EM-enhanced US-based seed detection for prostate brachytherapy

Transrectal ultrasound for intraoperative interstitial needle guidance in cervical cancer brachytherapy

OBJECTIVE

This study aimed to prospectively assess the visibility of interstitial needles on transrectal ultrasound (TRUS) in cervical cancer brachytherapy patients and evaluate its impact on implant and treatment plan quality.

MATERIAL AND METHODS

TRUS was utilized during and after applicator insertion, with each needle's visibility documented through axial images at the high-risk clinical target volume's largest diameter. Needle visibility on TRUS was scored from 0 (no visibility) to 3 (excellent discrimination, margins distinct). Quantitative assessment involved measuring the distance between tandem and each needle on TRUS and comparing it to respective magnetic resonance imaging (MRI) measurements. Expected treatment plan quality based on TRUS images was rated from 1 (meeting all planning objectives) to 4 (violation of High-risk clinical target volume (CTVHR) and/or organ at risk (OAR) hard constraints) and compared to the final MRI-based plan.

RESULTS

Analysis included 23 patients with local FIGO stage IB2-IVA, comprising 41 applications with a total of 230 needles. A high visibility rate of 99.1% (228/230 needles) was observed, with a mean visibility score of 2.5 ± 0.7 for visible needles. The maximum and mean difference between MRI and TRUS measurements were 8 mm and -0.1 ± 1.6 mm, respectively, with > 3 mm discrepancies in 3.5% of needles. Expected treatment plan quality after TRUS assessment exactly aligned with the final MRI plan in 28 out of 41 applications with only minor deviations in all other cases.

CONCLUSION

Real-time TRUS-guided interstitial needle placement yielded high-quality implants, thanks to excellent needle visibility during insertion. This supports the potential of TRUS-guided brachytherapy as a promising modality for gynecological indications.

Toward 3D-TRUS image-guided interstitial brachytherapy for cervical cancer

PURPOSE

To qualitatively and quantitatively analyze needle visibility in combined intracavitary and interstitial cervical cancer brachytherapy on 3D transrectal ultrasound (TRUS) in comparison to gold standard MRI.

METHODS AND MATERIALS

Image acquisition was done with a customized TRUS stepper unit and software (Medcom, Germany; Elekta, Sweden; ACMIT, Austria) followed by an MRI on the same day with the applicator in place. Qualitative assessment was done with following scoring system: 0 = no visibility 1 (= poor), 2 (= fair), 3 (= excellent) discrimination, quantitative assessment was done by measuring the distance between each needle and the tandem two centimeters (cm) above the ring and comparing to the respective measurement on MRI.

RESULTS

Twenty-nine implants and a total of 188 needles (132 straight, 35 oblique, 21 free-hand) were available. Overall, 79% were visible (87% straight, 51% oblique, 76% free-hand). Mean visibility score was 1.4 ± 0.5 for all visible needles. Distance of the visible needles to tandem was mean ± standard deviation (SD) 21.3 millimeters (mm) ± 6.5 mm on MRI and 21.0 mm ± 6.4 mm on TRUS, respectively. Difference between MRI and TRUS was max 14 mm, mean ± SD -0.3 mm ± 2.6 mm. 11% differed more than 3 mm.

CONCLUSIONS

Straight needles were better detectable than oblique needles (87% vs. 51%). Detectability was impaired by insufficient rotation of the TRUS probe, poor image quality or anatomic variation. As needles show a rather indistinct signal on TRUS, online detection with a standardized imaging protocol in combination with tracking should be investigated, aiming at the development of real time image guidance and online treatment planning.

A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy

Purpose

Low-dose-rate brachytherapy is a key treatment for low-risk or favorable intermediate-risk prostate cancer. The number of radioactive seeds inserted during the procedure depends on prostate volume, and is not easy to predict without pre-planning. Consequently, a large number of unused seeds may be left after treatment. The objective of the present study was to predict the exact number of seeds for future patients using machine learning and a database of 409 treatments.

Material and methods

Database consisted of 18 dosimetric and efficiency parameters for each of 409 cases. Nine predictive algorithms based on machine-learning were compared in this database, which was divided into training group (80%) and test group (20%). Ten-fold cross-validation was applied to obtain robust statistics. The best algorithm was then used to build an abacus able to predict number of implanted seeds from expected prostate volume only. As an evaluation, the abacus was also applied on an independent series of 38 consecutive patients.

Results

The best coefficients of determination R² were given by support vector regression, with values attaining 0.928, 0.948, and 0.968 for training set, test set, and whole set, respectively. In terms of predicted seeds in test group, mean square error, median absolute error, mean absolute error, and maximum error were 2.55, 0.92, 1.21, and 7.29, respectively. The use of obtained abacus in 38 additional patients resulted in saving of 493 seeds (393 vs. 886 remaining seeds).

Conclusions

Machine-learning-based abacus proposed in this study aims at estimating the necessary number of seeds for future patients according to past experience. This new abacus, based on 409 treatments and successfully tested in 38 new patients, is a good alternative to non-specific recommendations.

A review of brachytherapy physical phantoms developed over the last 20 years: clinical purpose and future requirements

Within the brachytherapy community, many phantoms are constructed in-house, and less commercial development is observed as compared to the field of external beam. Computational or virtual phantom design has seen considerable growth; however, physical phantoms are beneficial for brachytherapy, in which quality is dependent on physical processes, such as accuracy of source placement. Focusing on the design of physical phantoms, this review paper presents a summary of brachytherapy specific phantoms in published journal articles over the last twenty years (January 1, 2000 - December 31, 2019). The papers were analyzed and tabulated by their primary clinical purpose, which was deduced from their associated publications. A substantial body of work has been published on phantom designs from the brachytherapy community, but a standardized method of reporting technical aspects of the phantoms is lacking. In-house phantom development demonstrates an increasing interest in magnetic resonance (MR) tissue mimicking materials, which is not yet reflected in commercial phantoms available for brachytherapy. The evaluation of phantom design provides insight into the way, in which brachytherapy practice has changed over time, and demonstrates the customised and broad nature of treatments offered.

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy

PURPOSE

New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position and orientation of the implanted radioactive seeds from real-time ultrasound (US) images. This is necessary to compare them to the planned one and to adjust automatically the dosimetric plan accordingly for next seeds implantations. The image modality, the small size of the seeds, and the artifacts they produce make it a very challenging problem. The objective of the presented work is to setup and to evaluate a robust and automatic method for seed localization in three-dimensional (3D) US images.

METHODS

The presented method is based on a prelocalization of the needles through which the seeds are injected in the prostate. This prelocalization allows focusing the search on a region of interest (ROI) around the needle tip. Seeds localization starts by binarizing the ROI and removing false positives using, respectively, a Bayesian classifier and a support vector machine (SVM). This is followed by a registration stage using first an iterative closest point (ICP) for localizing the connected set of seeds (named strand) inserted through a needle, and secondly refining each seed position using sum of squared differences (SSD) as a similarity criterion. ICP registers a geometric model of the strand to the candidate voxels while SSD compares an appearance model of a single seed to a subset of the image. The method was evaluated both for 3D images of an Agar-agar phantom and a dataset of clinical 3D images. It was tested on stranded and on loose seeds.

RESULTS

Results on phantom and clinical images were compared with a manual localization giving mean errors of 1.09 ± 0.61 mm on phantom image and 1.44 ± 0.45 mm on clinical images. On clinical images, the mean errors of individual seeds orientation was 4.33 ± 8 . 51 ∘ .

CONCLUSIONS

The proposed algorithm for radioactive seed localization is robust, tested on different US images, accurate, giving small mean error values, and returns the five cylindrical seeds degrees of freedom.

BrachyView: Reconstruction of seed positions and volume of an LDR prostate brachytherapy patient plan using a baseline subtraction algorithm

PURPOSE

BrachyView is a novel in-body imaging system developed with the objective to provide real-time intraoperative dosimetry for low dose rate (LDR) prostate brachytherapy treatments. The BrachyView coordinates combined with conventional transrectal ultrasound (TRUS) imaging, provides the possibility to localise the effective position of the implanted seeds inside the prostate volume, providing a unique tool for intra-operative verification of the quality of the implantation. This research presents the first complete LDR brachytherapy plan reconstructed by the BrachyView system and is used to evaluate the effectiveness of an imaging algorithm with baseline subtraction.

METHODS

A plan featuring 98 I-125 brachytherapy seeds, with an average activity of 0.248 mCi, were implanted into a prostate gel phantom under TRUS guidance. Images of implanted seeds were obtained by the BrachyView after the implantation of seeds. The baseline subtraction algorithm is applied as a pixel-to-pixel counts subtraction and is applied to every second projection obtained after the implantation of each needle. Seed positions and effectiveness of the baseline reconstruction in the identification of seeds were verified by a high-resolution post-implant CT scan.

RESULTS

A complete brachytherapy plan has been reconstructed with a 100% detection rate. This is possible due to the effectiveness of the baseline subtraction, with its application an overall increase of 11.3% in position accuracy and 8.2% increase in detection rate was noted.

CONCLUSION

It has been demonstrated that the BrachyView system shows the potential to be a solution to providing clinics with the means for intraoperative dosimetry for LDR prostate brachytherapy treatments.