org/10.1186/s13014-017-0879-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5584034PMC
September 2017

Use of dual-energy computed tomography to measure skeletal-wide marrow composition and cancellous bone mineral density.

J Bone Miner Metab 2017 Jul 9;35(4):428-436. Epub 2016 Dec 9.

Department of Radiation Oncology, University of Minnesota, Minneapolis, MN, USA.

Temporal and spatial variations in bone marrow adipose tissue (MAT) can be indicative of several pathologies and confound current methods of assessing immediate changes in bone mineral remodeling. We present a novel dual-energy computed tomography (DECT) method to monitor MAT and marrow-corrected volumetric BMD (mcvBMD) throughout the body. Twenty-three cancellous skeletal sites in 20 adult female cadavers aged 40-80 years old were measured using DECT (80 and 140 kVp). vBMD was simultaneous recorded using QCT. MAT was further sampled using MRI. Thirteen lumbar vertebrae were then excised from the MRI-imaged donors and examined by microCT. After MAT correction throughout the skeleton, significant differences (p < 0.05) were found between QCT-derived vBMD and DECT-derived mcvBMD results. McvBMD was highly heterogeneous with a maximum at the posterior skull and minimum in the proximal humerus (574 and 0.7 mg/cc, respectively). BV/TV and BMC have a nearly significant correlation with mcvBMD (r = 0.545, p = 0.057 and r = 0.539, p = 0.061, respectively). MAT assessed by DECT showed a significant correlation with MRI MAT results (r = 0.881, p < 0.0001). Both DECT- and MRI-derived MAT had a significant influence on uncorrected vBMD (r = -0.86 and r = -0.818, p ≤ 0.0001, respectively). Conversely, mcvBMD had no correlation with DECT- or MRI-derived MAT (r = 0.261 and r = 0.067). DECT can be used to assess MAT while simultaneously collecting mcvBMD values at each skeletal site. MAT is heterogeneous throughout the skeleton, highly variable, and should be accounted for in longitudinal mcvBMD studies. McvBMD accurately reflects the calcified tissue in cancellous bone.
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http://dx.doi.org/10.1007/s00774-016-0796-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5689468PMC
July 2017

Fast Megavoltage Computed Tomography: A Rapid Imaging Method for Total Body or Marrow Irradiation in Helical Tomotherapy.

Int J Radiat Oncol Biol Phys 2016 11 6;96(3):688-95. Epub 2016 Jul 6.

Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota; Department of Therapeutic Radiology, University of Minnesota, Minneapolis, Minnesota; Department of Radiation Oncology and Beckman Research Institute, City of Hope, Duarte, California. Electronic address:

Purpose: Megavoltage computed tomographic (MVCT) imaging has been widely used for the 3-dimensional (3-D) setup of patients treated with helical tomotherapy (HT). One drawback of MVCT is its very long imaging time, the result of slow couch speeds of approximately 1 mm/s, which can be difficult for the patient to tolerate. We sought to develop an MVCT imaging method allowing faster couch speeds and to assess its accuracy for image guidance for HT.

Methods And Materials: Three cadavers were scanned 4 times with couch speeds of 1, 2, 3, and 4 mm/s. The resulting MVCT images were reconstructed using an iterative reconstruction (IR) algorithm with a penalty term of total variation and with a conventional filtered back projection (FBP) algorithm. The MVCT images were registered with kilovoltage CT images, and the registration errors from the 2 reconstruction algorithms were compared. This fast MVCT imaging was tested in 3 cases of total marrow irradiation as a clinical trial.

Results: The 3-D registration errors of the MVCT images reconstructed with the IR algorithm were smaller than the errors of images reconstructed with the FBP algorithm at fast couch speeds (2, 3, 4 mm/s). The scan time and imaging dose at a speed of 4 mm/s were reduced to 30% of those from a conventional coarse mode scan. For the patient imaging, faster MVCT (3 mm/s couch speed) scanning reduced the imaging time and still generated images useful for anatomic registration.

Conclusions: Fast MVCT with the IR algorithm is clinically feasible for large 3-D target localization, which may reduce the overall time for the treatment procedure. This technique may also be useful for calculating daily dose distributions or organ motion analyses in HT treatment over a wide area. Automated integration of this imaging is at least needed to further assess its clinical benefits.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5081222PMC
http://dx.doi.org/10.1016/j.ijrobp.2016.06.2458DOI Listing
November 2016

Evaluation of Functional Marrow Irradiation Based on Skeletal Marrow Composition Obtained Using Dual-Energy Computed Tomography.

Int J Radiat Oncol Biol Phys 2016 11 6;96(3):679-87. Epub 2016 Jul 6.

Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota; Department of Therapeutic Radiology, University of Minnesota, Minneapolis, Minnesota; Department of Radiation Oncology, Beckman Research Institute, City of Hope, Duarte, California. Electronic address:

Purpose: To develop an imaging method to characterize and map marrow composition in the entire skeletal system, and to simulate differential targeted marrow irradiation based on marrow composition.

Methods And Materials: Whole-body dual energy computed tomography (DECT) images of cadavers and leukemia patients were acquired, segmented to separate bone marrow components, namely, bone, red marrow (RM), and yellow marrow (YM). DECT-derived marrow fat fraction was validated using histology of lumbar vertebrae obtained from cadavers. The fractions of RM (RMF = RM/total marrow) and YMF were calculated in each skeletal region to assess the correlation of marrow composition with sites and ages. Treatment planning was simulated to target irradiation differentially at a higher dose (18 Gy) to either RM or YM and a lower dose (12 Gy) to the rest of the skeleton.

Results: A significant correlation between fat fractions obtained from DECT and cadaver histology samples was observed (r=0.861, P<.0001, Pearson). The RMF decreased in the head, neck, and chest was significantly inversely correlated with age but did not show any significant age-related changes in the abdomen and pelvis regions. Conformity of radiation to targets (RM, YM) was significantly dependent on skeletal sites. The radiation exposure was significantly reduced (P<.05, t test) to organs at risk (OARs) in RM and YM irradiation compared with standard total marrow irradiation (TMI).

Conclusions: Whole-body DECT offers a new imaging technique to visualize and measure skeletal-wide marrow composition. The DECT-based treatment planning offers volumetric and site-specific precise radiation dosimetry of RM and YM, which varies with aging. Our proposed method could be used as a functional compartment of TMI for further targeted radiation to specific bone marrow environment, dose escalation, reduction of doses to OARs, or a combination of these factors.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5081224PMC
http://dx.doi.org/10.1016/j.ijrobp.2016.06.2459DOI Listing
November 2016

Characterization of deformation and physical force in uniform low contrast anatomy and its impact on accuracy of deformable image registration.

Med Phys 2016 Jan;43(1):52

Department of Radiation Oncology, Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455.

Purpose: Little is known about the effect of force on organ deformation and consequently its impact on precision dose delivery. The purpose of this study was to evaluate the fundamental relationship between anatomic deformation and its causative physical force to ascertain if a threshold limit exists for deformable image registration (DIR) accuracy in uniform low contrast anatomy, beyond which its applicability may be clinically inappropriate.

Methods: To simulate a simplified model, a tissue equivalent deformable bladder phantom with 21 implanted fiducial markers was developed using a viscoelastic polymer. The bladder phantom was deformed by applying a force in increments from 10 to 70 N. DIR accuracy was studied using intensity based mim and Velocity B-spline algorithms by comparing the 3D vector of the 21 marker locations at the original target image with the synthetically derived marker positions from each target image obtained from DIR.

Results: The relationship between applied force in 1D deformation along the axis of applied force and 3D deformation of the phantom showed a linear response. The maximum and average displacements of markers exhibited a nonlinear response to the applied force. In the absence of implanted markers, DIR performance was suboptimal with a threshold limit of only 20 N (5 mm deformation) beyond which the average marker error was ≥3 mm. DIR performance improved significantly with the addition of only one marker for the intensity based mim algorithm. In contrast, the Velocity B-spline algorithm showed reduced sensitivity to the number of markers introduced in both the source and target images.

Conclusions: The limits of applicability of DIR are strongly dependent on the magnitude of deformation. There is a threshold limit beyond which the accuracy of DIR fails in uniform low contrast anatomy. The sensitivity of the DIR performance to the number of fiducial markers present indicates that if DIR performance is solely assessed with the contrast rich features present in clinical anatomy, the results may not be reflective of the true DIR performance in uniform low contrast anatomy.
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http://dx.doi.org/10.1118/1.4937935DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4684569PMC
January 2016

Outcome Prediction after Radiotherapy with Medical Big Data.

Authors:
Taiki Magome

Igaku Butsuri 2016 ;36(1):39-41

Department of Radiological Sciences, Faculty of Health Sciences, Komazawa University.

Data science is becoming more important in many fields. In medical physics field, we are facing huge data every day. Treatment outcomes after radiation therapy are determined by complex interactions between clinical, biological, and dosimetrical factors. A key concept of recent radiation oncology research is to predict the outcome based on medical big data for personalized medicine. Here, some reports, which are analyzing medical databases with machine learning techniques, were reviewed and feasibility of outcome prediction after radiation therapy was discussed. In addition, some strategies for saving manual labors to analyze huge data in medical physics were discussed.
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http://dx.doi.org/10.11323/jjmp.36.1_39DOI Listing
May 2017

Reconstruction of the treatment area by use of sinogram in helical tomotherapy.

Radiat Oncol 2014 Nov 28;9:252. Epub 2014 Nov 28.

Department of Radiology, University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo, Tokyo, Japan.

Background: TomoTherapy (Accuray, USA) has an image-guided radiotherapy system with a megavoltage (MV) X-ray source and an on-board imaging device. This system allows one to acquire the delivery sinogram during the actual treatment, which partly includes information from the irradiated object. In this study, we try to develop image reconstruction during treatment with helical tomotherapy.

Findings: Sinogram data were acquired during helical tomotherapy delivery using an arc-shaped detector array that consists of 576 xenon-gas filled detector cells. In preprocessing, these were normalized with full air-scan data. A software program was developed that reconstructs 3D images during treatment with corrections as; (1) the regions outside the field were masked not to be added in the backprojection (a masking correction), and (2) each voxel of the reconstructed image was divided by the number of the beamlets passing through its voxel (a ray-passing correction). The masking correction produced a reconstructed image, however, it contained streak artifacts. The ray-passing correction reduced this artifact. Although the SNR (the ratio of mean to standard deviation in a homogeneous region) and the contrast of the reconstructed image were slightly improved with the ray-passing correction, use of only the masking correction was sufficient for the visualization purpose.

Conclusions: The visualization of the treatment area was feasible by using the sinogram in helical tomotherapy. This proposed method would be useful in the treatment verification.
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http://dx.doi.org/10.1186/s13014-014-0252-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255647PMC
November 2014

Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method.

J Radiat Res 2014 Nov 30;55(6):1153-62. Epub 2014 Jun 30.

Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.

To assist radiation oncologists in the delineation of tumor regions during treatment planning for lung cancer, we have proposed an automated contouring algorithm based on an optimum contour selection (OCS) method for treatment planning computed tomography (CT) images with positron emission tomography (PET)/CT images. The basic concept of the OCS is to select a global optimum object contour based on multiple active delineations with a level set method around tumors. First, the PET images were registered to the planning CT images by using affine transformation matrices. The initial gross tumor volume (GTV) of each lung tumor was identified by thresholding the PET image at a certain standardized uptake value, and then each initial GTV location was corrected in the region of interest of the planning CT image. Finally, the contours of final GTV regions were determined in the planning CT images by using the OCS. The proposed method was evaluated by testing six cases with a Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs contoured by radiation oncologists and the proposed method. The average three-dimensional DSC for the six cases was 0.78 by the proposed method, but only 0.34 by a conventional method based on a simple level set method. The proposed method may be helpful for treatment planners in contouring the GTV regions.
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http://dx.doi.org/10.1093/jrr/rru056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229921PMC
November 2014

Independent absorbed-dose calculation using the Monte Carlo algorithm in volumetric modulated arc therapy.

Radiat Oncol 2014 Mar 14;9:75. Epub 2014 Mar 14.

Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.

Purpose: To report the result of independent absorbed-dose calculations based on a Monte Carlo (MC) algorithm in volumetric modulated arc therapy (VMAT) for various treatment sites.

Methods And Materials: All treatment plans were created by the superposition/convolution (SC) algorithm of SmartArc (Pinnacle V9.2, Philips). The beam information was converted into the format of the Monaco V3.3 (Elekta), which uses the X-ray voxel-based MC (XVMC) algorithm. The dose distribution was independently recalculated in the Monaco. The dose for the planning target volume (PTV) and the organ at risk (OAR) were analyzed via comparisons with those of the treatment plan.Before performing an independent absorbed-dose calculation, the validation was conducted via irradiation from 3 different gantry angles with a 10- × 10-cm2 field. For the independent absorbed-dose calculation, 15 patients with cancer (prostate, 5; lung, 5; head and neck, 3; rectal, 1; and esophageal, 1) who were treated with single-arc VMAT were selected. To classify the cause of the dose difference between the Pinnacle and Monaco TPSs, their calculations were also compared with the measurement data.

Result: In validation, the dose in Pinnacle agreed with that in Monaco within 1.5%. The agreement in VMAT calculations between Pinnacle and Monaco using phantoms was exceptional; at the isocenter, the difference was less than 1.5% for all the patients. For independent absorbed-dose calculations, the agreement was also extremely good. For the mean dose for the PTV in particular, the agreement was within 2.0% in all the patients; specifically, no large difference was observed for high-dose regions. Conversely, a significant difference was observed in the mean dose for the OAR. For patients with prostate cancer, the mean rectal dose calculated in Monaco was significantly smaller than that calculated in Pinnacle.

Conclusions: There was no remarkable difference between the SC and XVMC calculations in the high-dose regions. The difference observed in the low-dose regions may have arisen from various causes such as the intrinsic dose deviation in the MC calculation, modeling accuracy, and CT-to-density table used in each planning system It is useful to perform independent absorbed-dose calculations with the MC algorithm in intensity-modulated radiation therapy commissioning.
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http://dx.doi.org/10.1186/1748-717X-9-75DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3995553PMC
March 2014

Similar-case-based optimization of beam arrangements in stereotactic body radiotherapy for assisting treatment planners.

Biomed Res Int 2013 2;2013:309534. Epub 2013 Nov 2.

Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 8128582, Japan ; Department of Radiology, The University of Tokyo Hospital, Tokyo 1138655, Japan.

Objective: To develop a similar-case-based optimization method for beam arrangements in lung stereotactic body radiotherapy (SBRT) to assist treatment planners.

Methods: First, cases that are similar to an objective case were automatically selected based on geometrical features related to a planning target volume (PTV) location, PTV shape, lung size, and spinal cord position. Second, initial beam arrangements were determined by registration of similar cases with the objective case using a linear registration technique. Finally, beam directions of the objective case were locally optimized based on the cost function, which takes into account the radiation absorption in normal tissues and organs at risk. The proposed method was evaluated with 10 test cases and a treatment planning database including 81 cases, by using 11 planning evaluation indices such as tumor control probability and normal tissue complication probability (NTCP).

Results: The procedure for the local optimization of beam arrangements improved the quality of treatment plans with significant differences (P < 0.05) in the homogeneity index and conformity index for the PTV, V10, V20, mean dose, and NTCP for the lung.

Conclusion: The proposed method could be usable as a computer-aided treatment planning tool for the determination of beam arrangements in SBRT.
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http://dx.doi.org/10.1155/2013/309534DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835834PMC
June 2014

Automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of planning CT and FDG-PET/CT images.

Annu Int Conf IEEE Eng Med Biol Soc 2013 ;2013:2988-91

We have developed an automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of treatment planning computed tomography (CT) and 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/CT images. First, the PET images were registered with the treatment planning CT images through the diagnostic CT images of PET/CT. Second, six voxel-based features including voxel values and magnitudes of image gradient vectors were derived from each voxel in the planning CT and PET /CT image data sets. Finally, lung tumors were extracted by using a support vector machine (SVM), which learned 6 voxel-based features inside and outside each true tumor region determined by radiation oncologists. The results showed that the average DSCs for 3 and 6 features for three cases were 0.744 and 0.899, and thus the SVM may need 6 features to learn the distinguishable characteristics. The proposed method may be useful for assisting treatment planners in delineation of the tumor region.
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http://dx.doi.org/10.1109/EMBC.2013.6610168DOI Listing
June 2015

Computer-aided beam arrangement based on similar cases in radiation treatment-planning databases for stereotactic lung radiation therapy.

J Radiat Res 2013 May 18;54(3):569-77. Epub 2012 Dec 18.

Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.

The purpose of this study was to develop a computer-aided method for determination of beam arrangements based on similar cases in a radiotherapy treatment-planning database for stereotactic lung radiation therapy. Similar-case-based beam arrangements were automatically determined based on the following two steps. First, the five most similar cases were searched, based on geometrical features related to the location, size and shape of the planning target volume, lung and spinal cord. Second, five beam arrangements of an objective case were automatically determined by registering five similar cases with the objective case, with respect to lung regions, by means of a linear registration technique. For evaluation of the beam arrangements five treatment plans were manually created by applying the beam arrangements determined in the second step to the objective case. The most usable beam arrangement was selected by sorting the five treatment plans based on eight plan evaluation indices, including the D95, mean lung dose and spinal cord maximum dose. We applied the proposed method to 10 test cases, by using an RTP database of 81 cases with lung cancer, and compared the eight plan evaluation indices between the original treatment plan and the corresponding most usable similar-case-based treatment plan. As a result, the proposed method may provide usable beam arrangements, which have no statistically significant differences from the original beam arrangements (P > 0.05) in terms of the eight plan evaluation indices. Therefore, the proposed method could be employed as an educational tool for less experienced treatment planners.
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http://dx.doi.org/10.1093/jrr/rrs123DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3650748PMC
May 2013

Computerized estimation of patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy.

J Radiat Res 2012 Nov 26;53(6):961-72. Epub 2012 Jul 26.

Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Japan.

We have developed a computerized method for estimating patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy. The patient setup errors were estimated based on a template-matching technique that compared the portal image and a localized pelvic template image with a clinical target volume produced from a digitally reconstructed radiography (DRR) image of each patient. We evaluated the proposed method by calculating the residual error between the patient setup error obtained by the proposed method and the gold standard setup error determined by consensus between two radiation oncologists. Eleven training cases with prostate cancer were used for development of the proposed method, and then we applied the method to 10 test cases as a validation test. As a result, the residual errors in the anterior-posterior, superior-inferior and left-right directions were smaller than 2 mm for the validation test. The mean residual error was 2.65 ± 1.21 mm in the Euclidean distance for training cases, and 3.10 ± 1.49 mm for the validation test. There was no statistically significant difference in the residual error between the test for training cases and the validation test (P = 0.438). The proposed method appears to be robust for detecting patient setup error in the treatment of prostate cancer radiotherapy.
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http://dx.doi.org/10.1093/jrr/rrs043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483845PMC
November 2012

Automated detection of multiple sclerosis candidate regions in MR images: false-positive removal with use of an ANN-controlled level-set method.

Radiol Phys Technol 2012 Jan 3;5(1):105-13. Epub 2011 Dec 3.

Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.

Our purpose in this study was to develop an automated segmentation scheme for multiple sclerosis (MS) lesions in magnetic resonance images using an artificial neural network (ANN)-controlled level-set method. Forty-nine slices with T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images were selected from six examinations of three MS patients including 168 MS lesions for this study. First, MS lesions were enhanced by background subtraction. Initial regions of MS candidates were detected based on a multiple-gray-level thresholding technique and a region-growing technique on the subtraction image. Then, final regions of MS candidates were determined by application of a proposed segmentation method using an ANN-controlled level-set method, which was used for reduction of false positives (FPs) as well as more accurate segmentation. Finally, all candidate regions were classified into true positive and FP candidate regions by use of a support vector machine. As the result of a leave-one-candidate-out test method, the detection sensitivity for MS lesions increased from 64.9 to 75.0% while decreasing the number of FPs per slice from 19.9 to 4.4 compared with a previous study. The proposed scheme improved the sensitivity and the number of FPs in the detection of MS lesions.
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http://dx.doi.org/10.1007/s12194-011-0141-2DOI Listing
January 2012

[Medical imaging processing and evaluation in radiation therapy].

Nihon Hoshasen Gijutsu Gakkai Zasshi 2011 ;67(1):76-83

Faculty of Medical Sciences, Kyushu University.

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http://dx.doi.org/10.6009/jjrt.67.76DOI Listing
June 2011

Automated segmentation method of white matter and gray matter regions with multiple sclerosis lesions in MR images.

Radiol Phys Technol 2011 Jan 30;4(1):61-72. Epub 2010 Sep 30.

Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.

Our purpose in this study was to develop an automated method for segmentation of white matter (WM) and gray matter (GM) regions with multiple sclerosis (MS) lesions in magnetic resonance (MR) images. The brain parenchymal (BP) region was derived from a histogram analysis for a T1-weighted image. The WM regions were segmented by addition of MS candidate regions, which were detected by our computer-aided detection system for the MS lesions, and subtraction of a basal ganglia and thalamus template from "tentative" WM regions. The GM regions were obtained by subtraction of the WM regions from the BP region. We applied our proposed method to T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery images acquired from 7 MS patients and 7 control subjects on a 3.0 T MRI system. The average similarity indices between the specific regions obtained by our method and by neuroradiologists for the BP and WM regions were 95.5 ± 1.2 and 85.2 ± 4.3%, respectively, for MS patients. Moreover, they were 95.0 ± 2.0 and 85.9 ± 3.4%, respectively, for the control subjects. The proposed method might be feasible for segmentation of WM and GM regions in MS patients.
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http://dx.doi.org/10.1007/s12194-010-0106-xDOI Listing
January 2011

Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine.

Comput Med Imaging Graph 2010 Jul 26;34(5):404-13. Epub 2010 Feb 26.

Kyushu University, Fukuoka, Japan.

The purpose of this study was to develop a computerized method for detection of multiple sclerosis (MS) lesions in brain magnetic resonance (MR) images. We have proposed a new false positive reduction scheme, which consisted of a rule-based method, a level set method, and a support vector machine. We applied the proposed method to 49 slices selected from 6 studies of three MS cases including 168 MS lesions. As a result, the sensitivity for detection of MS lesions was 81.5% with 2.9 false positives per slice based on a leave-one-candidate-out test, and the similarity index between MS regions determined by the proposed method and neuroradiologists was 0.768 on average. These results indicate the proposed method would be useful for assisting neuroradiologists in assessing the MS in clinical practice.
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http://dx.doi.org/10.1016/j.compmedimag.2010.02.001DOI Listing
July 2010

Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging.

Comput Med Imaging Graph 2010 Jul 8;34(5):370-6. Epub 2010 Feb 8.

Kyushu University, Fukuoka, Japan.

It has been reported that the severity of subcortical vascular dementia (VaD) correlated with an area ratio of white matter hyperintensity (WMH) regions to the brain parenchyma (WMH area ratio). The purpose of this study was to develop a computer-aided evaluation method of WMH regions for diagnosis of subcortical VaD based on magnetic resonance (MR) images. A brain parenchymal region was segmented based on the histogram analysis of a T1-weigthed image. The WMH regions were segmented on the subtraction image between a T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images using two segmentation methods, i.e., a region-growing technique and a level-set method, which were automatically and adaptively selected on each WMH region based on its image features by using a support vector machine. We applied the proposed method to 33 slices of the three types of MR images with 245 lesions, which were acquired from 10 patients (age range: 64-90 years, mean: 78) with a diagnosis of VaD on a 1.5-T MR imaging scanner. The average similarity index between regions determined by a manual method and the proposed method was 93.5+/-2.0% for brain parenchymal regions and 78.2+/-11.0% for WMH regions. The WMH area ratio obtained by the proposed method correlated with that determined by two neuroradiologists with a correlation coefficient of 0.992. The results presented in this study suggest that the proposed method could assist neuroradiologists in the evaluation of WMH regions related to the subcortical VaD.
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http://dx.doi.org/10.1016/j.compmedimag.2009.12.014DOI Listing
July 2010
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