Biopsy workflow using multimodal imaging
The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.
1. A multi-modal imaging method to generate a patient-specific transformation model, comprising the steps of:
acquiring pre-biopsy magnetic resonance (MR) image data and pre-biopsy ultrasound image data of a patient;
segmenting an anatomic region of interest and one or more lesions in an MR volume generated from the pre-biopsy MR image data;
segmenting the anatomic region of interest in an ultrasound volume generated from ultrasound image data;
aligning contours of the segmented anatomic region of interest in the MR volume with corresponding contours of the anatomic region of interest in the ultrasound volume to generate a pre-biopsy volume in which the MR volume and the ultrasound volume are aligned and on which contours of the one or more lesions from the MR volume are aligned;
training a deep learning transformation model based at least in part on the pre-biopsy ultrasound image data to create a deformable transformation model specific to the patient;
wherein the pre-biopsy ultrasound image data comprise longitudinal volume frames; and
wherein a range of motion of the pre-biopsy longitudinal volume frames is captured by the deep learning transformation model.
2. The method of claim 1 , wherein the pre-biopsy MR image data comprises two-dimensional (2D) MR images reconstructed into a 3D representation and the pre-biopsy ultrasound image data comprises three-dimensional (3D) ultrasound images acquired over time.
3. The method of claim 1 , wherein the deep learning transformation model is specific to the anatomic region of interest.
4. The method of claim 3 , wherein the anatomic region of interest comprises a prostate.
5. The method of claim 1 , wherein the pre-biopsy MR image data and pre-biopsy ultrasound image data are acquired simultaneously.
6. The method of claim 5 , wherein the pre-biopsy ultrasound image data is acquired using an MR compatible ultrasound probe.
7. The method of claim 1 , further comprising the steps of:
acquiring one or more biopsy ultrasound images of a patient during a biopsy procedure;
performing a deformable alignment using the deformable transformation model to align each biopsy ultrasound image with the pre-biopsy volume, wherein the contours of the one or more lesions are previously associated with each biopsy ultrasound image; and
providing a real-time visualization of the contours of the one or more lesions on some or all of the biopsy ultrasound images.
8. A multi-modal imaging system comprising:
a memory encoding processor-executable routines; and
a processing component configured to access the memory and execute the processor-executable routines, wherein the routines, when executed by the processing component, cause the processing component to perform actions comprising:
acquiring pre-biopsy magnetic resonance (MR) image data and pre-biopsy ultrasound image data of a patient;
segmenting an anatomic region of interest and one or more lesions in an MR volume generated from the pre-biopsy MR image data;
segmenting the anatomic region of interest in an ultrasound volume generated from the ultrasound image data;
aligning contours of the segmented anatomic region of interest in the MR volume with corresponding contours of the anatomic region of interest in the ultrasound volume to generate a pre-biopsy volume in which the MR volume and the ultrasound volume are aligned and on which contours of the one or more lesions from the MR volume are aligned;
training a deep learning transformation model based at least in part on the pre-biopsy ultrasound image data to create a deformable transformation model specific to the patient;
acquiring one or more biopsy ultrasound images of the patient during a biopsy procedure;
performing a deformable alignment using the deformable transformation model to align each biopsy ultrasound image with the pre-biopsy volume, wherein the contours of the one or more lesions are associated with each biopsy ultrasound image;
providing a real-time visualization of the contours of the one or more lesions on some or all of the biopsy ultrasound images;
wherein the pre-biopsy ultrasound image data comprise longitudinal volume frames; and
wherein a range of motion of the pre-biopsy longitudinal volume frames is captured by the deep learning transformation model.
9. The method of claim 8 , wherein the pre-biopsy MR image data comprises two-dimensional (2D) MR images reconstructed into a 3D representation and the pre-biopsy ultrasound image data comprises three-dimensional (3D) ultrasound images.
10. The method of claim 8 , wherein the deep learning transformation model is specific to the anatomic region of interest.
11. The method of claim 8 , wherein the pre-biopsy MR image data and pre-biopsy ultrasound image data are acquired simultaneously.
12. A method to register a real-time image for a biopsy system, comprising the steps of:
acquiring one or more biopsy ultrasound images of a patient during a biopsy procedure;
performing a deformable alignment using a deformable transformation model to align each biopsy ultrasound image with a pre-biopsy volume, wherein the deformable transformation is specific to the patient and wherein the contours of one or more lesions are associated with each biopsy ultrasound image;
providing a real-time visualization of the contours of the one or more lesions on some or all of the biopsy ultrasound images;
wherein the deformable transformation model is generated using a deep learning transformation model trained at least in part based on pre-biopsy ultrasound image data;
wherein the pre-biopsy ultrasound image data comprise longitudinal volume frames; and
wherein a range of motion of the pre-biopsy longitudinal volume frames is captured by the deep learning transformation model.
13. The method of claim 12 , wherein the biopsy procedure comprises a trans-perineal biopsy procedure.
14. The method of claim 12 , wherein the deep learning transformation model is specific to the anatomy of interest.