Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

2D/3D Deep Registration Along Trajectories With Spatiotemporal Context: Application to Prostate Biopsy Navigation

OBJECTIVE

The accuracy of biopsy targeting is a major issue for prostate cancer diagnosis and therapy. However, navigation to biopsy targets remains challenging due to the limitations of transrectal ultrasound (TRUS) guidance added to prostate motion issues. This article describes a rigid 2D/3D deep registration method, which provides a continuous tracking of the biopsy location w.r.t the prostate for enhanced navigation.

METHODS

A spatiotemporal registration network (SpT-Net) is proposed to localize the live 2D US image relatively to a previously aquired US reference volume. The temporal context relies on prior trajectory information based on previous registration results and probe tracking. Different forms of spatial context were compared through inputs (local, partial or global) or using an additional spatial penalty term. The proposed 3D CNN architecture with all combinations of spatial and temporal context was evaluated in an ablation study. For providing a realistic clinical validation, a cumulative error was computed through series of registrations along trajectories, simulating a complete clinical navigation procedure. We also proposed two dataset generation processes with increasing levels of registration complexity and clinical realism.

RESULTS

The experiments show that a model using local spatial information combined with temporal information performs better than more complex spatiotemporal combination.

CONCLUSION

The best proposed model demonstrates robust real-time 2D/3D US cumulated registration performance on trajectories. Those results respect clinical requirements, application feasibility, and they outperform similar state-of-the-art methods.

SIGNIFICANCE

Our approach seems promising for clinical prostate biopsy navigation assistance or other US image-guided procedure.

Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy

Radiotherapy is a widely used treatment modality for various types of cancers. A challenge for precise delivery of radiation to the treatment site is the management of internal motion caused by the patient's breathing, especially around abdominal organs such as the liver. Current image-guided radiation therapy (IGRT) solutions rely on ionising imaging modalities such as X-ray or CBCT, which do not allow real-time target tracking. Ultrasound imaging (US) on the other hand is relatively inexpensive, portable and non-ionising. Although 2D US can be acquired at a sufficient temporal frequency, it doesn't allow for target tracking in multiple planes, while 3D US acquisitions are not adapted for real-time. In this work, a novel deep learning-based motion modelling framework is presented for ultrasound IGRT. Our solution includes an image similarity-based rigid alignment module combined with a deep deformable motion model. Leveraging the representational capabilities of convolutional autoencoders, our deformable motion model associates complex 3D deformations with 2D surrogate US images through a common learned low dimensional representation. The model is trained on a variety of deformations and anatomies which enables it to generate the 3D motion experienced by the liver of a previously unseen subject. During inference, our framework only requires two pre-treatment 3D volumes of the liver at extreme breathing phases and a live 2D surrogate image representing the current state of the organ. In this study, the presented model is evaluated on a 3D+t US data set of 20 volunteers based on image similarity as well as anatomical target tracking performance. We report results that surpass comparable methodologies in both metric categories with a mean tracking error of 3.5±2.4 mm, demonstrating the potential of this technique for IGRT.

Towards real-time free-hand biopsy navigation

PURPOSE

Performing a transrectal ultrasound (TRUS) prostate biopsy is at the heart of the current prostate cancer detection procedure. With today's two-dimensional (2D) live ultrasound (US) imaging equipment, this task remains complex due to the poor visibility of cancerous tissue on TRUS images and the limited anatomical context available in the 2D TRUS plane. This paper presents a rigid 2D/3DUS registration method for navigated prostate biopsy. This allows continuous localization of the biopsy trajectory during the procedure.

METHODS

We proposed an organ-based approach to achieve real-time rigid registration without the need for any probe localization device. The registration method combines image similarity and geometric proximity of detected features. Additions to our previous work include a multi-level approach and the use of a rejection rate favouring the best matches. Their aim is to increase the accuracy and time performances. These modifications and their in-depth evaluation on real clinical cases and comparison to this previous work are described. We performed static and dynamic evaluations along biopsy trajectories on a very large amount of data acquired under uncontrolled routine conditions. The computed transforms are compared to a ground truth obtained either from corresponding manually detected fiducials or from an already evaluated registration method.

RESULTS

All results show that the current method outperforms its previous version, both in terms of accuracy (the average error reported here is 12 to 17% smaller depending on the experiment) and processing time (from 20 to 60 times faster compared to the previous implementation). The dynamic registration experiment demonstrates that the method can be successfully used for continuous tracking of the biopsy location w.r.t the prostate at a rate that varies between 5 and 15 Hz.

CONCLUSIONS

This work shows that on the fly 2D/3DUS registration can be performed very efficiently on biopsy trajectories. This allows us to plan further improvements in prostate navigation and a clinical transfer.

Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions

PURPOSE

Transrectal ultrasound (TRUS) image guidance is the standard of care for diagnostic and therapeutic interventions in prostate cancer (PCa) patients, but can lead to high false-negative rates, compromising downstream effectiveness of therapeutic choices. A promising approach to improve in-situ detection of PCa lies in using the optical properties of the tissue to discern cancer from healthy tissue. In this work, we present the first in-situ image-guided navigation system for a spatially tracked Raman spectroscopy probe integrated in a PCa workflow, capturing the optical tissue fingerprint. The probe is guided with fused TRUS/MR imaging and tested with both tissue-simulating phantoms and ex-vivo prostates. The workflow was designed to be integrated the clinical workflow for trans-perineal prostate biopsies, as well as for high-dose rate (HDR) brachytherapy.

METHODS

The proposed system developed in 3D Slicer includes an electromagnetically tracked Raman spectroscopy probe, along with tracked TRUS imaging automatically registered to diagnostic MRI. The proposed system is tested on both custom gelatin tissue-simulating optical phantoms and biological tissue phantoms. A random-forest classifier was then trained on optical spectrums from ex-vivo prostates following prostatectomy using our optical probe. Preliminary in-human results are presented with the Raman spectroscopy instrument to detect malignant tissue in-situ with histopathology confirmation.

RESULTS

In 5 synthetic gelatin and biological tissue phantoms, we demonstrate the ability of the image-guided Raman system by detecting over 95% of lesions, based on biopsy samples. The included lesion volumes ranged from 0.1 to 0.61 cc. We showed the compatibility of our workflow with the current HDR brachytherapy setup. In ex-vivo prostates of PCa patients, the system showed a 81% detection accuracy in high grade lesions.

CONCLUSION

Pre-clinical experiments demonstrated promising results for in-situ confirmation of lesion locations in prostates using Raman spectroscopy, both in phantoms and human ex-vivo prostate tissue, which is required for integration in HDR brachytherapy procedures.