Samuel Kadoury

Magnetic Resonance Imaging/Ultrasound Fusion Guided Prostate Biopsy Improves Cancer Detection Following Transrectal Ultrasound Biopsy and Correlates With Multiparametric Magnetic Resonance Imaging

PURPOSE A novel platform was developed that fuses pre-biopsy magnetic resonance imaging with real-time transrectal ultrasound imaging to identify and biopsy lesions suspicious for prostate cancer. The cancer detection rates for the first 101 patients are reported. MATERIALS AND METHODS This prospective, single institution study was approved by the institutional review board. Patients underwent 3.0 T multiparametric magnetic resonance imaging with endorectal coil, which included T2-weighted, spectroscopic, dynamic contrast enhanced and diffusion weighted magnetic resonance imaging sequences. Lesions suspicious for cancer were graded according to the number of sequences suspicious for cancer as low (2 or less), moderate (3) and high (4) suspicion. Patients underwent standard 12-core transrectal ultrasound biopsy and magnetic resonance imaging/ultrasound fusion guided biopsy with electromagnetic tracking of magnetic resonance imaging lesions. Chi-square and within cluster resampling analyses were used to correlate suspicion on magnetic resonance imaging and the incidence of cancer detected on biopsy. RESULTS Mean patient age was 63 years old. Median prostate specific antigen at biopsy was 5.8 ng/ml and 90.1% of patients had a negative digital rectal examination. Of patients with low, moderate and high suspicion on magnetic resonance imaging 27.9%, 66.7% and 89.5% were diagnosed with cancer, respectively (p <0.0001). Magnetic resonance imaging/ultrasound fusion guided biopsy detected more cancer per core than standard 12-core transrectal ultrasound biopsy for all levels of suspicion on magnetic resonance imaging. CONCLUSIONS Prostate cancer localized on magnetic resonance imaging may be targeted using this novel magnetic resonance imaging/ultrasound fusion guided biopsy platform. Further research is needed to determine the role of this platform in cancer detection, active surveillance and focal therapy, and to determine which patients may benefit.

Intravoxel incoherent motion MR imaging for prostate cancer: An evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations

There has been a resurgent interest in intravoxel incoherent motion (IVIM) MR imaging to obtain perfusion as well as diffusion information on lesions, in which the diffusion was modeled as Gaussian diffusion. However, it was observed that this diffusion deviated from expected monoexponential decay at high b‐values and the reported perfusion in prostate is contrary to the findings in dynamic contrast‐enhanced (DCE) MRI studies and angiogenesis. Thus, this work is to evaluate the effect of different b‐values on IVIM perfusion fractions (f) and diffusion coefficients (D) for prostate cancer detection. The results show that both parameters depended heavily on the b‐values, and those derived without the highest b‐value correlated best with the results from DCE‐MRI studies; specifically, f was significantly elevated (7.2% vs. 3.7%) in tumors when compared with normal tissues, in accordance with the volume transfer constant (Ktrans; 0.39 vs. 0.18 min−1) and plasma fractional volume (vp; 8.4% vs. 3.4%). In conclusion, it is critical to choose an appropriate range of b‐values in studies or include the non‐Gaussian diffusion contribution to obtain unbiased IVIM measurements. These measurements could eliminate the need for DCE‐MRI, which is especially relevant in patients who cannot receive intravenous gadolinium‐based contrast media. Magn Reson Med, 2013.

Multimodality image fusion-guided procedures: technique, accuracy, and applications.

Personalized therapies play an increasingly critical role in cancer care: Image guidance with multimodality image fusion facilitates the targeting of specific tissue for tissue characterization and plays a role in drug discovery and optimization of tailored therapies. Positron-emission tomography (PET), magnetic resonance imaging (MRI), and contrast-enhanced computed tomography (CT) may offer additional information not otherwise available to the operator during minimally invasive image-guided procedures, such as biopsy and ablation. With use of multimodality image fusion for image-guided interventions, navigation with advanced modalities does not require the physical presence of the PET, MRI, or CT imaging system. Several commercially available methods of image-fusion and device navigation are reviewed along with an explanation of common tracking hardware and software. An overview of current clinical applications for multimodality navigation is provided.

Real-time FDG PET Guidance during Biopsies and Radiofrequency Ablation Using Multimodality Fusion with Electromagnetic Navigation

PURPOSE To assess the feasibility of combined electromagnetic device tracking and computed tomography (CT)/ultrasonography (US)/fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) fusion for real-time feedback during percutaneous and intraoperative biopsies and hepatic radiofrequency (RF) ablation. MATERIALS AND METHODS In this HIPAA-compliant, institutional review board-approved prospective study with written informed consent, 25 patients (17 men, eight women) underwent 33 percutaneous and three intraoperative biopsies of 36 FDG-avid targets between November 2007 and August 2010. One patient underwent biopsy and RF ablation of an FDG-avid hepatic focus. Targets demonstrated heterogeneous FDG uptake or were not well seen or were totally inapparent at conventional imaging. Preprocedural FDG PET scans were rigidly registered through a semiautomatic method to intraprocedural CT scans. Coaxial biopsy needle introducer tips and RF ablation electrode guider needle tips containing electromagnetic sensor coils were spatially tracked through an electromagnetic field generator. Real-time US scans were registered through a fiducial-based method, allowing US scans to be fused with intraprocedural CT and preacquired FDG PET scans. A visual display of US/CT image fusion with overlaid coregistered FDG PET targets was used for guidance; navigation software enabled real-time biopsy needle and needle electrode navigation and feedback. RESULTS Successful fusion of real-time US to coregistered CT and FDG PET scans was achieved in all patients. Thirty-one of 36 biopsies were diagnostic (malignancy in 18 cases, benign processes in 13 cases). RF ablation resulted in resolution of targeted FDG avidity, with no local treatment failure during short follow-up (56 days). CONCLUSION Combined electromagnetic device tracking and image fusion with real-time feedback may facilitate biopsies and ablations of focal FDG PET abnormalities that would be challenging with conventional image guidance.

A versatile 3D reconstruction system of the spine and pelvis for clinical assessment of spinal deformities

This paper presents a three-dimensional (3D) reconstruction system of the human spine for the routine evaluation of musculoskeletal pathologies like idiopathic scoliosis. The main objective of this 3D reconstruction system is to offer a versatile and robust tool for the 3D analysis of spines in any healthcare centre with standard clinical setup using standard uncalibrated radiographic images. The novel system uses a self-calibration algorithm and a weak-perspective method to reconstruct the 3D coordinates of anatomical landmarks from bi-planar radiographic images of a patient’s trunk. Additionally, a small planar object of known dimensions is proposed to warrant an accurately scaled model of the spine. In order to assess the validity of the 3D reconstructions yielded by the proposed system, a clinical study using 60 pairs of digitized X-rays of adolescents was conducted. The subject cohort in the study group was composed of 51 scoliotic and 9 non-scoliotic patients, with an average Cobb angle on the frontal plane of 25°. For each case, a 3D reconstruction of the spine and pelvis was obtained with the previous system used at our hospital (which requires a positioning apparatus and a calibration jacket), and with the proposed method. Results show that 3D reconstructions obtained with the new system using uncalibrated X-ray images yield geometrically accurate models with insignificant differences for 2D and 3D clinical indexes commonly used in the evaluation of spinal deformities. This demonstrates the system to be a viable and accurate tool for clinical studies and biomechanical analysis purposes, with the added advantage of versatility to any clinical setup for routine follow-ups and surgical planning.

Robust, accurate and fast automatic segmentation of the spinal cord.

Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject correspondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. Although several automatic segmentation methods exist, they are often restricted in terms of image contrast and field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, accuracy and speed. The algorithm is based on the propagation of a deformable model and is divided into three parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough transform on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mesh. Secondly, a low-resolution deformable model is propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a local contrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applied on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in 15 healthy subjects and two patients with spinal cord injury, using T1- and T2-weighted images of the entire spinal cord and on multiecho T2*-weighted images. Our method was compared against manual segmentation and against an active surface method. Results show high precision for all the MR sequences. Dice coefficients were 0.9 for the T1- and T2-weighted cohorts and 0.86 for the T2*-weighted images. The proposed method runs in less than 1min on a normal computer and can be used to quantify morphological features such as cross-sectional area along the whole spinal cord.

A Novel System for the 3-D Reconstruction of the Human Spine and Rib Cage From Biplanar X-Ray Images

The main objective of this study was to develop a 3D X-ray reconstruction system of the spine and rib cage for an accurate 3D clinical assessment of spinal deformities. The system currently used at Sainte-Justine Hospital in Montreal is based on an implicit calibration technique based on a direct linear transform (DLT), using a sufficiently large rigid object incorporated in the positioning apparatus to locate any anatomical structure to be reconstructed within its bounds. During the time lapse between the two successive X-ray acquisitions required for the 3D reconstruction, involuntary patient motion introduce errors due to the incorrect epipolar geometry inferred from the stationary object. An approach using a new calibration jacket and explicit calibration algorithm is proposed in this paper. This approach yields accurate results and compensates for involuntary motion occurring between X-ray exposures.

The Importance of Skip Connections in Biomedical Image Segmentation

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.

Personalized X-Ray 3-D Reconstruction of the Scoliotic Spine From Hybrid Statistical and Image-Based Models

This paper presents a novel 3-D reconstruction method of the scoliotic spine using prior vertebra models with image-based information taken from biplanar X-ray images. We first propose a global modeling approach by exploiting the 3-D scoliotic curve reconstructed from a coronal and sagittal X-ray image in order to generate an approximate statistical model from a 3-D database of scoliotic patients based on a transformation algorithm which incorporates intuitive geometrical properties. The personalized 3-D reconstruction of the spine is then achieved with a novel segmentation method which takes into account the variable appearance of scoliotic vertebrae (rotation, wedging) from standard quality images in order to segment and isolate individual vertebrae on the radiographic planes. More specifically, it uses prior 3-D models regulated from 2-D image level set functionals to identify and match corresponding bone structures on the biplanar X-rays. An iterative optimization procedure integrating similarity measures such as deformable vertebral contours regulated from high-level anatomical primitives, morphological knowledge and epipolar constraints is then applied to globally refine the 3-D anatomical landmarks on each vertebra level of the spine. This method was validated on twenty scoliotic patients by comparing results to a standard manual approach. The qualitative evaluation of the retro-projection of the vertebral contours confirms that the proposed method can achieve better consistency to the X-ray images natural content. A comparison to synthetic models and real patient data also yields good accuracy on the localization of low-level primitives such as anatomical landmarks identified by an expert on each vertebra. The experiments reported in this paper demonstrate that the proposed method offers a better matching accuracy on a set of landmarks from biplanar views when compared to a manual technique for each evaluated cases, and its precision is comparable to 3-D models generated from magnetic resonance images, thus suitable for routine 3-D clinical assessment of spinal deformities.

Spine Segmentation in Medical Images Using Manifold Embeddings and Higher-Order MRFs

We introduce a novel approach for segmenting articulated spine shape models from medical images. A nonlinear low-dimensional manifold is created from a training set of mesh models to establish the patterns of global shape variations. Local appearance is captured from neighborhoods in the manifold once the overall representation converges. Inference with respect to the manifold and shape parameters is performed using a higher-order Markov random field (HOMRF). Singleton and pairwise potentials measure the support from the global data and shape coherence in manifold space respectively, while higher-order cliques encode geometrical modes of variation to segment each localized vertebra models. Generic feature functions learned from ground-truth data assigns costs to the higher-order terms. Optimization of the model parameters is achieved using efficient linear programming and duality. The resulting model is geometrically intuitive, captures the statistical distribution of the underlying manifold and respects image support. Clinical experiments demonstrated promising results in terms of spine segmentation. Quantitative comparison to expert identification yields an accuracy of 1.6 ± 0.6 mm for CT imaging and of 2.0 ± 0.8 mm for MR imaging, based on the localization of anatomical landmarks.