Frank Preiswerk

3D organ motion prediction for MR-guided high intensity focused ultrasound

MR-guided High Intensity Focused Ultrasound is an emerging non-invasive technique capable of depositing sharply localised energy deep within the body, without affecting the surrounding tissues. This, however, implies exact knowledge of the targets position when treating mobile organs. In this paper we present an atlas-based prediction technique that trains an atlas from time-resolved 3D volumes using 4DMRI, capturing the full patient specific motion of the organ. Based on a breathing signal, the respiratory state of the organ is then tracked and used to predict the targets future position. To additionally compensate for the non-periodic slower organ drifts, the static motion atlas is combined with a population-based statistical exhalation drift model. The proposed method is validated on organ motion data of 12 healthy volunteers. Experiments estimating the future position of the entire liver result in an average prediction error of 1.1 mm over time intervals of up to 13 minutes.

Hybrid Ultrasound/Magnetic Resonance Simultaneous Acquisition and Image Fusion for Motion Monitoring in the Upper Abdomen

ObjectivesThe combination of ultrasound (US) and magnetic resonance imaging (MRI) may provide a complementary description of the investigated anatomy, together with improved guidance and assessment of image-guided therapies. The aim of the present study was to integrate a clinical setup for simultaneous US and magnetic resonance (MR) acquisition to obtain synchronized monitoring of liver motion. The feasibility of this hybrid imaging and the precision of image fusion were evaluated. Materials and MethodsUltrasound imaging was achieved using a clinical US scanner modified to be MR compatible, whereas MRI was achieved on 1.5- and 3-T clinical scanners. Multimodal registration was performed between a high-resolution T1 3-dimensional (3D) gradient echo (volume interpolated gradient echo) during breath-hold and a simultaneously acquired 2D US image, or equivalent, retrospective registration of US imaging probe in the coordinate frame of MRI. A preliminary phantom study was followed by 4 healthy volunteer acquisitions, performing simultaneous 4D MRI and 2D US harmonic imaging (Fo = 2.2 MHz) under free breathing. ResultsNo characterized radiofrequency mutual interferences were detected under the tested conditions with commonly used MR sequences in clinical routine, during simultaneous US/MRI acquisition. Accurate spatial matching between the 2D US and the corresponding MRI plane was obtained during breath-hold. In situ fused images were delivered. Our 4D MRI sequence permitted the dynamic reconstruction of the intra-abdominal motion and the calculation of high temporal resolution motion field vectors. ConclusionsThis study demonstrates that, truly, simultaneous US/MR dynamic acquisition in the abdomen is achievable using clinical instruments. A potential application is the US/MR hybrid guidance of high-intensity focused US therapy in the liver.

Model-guided respiratory organ motion prediction of the liver from 2D ultrasound

With the availability of new and more accurate tumour treatment modalities such as high-intensity focused ultrasound or proton therapy, accurate target location prediction has become a key issue. Various approaches for diverse application scenarios have been proposed over the last decade. Whereas external surrogate markers such as a breathing belt work to some extent, knowledge about the internal motion of the organs inherently provides more accurate results. In this paper, we combine a population-based statistical motion model and information from 2d ultrasound sequences in order to predict the respiratory motion of the right liver lobe. For this, the motion model is fitted to a 3d exhalation breath-hold scan of the liver acquired before prediction. Anatomical landmarks tracked in the ultrasound images together with the model are then used to reconstruct the complete organ position over time. The prediction is both spatial and temporal, can be computed in real-time and is evaluated on ground truth over long time scales (5.5 min). The method is quantitatively validated on eight volunteers where the ultrasound images are synchronously acquired with 4D-MRI, which provides ground-truth motion. With an average spatial prediction accuracy of 2.4 mm, we can predict tumour locations within clinically acceptable margins.

Robust tumour tracking from 2D imaging using a population-based statistical motion model

This paper describes a method for tracking a tumour using the planar projections of fiducial markers as surrogates. The projections can originate from various sources such as a beam-eye view X-ray, a portal imager or a fluoroscope. The two-dimensional position of the fiducial markers in the planar image in conjunction with a population-based statistical motion model is used to accurately predict and track the motion of a target volume during treatment. The basic assumption is that the projected surrogate locations contain valuable information about the in-plane motion of the lesion whereas the statistical motion model helps to describe the unobserved out-of-plane motion of the target volume. We analysed the accuracy with regard to varying the camera position and uncertainty in the measurement of the surrogate positions to simulate image noise and camera registration errors. The experiments showed that the tumour motion can be robustly predicted with an accuracy of 2.6 mm over a wide range of target volumes and treatment field directions despite a measurement error of σ = 2 mm for the fiducials.

A bayesian framework for estimating respiratory liver motion from sparse measurements

In this paper, we present an approach for modelling and predicting organ motion from partial information. We used 4D-MRI sequences of 12 subjects to build a statistical population model for respiratory motion of the liver. Using a Bayesian reconstruction approach, a pre-operative CT scan and a few known surrogate markers, we are able to accurately predict the position of the entire liver at all times. The surrogates may, for example, come from ultrasound, portal images captured during radiotherapy or from implanted electromagnetic beacons. In leave-one-out experiments, we achieve an average prediction error of 1.2 mm over sequences of 20 min with only three surrogates. Our model is accurate enough for clinically relevant treatment intervals and has the potential to be used for adapting the gating window in tumour therapy or even for tracking a tumour continuously during irradiation.

Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

BackgroundMultivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes.ResultsIn this study, we developed a computationally efficient algorithm for: i) the assessment of the significance of gene contributions and ii) the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples.ConclusionThe methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data.

Simultaneous acquisition of image and navigator slices using CAIPIRINHA for 4D MRI

Respiratory organ motion is still the major challenge of various image‐guided treatments in the abdomen. Dynamic organ motion tracking, necessary for the treatment control, can be performed with volumetric time‐resolved MRI that sequentially acquires one image and one navigator slice. Here, a novel imaging method is proposed for truly simultaneous high temporal resolution acquisition.

Hybrid MRI ultrasound acquisitions, and scannerless real-time imaging

To combine MRI, ultrasound, and computer science methodologies toward generating MRI contrast at the high frame rates of ultrasound, inside and even outside the MRI bore.

Towards more precise, minimally-invasive tumour treatment under free breathing

In recent years, significant advances have been made towards compensating respiratory organ motion for the treatment of tumours, e.g. for the liver. Among the most promising approaches are statistical population models of organ motion. In this paper we give an overview on our work in the field. We explain how 4D motion data can be acquired, how these motion models can then be built and applied in realistic scenarios. The application of the motion models is first shown on a case where 3D surrogate marker data is available. Then we will evaluate the prediction accuracy if only 2D and lastly 1D surrogate marker motion data is available. For all three scenarios we will give quantitative prediction accuracy results.

Hybrid Utrasound and MRI Acquisitions for High-Speed Imaging of Respiratory Organ Motion

Magnetic Resonance (MR) imaging provides excellent image quality at a high cost and low frame rate. Ultrasound (US) provides poor image quality at a low cost and high frame rate. We propose an instance-based learning system to obtain the best of both worlds: high quality MR images at high frame rates from a low cost single-element US sensor. Concurrent US and MRI pairs are acquired during a relatively brief offine learning phase involving the US transducer and MR scanner. High frame rate, high quality MR imaging of respiratory organ motion is then predicted from US measurements, even after stopping MRI acquisition, using a probabilistic kernel regression framework. Experimental results show predicted MR images to be highly representative of actual MR images.