MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

Ghose, Soumya; Mitra, Jhimli; Rivest-Hénault, David; Fazlollahi, Amir; Stanwell, Peter; Pichler, Peter-Paul; Sun, Jidi; Fripp, Jürgen; Greer, Peter B.; Dowling, Jason

doi:10.1118/1.4944871

Cited by 40 publications

(76 citation statements)

References 39 publications

(54 reference statements)

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“…With respect to the difference in slice thickness used for the image acquisition in previous studies, our results for the GFMs spatial position were similar. 37,40 The TPR of our method was determined to be 84% which was larger than the TPR for the bTFE alone but smaller than the TPR determined for multiparametric MRI. 40 The detection performance results from the human observatory study showed that MEGRE images could be a valid approach for identifying GFMs.…”

Section: Discussionmentioning

confidence: 57%

“…37,40 One of these studies used Medical Physics, 44 (11), November 2017 GRE-based MRI sequences to produce T1-and T2*-weighted images. The sensitivity, specificity, and accuracy for the T1-weighted images were 0.84, 1, and 0.88 and for the T2*-weighted images, it were 0.55, 0.86, and 0.63.…”

Section: Discussionmentioning

confidence: 99%

“…The model accuracy was determined to be 0.5 AE 0.5 mm for both image types. 37 Another study using multiparametric MRI information yielded a TPR of 0.95. The balanced steady-state free precession sequence (bTFE) was recommended to improve the GFM detection and performed alone a TPR of 0.77.…”

Section: Discussionmentioning

confidence: 99%

“…The mean absolute difference in the internal distances of the true-positive GFMs between CT and MRI determined in the observatory study was similar to the results of previous manual detection studies reporting 0.6 AE 0.4 and 0.6 AE 0.6 mm. 25,37 Multiecho gradient echo with sum of squares echo combination for echo averaging has previously been shown to enhance detection of 125-Iodine seeds and GFM. It was concluded that multiple echoes for GRE provided better detection performance than both conventional FSE and single echo GRE.…”

Section: Discussionmentioning

confidence: 99%

“…The authors also concluded the need for improved differentiation between GFMs and intraprostatic calcifications. 37 Another method for automatic GFM identification were recently proposed utilizing a machine learning pattern recognition framework using image information from a Medical Physics, 44 (11), November 2017 multiparametric acquisition protocol containing five different MRI series. About 81% of the patients (n = 32) had all markers correctly identified.…”

Section: Introductionmentioning

confidence: 99%

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Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy

et al. 2017

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Purpose: The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration. Methods: T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancer patients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images. Results: The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 AE 1.25 mm and 1.14 AE 1.06 mm, respectively. Conclusions: Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.

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Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy

et al. 2017

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Using C‐Arm X‐ray images from marker insertion to confirm the gold fiducial marker identification in an MRI‐only prostate radiotherapy workflow

Gustafsson

Persson

Gunnlaugsson

et al. 2018

J Applied Clin Med Phys

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Prostate cancer radiotherapy workflows, solely based on magnetic resonance imaging (MRI), are now in clinical use. In these workflows, intraprostatic gold fiducial markers (GFM) show similar signal behavior as calcifications and bleeding in T2‐weighted MRI‐images. Accurate GFM identification in MRI‐only radiotherapy workflows is therefore a major challenge. C‐arm X‐ray images (CkV‐images), acquired at GFM implantation, could provide GFM position information and be used to confirm correct identification in T2‐weighted MRI‐images. This would require negligible GFM migration between implantation and MRI‐imaging. Marker migration was therefore investigated. The aim of this study was to show the feasibility of using CkV‐images to confirm GFM identification in an MRI‐only prostate radiotherapy workflow. An anterior‐posterior digitally reconstructed radiograph (DRR)‐image and a mirrored posterior‐anterior CkV‐image were acquired two weeks apart for 16 patients in an MRI‐only radiotherapy workflow. The DRR‐image originated from synthetic CT‐images (created from MRI‐images). A common image geometry was defined between the DRR‐ and CkV‐image for each patient. A rigid registration between the GFM center of mass (CoM) coordinates was performed and the distance between each of the GFM in the DRR‐ and registered CkV‐image was calculated. The same methodology was used to assess GFM migration for 31 patients in a CT‐based radiotherapy workflow. The distance calculated was considered a measure of GFM migration. A statistical test was performed to assess any difference between the cohorts. The mean absolute distance difference for the GFM CoM between the DRR‐ and CkV‐image in the MRI‐only cohort was 1.7 ± 1.4 mm. The mean GFM migration was 1.2 ± 0.7 mm. No significant difference between the measured total distances of the two cohorts could be detected (P = 0.37). This demonstrated that, a C‐Arm X‐ray image acquired from the GFM implantation procedure could be used to confirm GFM identification from MRI‐images. GFM migration was present but did not constitute a problem.

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Improving fiducial and prostate capsule visualization for radiotherapy planning using MRI

Pathmanathan

Schmidt

Brand

et al. 2019

J Applied Clin Med Phys

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Background and purpose Intraprostatic fiducial markers (FM) improve the accuracy of radiotherapy (RT) delivery. Here we assess geometric integrity and contouring consistency using a T2*‐weighted (T2*W) sequence alone, which allows visualization of the FM. Material and methods Ten patients scanned within the Prostate Advances in Comparative Evidence (PACE) trial (NCT01584258) had prostate images acquired with computed tomography (CT) and Magnetic Resonance (MR) Imaging: T2‐weighted (T2W) and T2*W sequences. The prostate was contoured independently on each imaging dataset by three clinicians. Interobserver variability was assessed using comparison indices with Monaco ADMIRE (research version 2.0, Elekta AB) and examined for statistical differences between imaging sets. CT and MR images of two test objects were acquired to assess geometric distortion and accuracy of marker positioning. The first was a linear test object comprising straight tubes in three orthogonal directions, the second was a smaller test object with markers suspended in gel. Results Interobserver variability for prostate contouring was lower for both T2W and T2*W compared to CT, this was statistically significant when comparing CT and T2*W images. All markers are visible in T2*W images with 29/30 correctly identified, only 3/30 are visible in T2W images. Assessment of geometric distortion revealed in‐plane displacements were under 0.375 mm in MRI, and through plane displacements could not be detected. The signal loss in the MR images is symmetric in relation to the true marker position shown in CT images. Conclusion Prostate T2*W images are geometrically accurate, and yield consistent prostate contours. This single sequence can be used to identify FM and for prostate delineation in a mixed MR‐CT workflow.

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MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

Cited by 40 publications

References 39 publications

Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy

Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy

Using C‐Arm X‐ray images from marker insertion to confirm the gold fiducial marker identification in an MRI‐only prostate radiotherapy workflow

Improving fiducial and prostate capsule visualization for radiotherapy planning using MRI

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