Rosalind Pratt

Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures

Abstract. Precise device guidance is important for interventional procedures in many different clinical fields including fetal medicine, regional anesthesia, interventional pain management, and interventional oncology. While ultrasound is widely used in clinical practice for real-time guidance, the image contrast that it provides can be insufficient for visualizing tissue structures such as blood vessels, nerves, and tumors. This study was centered on the development of a photoacoustic imaging system for interventional procedures that delivered excitation light in the ranges of 750 to 900 nm and 1150 to 1300 nm, with an optical fiber positioned in a needle cannula. Coregistered B-mode ultrasound images were obtained. The system, which was based on a commercial ultrasound imaging scanner, has an axial resolution in the vicinity of 100  μm and a submillimeter, depth-dependent lateral resolution. Using a tissue phantom and 800 nm excitation light, a simulated blood vessel could be visualized at a maximum distance of 15 mm from the needle tip. Spectroscopic contrast for hemoglobin and lipids was observed with ex vivo tissue samples, with photoacoustic signal maxima consistent with the respective optical absorption spectra. The potential for further optimization of the system is discussed.

Computer-assisted surgical planning and intraoperative guidance in fetal surgery: a systematic review

Fetal surgery has become a clinical reality, with interventions for twin‐to‐twin transfusion syndrome (TTTS) and spina bifida demonstrated to improve outcome. Fetal imaging is evolving, with the use of 3D ultrasound and fetal MRI becoming more common in clinical practise. Medical imaging analysis is also changing, with technology being developed to assist surgeons by creating 3D virtual models that improve understanding of complex anatomy, and prove powerful tools in surgical planning and intraoperative guidance.

GIFT-Cloud

Highlights • A platform for sharing medical imaging data between clinicians and researchers.• Extensible system connects three hospitals and two universities.• Simple for end users with low impact on hospital IT systems.• Automated anonymisation of pixel data and metadata at the clinical site.• Maintains subject data groupings while preserving anonymity.

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribbles and Online Learning

Segmentation of the placenta from fetal MRI is critical for planning of fetal surgical procedures. Unfortunately, it is made difficult by poor image quality due to sparse acquisition, inter-slice motion, and the widely varying position and orientation of the placenta between pregnant women. We propose a minimally interactive online learning-based method named Slic-Seg to obtain accurate placenta segmentations from MRI. An online random forest is first trained on data coming from scribbles provided by the user in one single selected start slice. This then forms the basis for a slice-by-slice framework that segments subsequent slices before incorporating them into the training set on the fly. The proposed method was compared with its offline counterpart that is with no retraining, and with two other widely used interactive methods. Experiments show that our method 1) has a high performance in the start slice even in cases where sparse scribbles provided by the user lead to poor results with the competitive approaches, 2) has a robust segmentation in subsequent slices, and 3) results in less variability between users.

Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views

Highlights • Minimal user interaction is needed for a good segmentation of the placenta.• Random forests with high level features improved the segmentation.• Higher accuracy than state-of-the-art interactive segmentation methods.• Co-segmentation of multiple volumes outperforms single sparse volume based method.

Imaging the human placental microcirculation with micro-focus computed tomography: Optimisation of tissue preparation and image acquisition

Micro-CT provides 3D volume imaging with spatial resolution at the micrometre scale. We investigated the optimal human placenta tissue preparation (contrast agent, perfusion pressure, perfusion location and perfusion vessel) and imaging (energy, target material, exposure time and frames) parameters. Microfil (Flow Tech, Carver, MA) produced better fill than Barium sulphate (84.1%(±11.5%)vs70.4%(±18.02%) p = 0.01). Perfusion via umbilical artery produced better fill than via chorionic vessels (83.8%(±17.7%)vs78.0%(±21.9%), p < 0.05), or via umbilical vein (83.8%(±16.4%)vs69.8%(±20.3%), p < 0.01). Imaging at 50 keV with a molybdenum target produced the best contrast to noise ratio. We propose this method to enable quantification and comparison of the human fetoplacental vascular tree.

Patient-specific tissue-mimicking phantoms for photoacoustic and ultrasound imaging (Conference Presentation)

Phantoms are crucial for developing photoacoustic imaging systems and for training practitioners. Advances in 3D printing technology have allowed for the generation of detailed moulds for tissue-mimicking materials that represent anatomically realistic tissue structures such as blood vessels. Here, we present methods to generate phantoms for photoacoustic and ultrasound imaging based on patient-specific anatomy and mineral oil based compounds as tissue-mimicking materials. Moulds were created using a 3D printer with fused deposition modelling. Optical and acoustic properties were independently tuned to match different soft tissue types using additives: inorganic dyes for optical absorption, TiO2 particles for optical scattering, paraffin wax for acoustic attenuation, and solid glass spheres for acoustic backscattering. Melted mineral oil compounds with additives were poured into the 3D printed moulds to fabricate different anatomical structures. Optical absorption and reduced scattering coefficients across the wavelength range of 400 to 1600 nm were measured using a spectrophotometer with an integrating sphere, and inverse adding-doubling. The acoustic attenuation and speed-of-sound were measured in reflection mode using a 10 MHz transducer. Three phantoms were created to represent nerves and adjacent blood vessels, a human placenta obtained after caesarean section, and a human heart based on an MRI image volume. Co-registered multi-wavelength photoacoustic and ultrasound images were acquired with a system that comprised a clinical ultrasound imaging scanner, an optical parametric oscillator, and linear-array ultrasound imaging probes. We conclude that mineral oil based compounds can be well suited to create anatomically-realistic phantoms for photoacoustic and ultrasound imaging using 3D printed moulds.

Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver

Ultrasound is frequently used for guiding minimally invasive procedures, but visualizing medical devices is often challenging with this imaging modality. When visualization is lost, the medical device can cause trauma to critical tissue structures. Here, a method to track the needle tip during ultrasound image-guided procedures is presented. This method involves the use of a fiber-optic ultrasound receiver that is affixed within the cannula of a medical needle to communicate ultrasonically with the external ultrasound probe. This custom probe comprises a central transducer element array and side element arrays. In addition to conventional two-dimensional (2D) B-mode ultrasound imaging provided by the central array, three-dimensional (3D) needle tip tracking is provided by the side arrays. For B-mode ultrasound imaging, a standard transmit-receive sequence with electronic beamforming is performed. For ultrasonic tracking, Golay-coded ultrasound transmissions from the 4 side arrays are received by the hydrophone sensor, and subsequently the received signals are decoded to identify the needle tips spatial location with respect to the ultrasound imaging probe. As a preliminary validation of this method, insertions of the needle/hydrophone pair were performed in clinically realistic contexts. This novel ultrasound imaging/tracking method is compatible with current clinical workflow, and it provides reliable device tracking during in-plane and out-of-plane needle insertions.

OP05.08: Novel placental evaluation using multimodal MRI

Results: The coefficients of variation in mature fetuses were greater than 30% for placentas in-vivo, greater than 35 for placentas in-vitro, greater than 29% for liver tissue and greater than 33% for lung tissue. In mature fetuses strain index (SI) for fetal lung was greater than 0,9, for placentas in vivo greater than 1,0, for placentas in vitro, greater than 1,5, for liver tissue greater than 0,7. We found significant difference in SI in normal pregnancies comparing with pre-eclamptic pregnancies and diabetic pregnancies. We did not find significant difference between lung volumes in all investigated patients. Conclusions: The coefficient of variation values and strain stiffness for placentas in vivo and in vitro, and fetal lungs and liver increase during pregnancy in normal and pre-eclamptic patients with increasing gestational age and decrease in diabetic patients. Lung volumes increase during pregnancy in normal, pre-eclamptic and diabetic patients.