Fast quantitative bone marrow lesion measurement on knee MRI for the assessment of osteoarthritis.

Objective: Knee osteoarthritis (KOA) is a prevalent disease with a high economic and social cost. Magnetic resonance imaging (MRI) can be used to visualize many KOA-related structures including bone marrow lesions (BMLs), which are associated with OA pain. Several semi-automated software methods have been developed to segment BMLs, using manual, labor-intensive methods, which can be costly for large clinical trials and other studies of KOA. The goal of our study was to develop and validate a more efficient method to quantify BML volume on knee MRI scans. Materials and methods: We have applied a deep learning approach using a patch-based convolutional neural network (CNN) which was trained using 673 MRI data sets and the segmented BML masks obtained from a trained reader. Given the location of a BML provided by the reader, the network performed a fully automated segmentation of the BML, removing the need for tedious manual delineation. Accuracy was quantified using the Pearson's correlation coefficient, by a comparison to a second expert reader, and using the Dice Similarity Score (DSC). Results: The Pearson's R2 value was 0.94 and we found similar agreement when comparing two readers (R2 â€‹= â€‹0.85) and each reader versus the DL model (R2 â€‹= â€‹0.95 and R2 â€‹= â€‹0.81). The average DSC was 0.70. Conclusions: We developed and validated a deep learning-based method to segment BMLs on knee MRI data sets. This has the potential to be a valuable tool for future large studies of KOA.

Ultrasound-based sensors for respiratory motion assessment in multimodality PET imaging.

Breathing motion can displace internal organs by up to several cm; as such, it is a primary factor limiting image quality in medical imaging. Motion can also complicate matters when trying to fuse images from different modalities, acquired at different locations and/or on different days. Currently available devices for monitoring breathing motion often do so indirectly, by detecting changes in the outline of the torso rather than the internal motion itself, and these devices are often fixed to floors, ceilings or walls, and thus cannot accompany patients from one location to another. We have developed small ultrasound-based sensors, referred to as 'organ configuration motion' (OCM) sensors, that attach to the skin and provide rich motion-sensitive information. In the present work we tested the ability of OCM sensors to enable respiratory gating duringin vivoPET imaging. A motion phantom involving an FDG solution was assembled, and two cancer patients scheduled for a clinical PET/CT exam were recruited for this study. OCM signals were used to help reconstruct phantom andin vivodata into time series of motion-resolved images. As expected, the motion-resolved images captured the underlying motion. In Patient #1, a single large lesion proved to be mostly stationary through the breathing cycle. However, in Patient #2, several small lesions were mobile during breathing, and our proposed new approach captured their breathing-related displacements. In summary, a relatively inexpensive hardware solution was developed here for respiration monitoring. Because the proposed sensors attach to the skin, as opposed to walls or ceilings, they can accompany patients from one procedure to the next, potentially allowing data gathered in different places and at different times to be combined and compared in ways that account for breathing motion.

Ultrasound-based sensors to monitor physiological motion.

PURPOSE: Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals. METHODS: By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively. RESULTS: Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. CONCLUSION: The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.

Focused Ultrasound Platform for Investigating Therapeutic Neuromodulation Across the Human Hippocampus.

Pulsed low-intensity focused ultrasound (PLIFUS) has shown promise in inducing neuromodulation in several animal and human studies. Therefore, it is of clinical interest to develop experimental platforms to test repetitive PLIFUS as a therapeutic modality in humans with neurologic disorders. In the study described here, our aim was to develop a laboratory-built experimental device platform intended to deliver repetitive PLIFUS across the hippocampus in seizure onset zones of patients with drug-resistant temporal lobe epilepsy. The system uses neuronavigation targeting over multiple therapeutic sessions. PLIFUS (548 kHz) was emitted across multiple hippocampal targets in a human subject with temporal lobe epilepsy using a mechanically steered piezoelectric transducer. Stimulation was delivered up to 2.25 W/cm2 spatial peak temporal average intensity (free-field equivalent), with 36%-50% duty cycle, 500-ms sonications and 7-s inter-stimulation intervals lasting 140 s per target and repeated for multiple sessions. A first-in-human PLIFUS course of treatment was successfully delivered using the device platform with no adverse events.

Multi-pathway multi-echo acquisition and neural contrast translation to generate a variety of quantitative and qualitative image contrasts.

PURPOSE: Clinical exams typically involve acquiring many different image contrasts to help discriminate healthy from diseased states. Ideally, 3D quantitative maps of all of the main MR parameters would be obtained for improved tissue characterization. Using data from a 7-min whole-brain multi-pathway multi-echo (MPME) scan, we aimed to synthesize several 3D quantitative maps (T1 and T2 ) and qualitative contrasts (MPRAGE, FLAIR, T1 -weighted, T2 -weighted, and proton density [PD]-weighted). The ability of MPME acquisitions to capture large amounts of information in a relatively short amount of time suggests it may help reduce the duration of neuro MR exams. METHODS: Eight healthy volunteers were imaged at 3.0T using a 3D isotropic (1.2 mm) MPME sequence. Spin-echo, MPRAGE, and FLAIR scans were performed for training and validation. MPME signals were interpreted through neural networks for predictions of different quantitative and qualitative contrasts. Predictions were compared to reference values at voxel and region-of-interest levels. RESULTS: Mean absolute errors (MAEs) for T1 and T2 maps were 216 ms and 11 ms, respectively. In ROIs containing white matter (WM) and thalamus tissues, the mean T1 /T2 predicted values were 899/62 ms and 1139/58 ms, consistent with reference values of 850/66 ms and 1126/58 ms, respectively. For qualitative contrasts, signals were normalized to those of WM, and MAEs for MPRAGE, FLAIR, T1 -weighted, T2 -weighted, and PD-weighted contrasts were 0.14, 0.15, 0.13, 0.16, and 0.05, respectively. CONCLUSIONS: Using an MPME sequence and neural-network contrast translation, whole-brain results were obtained with a variety of quantitative and qualitative contrast in ~6.8 min.

Virtual Brain Projection for Evaluating Trans-skull Beam Behavior of Transcranial Ultrasound Devices.

Focused ultrasound single-element piezoelectric transducers constitute a promising method to deliver ultrasound to the brain in low-intensity applications, but are subject to defocusing and high attenuation because of transmission through the skull. Here, a novel virtual brain projection method is used to superimpose a magnetic resonance image of the brain in ex vivo human skulls to provide targets during trans-skull focused ultrasound single-element piezoelectric transducer pressure field mapping. Positions of the transducer, skull and hydrophone are tracked in real time using a stereoscopic navigation camera and 3-D Slicer software. Virtual targets of the left dorsolateral prefrontal cortex, left hippocampus and cerebellar vermis were chosen to illustrate the method's flexibility in evaluating focal-zone beam distortion and attenuation. The regions are of interest as non-invasive brain stimulation targets in the treatment of neuropsychiatric disorders via repeated ultrasound exposure. The technical approach can facilitate the assessment of transcranial ultrasound device operator positioning reliability, intracranial beam behavior and computational model validation.

Multipathway multi-echo (MPME) imaging: all main MR parameters mapped based on a single 3D scan.

PURPOSE: Quantitative parameter maps, as opposed to qualitative grayscale images, may represent the future of diagnostic MRI. A new quantitative MRI method is introduced here that requires a single 3D acquisition, allowing good spatial coverage to be achieved in relatively short scan times. METHODS: A multipathway multi-echo sequence was developed, and at least 3 pathways with 2 TEs were needed to generate T1 , T2 , T2* , B1+ , and B0 maps. The method required the central k-space region to be sampled twice, with the same sequence but with 2 very different nominal flip angle settings. Consequently, scan time was only slightly longer than that of a single scan. The multipathway multi-echo data were reconstructed into parameter maps, for phantom as well as brain acquisitions, in 5 healthy volunteers at 3 T. Spatial resolution, matrix size, and FOV were 1.2 × 1.0 × 1.2 mm3 , 160 × 192 × 160, and 19.2 × 19.2 × 19.2 cm3 (whole brain), acquired in 11.5 minutes with minimal acceleration. Validation was performed against T1 , T2 , and T2* maps calculated from gradient-echo and spin-echo data. RESULTS: In Bland-Altman plots, bias and limits of agreement for T1 and T2 results in vivo and in phantom were -2.9/±125.5 ms (T1 in vivo), -4.8/±20.8 ms (T2 in vivo), -1.5/±18.1 ms (T1 in phantom), and -5.3/±7.4 ms (T2 in phantom), for regions of interest including given brain structures or phantom compartments. Due to relatively high noise levels, the current implementation of the approach may prove more useful for region of interest-based as opposed to pixel-based interpretation. CONCLUSIONS: We proposed a novel approach to quantitatively map MR parameters based on a multipathway multi-echo acquisition.

Using the variogram for vector outlier screening: application to feature-based image registration.

PURPOSE: Matching points that are derived from features or landmarks in image data is a key step in some medical imaging applications. Since most robust point matching algorithms claim to be able to deal with outliers, users may place high confidence in the matching result and use it without further examination. However, for tasks such as feature-based registration in image-guided neurosurgery, even a few mismatches, in the form of invalid displacement vectors, could cause serious consequences. As a result, having an effective tool by which operators can manually screen all matches for outliers could substantially benefit the outcome of those applications. METHODS: We introduce a novel variogram-based outlier screening method for vectors. The variogram is a powerful geostatistical tool for characterizing the spatial dependence of stochastic processes. Since the spatial correlation of invalid displacement vectors, which are considered as vector outliers, tends to behave differently than normal displacement vectors, they can be efficiently identified on the variogram. RESULTS: We validate the proposed method on 9 sets of clinically acquired ultrasound data. In the experiment, potential outliers are flagged on the variogram by one operator and further evaluated by 8 experienced medical imaging researchers. The matching quality of those potential outliers is approximately 1.5 lower, on a scale from 1 (bad) to 5 (good), than valid displacement vectors. CONCLUSION: The variogram is a simple yet informative tool. While being used extensively in geostatistical analysis, it has not received enough attention in the medical imaging field. We believe there is a good deal of potential for clinically applying the proposed outlier screening method. By way of this paper, we also expect researchers to find variogram useful in other medical applications that involve motion vectors analyses.

Ultrasound-driven 4D MRI.

We present an ultrasound-driven 4D magnetic resonance imaging (US-4DMRI) method for respiratory motion imaging in the thorax and abdomen. The proposed US-4DMRI comes along with a high temporal resolution, and allows for organ motion imaging beyond a single respiratory cycle. With the availability of the US surrogate both inside and outside the MR bore, 4D MR images can be reconstructed for 4D treatment planning and online respiratory motion prediction during radiotherapy. US-4DMRI relies on simultaneously acquired 2D liver US images and abdominal 2D MR multi-slice scans under free respiration. MR volumes are retrospectively composed by grouping the MR slices corresponding to the most similar US images. We present two different US similarity metrics: an intensity-based approach, and a similarity measure relying on predefined fiducials which are being tracked over time. The proposed method is demonstrated on MR liver scans of eight volunteers acquired over a duration of 5.5 min each at a temporal resolution of 2.6 Hz with synchronous US imaging at 14 Hz-17 Hz. Visual inspection of the reconstructed MR volumes revealed satisfactory results in terms of continuity in organ boundaries and blood vessels. In quantitative leave-one-out experiments, both US similarity metrics reach the performance level of state-of-the-art navigator-based approaches.

Hybrid MRI-Ultrasound acquisitions, and scannerless real-time imaging.

PURPOSE: 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. METHODS: A small transducer, held onto the abdomen with an adhesive bandage, collected ultrasound signals during MRI. Based on these ultrasound signals and their correlations with MRI, a machine-learning algorithm created synthetic MR images at frame rates up to 100 per second. In one particular implementation, volunteers were taken out of the MRI bore with the ultrasound sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone in a "scannerless" manner. RESULTS: Hybrid ultrasound-MRI data were acquired in eight separate imaging sessions. Locations of liver features, in synthetic images, were compared with those from acquired images: The mean error was 1.0 pixel (2.1 mm), with best case 0.4 and worst case 4.1 pixels (in the presence of heavy coughing). For results from outside the bore, qualitative validation involved optically tracked ultrasound imaging with/without coughing. CONCLUSION: The proposed setup can generate an accurate stream of high-speed MR images, up to 100 frames per second, inside or even outside the MR bore. Magn Reson Med 78:897-908, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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.

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

PURPOSE: 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. METHODS: A standard balanced steady state free precession sequence was modified to simultaneously acquire two superimposed slices with different phase cycles, namely an image and a navigator slice. Instead of multiband RF pulses, two separate RF pulses were used for the excitation. Images were reconstructed using offline CAIPIRINHA reconstruction. Phantom and in vivo measurements of healthy volunteers were performed and evaluated. RESULTS: Phantom and in vivo measurements showed good image quality with high signal-to-noise ratio (SNR) and no reconstruction issues. CONCLUSION: We present a novel imaging method for truly simultaneous acquisition of image and navigator slices for four-dimensional (4D) MRI of organ motion. In this method, the time lag between the sequential acquisitions is eliminated, leading to an improved accuracy of organ motion models, while CAIPIRINHA reconstruction results in an improved SNR compared with an existing 4D MRI approach.

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.

Hybrid ultrasound/magnetic resonance simultaneous acquisition and image fusion for motion monitoring in the upper abdomen.

OBJECTIVES: The 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 METHODS: Ultrasound 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. RESULTS: No 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. CONCLUSIONS: This 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.

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.

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 target's 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 target's 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.

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

BACKGROUND: Multivariate 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. RESULTS: In 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. CONCLUSION: The 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.