MRI-based automated detection of implanted low dose rate (LDR) brachytherapy seeds using quantitative susceptibility mapping (QSM) and unsupervised machine learning (ML)

Nosrati, Reyhaneh; Soliman, A; Safigholi, Habib; Hashemi, Masoud; Wronski, Matt; Morton, Gerard; Pejović‐Milić, Ana; Stanisz, Greg J.; Song, William Y.

doi:10.1016/j.radonc.2018.09.003

Cited by 27 publications

(42 citation statements)

References 57 publications

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“…The MR postprocessing pipeline included the following main steps: (1) seed-induced MR distortion correction and edge enhancement, [14][15][16] and (2) quantitative susceptibility mapping (QSM) based on morphology enabled dipole inversion with automated zero referencing. 13,[17][18][19] The prostate tissue and the obturator internus were considered as the reference tissues and were automatically segmented by thresholding the GRE magnitude images at 50% of the maximum. The details of the positive contrast seed visualization algorithm are shown in Figure 1 (seed visualization block).…”

Section: Mr-based Seed Visualizationmentioning

confidence: 99%

“…Recently Nosrati et al have proposed and validated an MRI-only workflow based solely on MR postprocessing algorithms, which generates high quality positive contrast for conventional brachytherapy seeds. 13 The proposed algorithm exploits the strong paramagnetic properties of the titanium seeds in contrast to the diamagnetic biological tissues (eg, prostate, calcifications, etc.) and use magnetic susceptibility to visualize seeds with positive contrast.…”

mentioning

confidence: 99%

“…and use magnetic susceptibility to visualize seeds with positive contrast. The present study aimed at comparing the MRI-only workflow proposed in 13 to the standard CT-MRI fusion-based approach in terms of seed visualization, detection, and dosimetric outcomes.…”

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confidence: 99%

See 2 more Smart Citations

Postimplant Dosimetry of Permanent Prostate Brachytherapy: Comparison of MRI-Only and CT-MRI Fusion-Based Workflows

Nosrati

Wronski

Tseng

et al. 2020

International Journal of Radiation Oncology*Biology*Physics

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Section: Mr-based Seed Visualizationmentioning

confidence: 99%

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confidence: 99%

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Postimplant Dosimetry of Permanent Prostate Brachytherapy: Comparison of MRI-Only and CT-MRI Fusion-Based Workflows

Nosrati

Wronski

Tseng

et al. 2020

International Journal of Radiation Oncology*Biology*Physics

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“…Balanced steady‐state free precession (bSSFP) pulse sequences can improve MRI quality for seed identification and susceptibility‐based pulse sequences have been developed to provide positive contrast with conventional seeds . Susceptibility‐based pulse sequences have recently been combined with machine learning‐based automatic seed localization enabling all implanted seeds to be localized within 0.7 mm error in phantoms . Preliminary evidence suggests that this performance may translate to patients; however, to the authors’ knowledge, robust MRI‐only seed localization has not been validated in patients and may require hardware and software upgrades for implementation in many clinics.…”

Section: Introductionmentioning

confidence: 99%

“…18,19 Susceptibility-based pulse sequences have recently been combined with machine learning-based automatic seed localization enabling all implanted seeds to be localized within 0.7 mm error in phantoms. 20 Preliminary evidence suggests that this performance may translate to patients; 21 however, to the authors' knowledge, robust MRI-only seed localization has not been validated in patients and may require hardware and software upgrades for implementation in many clinics. The second technique involves the combination of projection x rays and MRI for seed localization acquired sequentially using a custom imaging facility (XMR).…”

Section: Introductionmentioning

confidence: 99%

Deformable registration of x ray and MRI for postimplant dosimetry in low dose rate prostate brachytherapy

Hrinivich

Park

et al. 2019

Medical Physics

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Purpose Dosimetric assessment following permanent prostate brachytherapy (PPB) commonly involves seed localization using CT and prostate delineation using coregistered MRI. However, pelvic CT leads to additional imaging dose and requires significant resources to acquire and process both CT and MRI. In this study, we propose an automatic postimplant dosimetry approach that retains MRI for soft‐tissue contouring, but eliminates the need for CT and reduces imaging dose while overcoming the inconsistent appearance of seeds on MRI with three projection x rays acquired using a mobile C‐arm. Methods Implanted seeds are reconstructed using x rays by solving a combinatorial optimization problem and deformably registered to MRI. Candidate seeds are located in MR images using local hypointensity identification. X ray‐based seeds are registered to these candidate seeds in three steps: (a) rigid registration using a stochastic evolutionary optimizer, (b) affine registration using an iterative closest point optimizer, and (c) deformable registration using a local feature point search and nonrigid coherent point drift. The algorithm was evaluated using 20 PPB patients with x rays acquired immediately postimplant and T2‐weighted MR images acquired the next day at 1.5 T with mean 0.8 × 0.8 × 3.0 mm3 voxel dimensions. Target registration error (TRE) was computed based on the distance from algorithm results to manually identified seed locations using coregistered CT acquired the same day as the MRI. Dosimetric accuracy was determined by comparing prostate D90 determined using the algorithm and the ground truth CT‐based seed locations. Results The mean ± standard deviation TREs across 20 patients including 1774 seeds were 2.23 ± 0.52 mm (rigid), 1.99 ± 0.49 mm (rigid + affine), and 1.76 ± 0.43 mm (rigid + affine + deformable). The corresponding mean ± standard deviation D90 errors were 5.8 ± 4.8%, 3.4 ± 3.4%, and 2.3 ± 1.9%, respectively. The mean computation time of the registration algorithm was 6.1 s. Conclusion The registration algorithm accuracy and computation time are sufficient for clinical PPB postimplant dosimetry.

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Delineation of prostatic calcification using quantitative susceptibility mapping: Spatial accuracy for magnetic resonance‐only radiotherapy planning

Kan

Yamada

Kunitomo

et al. 2021

J Applied Clin Med Phys

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To investigate the spatial accuracy of delineating prostatic calcifications by quantitative susceptibility mapping (QSM) in comparison with computed tomography (CT), we conducted phantom and human studies. Five differently‐sized spherical hydroxyapatites mimicking prostatic calcification (pseudo‐calcification) were arranged in the order of their sizes at the center of a plastic container filled with gelatin. This calcification phantom underwent magnetic resonance (MR) imaging, including the multiple spoiled gradient‐echo sequences (SPGR) for the QSM and CT as a reference. The volume of each pseudo‐calcification and center‐to‐center distance between the pseudo‐calcifications delineated by QSM and CT were measured. In the human study, eight patients with prostate cancer who underwent radiation therapy and had some prostatic calcifications were included. The patients underwent CT and SPGR and modified DIXON sequence for MR‐only simulation. The hybrid QSM processing combined with the complex signals in the SPGR and water and fat fraction maps estimated from the modified DIXON sequence were used to reconstruct the pelvic susceptibility map in humans. The threshold of CT numbers was set at 130 HU, while the QSM images were manually segmented in the calcification phantom and human studies. In the phantom study, there was an excellent agreement in the pseudo‐calcification volumes between QSM and CT (y = 1.02x – 7.38, R2 = 0.99). The signal profiles had similar trends in CT and QSM. The center‐to‐center distances between the pseudo‐calcifications in the phantom were also identical in QSM and CT. The calcification volumes were almost identical between the QSM and CT in the human study (y = 0.95x – 9.32, R2 = 1.00). QSM can offer geometric and volumetric accuracies to delineate prostatic calcifications, similar to CT. The prostatic calcification delineated by QSM may facilitate image‐guided radiotherapy in the MR‐only simulation workflow.

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MRI-based automated detection of implanted low dose rate (LDR) brachytherapy seeds using quantitative susceptibility mapping (QSM) and unsupervised machine learning (ML)

Cited by 27 publications

References 57 publications

Postimplant Dosimetry of Permanent Prostate Brachytherapy: Comparison of MRI-Only and CT-MRI Fusion-Based Workflows

Postimplant Dosimetry of Permanent Prostate Brachytherapy: Comparison of MRI-Only and CT-MRI Fusion-Based Workflows

Deformable registration of x ray and MRI for postimplant dosimetry in low dose rate prostate brachytherapy

Delineation of prostatic calcification using quantitative susceptibility mapping: Spatial accuracy for magnetic resonance‐only radiotherapy planning

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