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IIIT Hyderabad A METHOD FOR MOTION DETECTION AND CATEGORIZATION IN PERFUSION WEIGHTED MRI Rohit Gautam, Jayanthi Sivaswamy CVIT, IIIT Hyderabad, Hyderabad, India Ravi Varma KIMS Hospital, Hyderabad, India

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IIIT Hyderabad Introduction MRI for brain - Application of nuclear magnetic resonance (NMR) to create images of human brain. From MRI to Perfusion MRI Many neurological disorders can be detected using abnormal blood flow. Perfusion MRI utilizes this blood flow information in disease diagnosis. MRI Perfusion MRI

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IIIT Hyderabad What is Perfusion MRI ? Perfusion is the delivery of oxygen and nutrients to the cells via capillaries. A bolus injected into patient’s blood is tracked over time. It provides information regarding rate of blood flow, which helps to determine the affected regions in brain on the onset of disorder. Acquired data is a 3D time-series. 1 N n win n wout Time-points Before Bolus wash-in After Bolus wash-out Bolus in transit Volume

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IIIT Hyderabad Problem Patient motion during MRI scan (> 2 minutes) misaligns (corrupts) the acquired data. Aim - Motion detection and categorization of volumes corrupted due to patient motion. Difficulties Simultaneous local (non-uniform variation in image contrast due to bolus) and global (motion) changes. Current scenario: Motion correction is a time-limiting step in PWI analysis [ Straka et al. JMRI 07].

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IIIT Hyderabad N 1 Motion Before bolus wash-in After bolus wash-out Bolus in transit No variation in intensity Non-uniform Variation in intensity No variation in intensity

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IIIT Hyderabad Why motion correction ? TTP: Time to Peak CBV: Cerebral Blood Volume Perfusion parameters obtained from motion corrupted data vary with degree of motion. Error in CBV estimation Error in TTP estimation

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IIIT Hyderabad Division of Time-Series Find the different bolus stages. Intensity Correction Perform intensity correction on bolus affected volumes. Motion Detection Detect motion corrupted volumes (Tn). Motion Categorization Classify the time-series to a motion category. Proposed Divide and Conquer Strategy

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IIIT Hyderabad Division of Time-Series The signal intensity in perfusion MRI varies proportionally with bolus concentration. A gamma-variate function (GVF) can model the change in concentration of bolus with time. We fit GVF on the mean-intensity perfusion curve µ a (n) to estimate GVF-fit mean intensity curve µ g (n). Using µ g (n), we divide the time-series into 3 sets: Set1: pre-wash-in, Set2: transit and Set3: post-washout sets Wash-in Time point Wash-out Time point

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IIIT Hyderabad Division of Time-Series Find the different bolus stages. Intensity Correction Perform intensity correction on bolus affected volumes. Motion Detection Detect motion corrupted volumes (Tn). Motion Categorization Classify the time-series to a motion category. Divide and Conquer Strategy

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IIIT Hyderabad Intensity Correction Bolus is present only in Set2. Hence, intensity correction is required for bolus affected regions in these volumes before motion detection. A volume (F) is segmented into normal (F normal ) and bolus affected (F bolus ) regions using clustering technique. F is then intensity corrected : where, µ g (n) is the GVF-fit-mean intensity curve.

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IIIT Hyderabad Intensity Correction Slice 1 Slice 2 Intensity Corrected Slice 2 Absolute Difference Absolute Difference Ideally, these should be 0 Reduction in absolute intensity difference Intensity Correction Slice 1

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IIIT Hyderabad Division of Time-Series Find the different bolus stages. Intensity Correction Perform intensity correction on bolus affected volumes. Motion Detection D et ec t m ot io n co rr u pt ed vo lu m es (T n). Motion Categorization Classify the time-series to a motion category. Divide and Conquer Strategy

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IIIT Hyderabad Motion Detection Workflow

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IIIT Hyderabad Motion Detection For each mean shifted blocks of fixed size around each pixel, the flow vector is given by: where S is the cross power spectrum. Extract Central Slices Block wise Phase Correlation Process is accelerated by down-sampling of central slices. Block wise Phase Correlation

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IIIT Hyderabad Motion Flow Maps Slice 1 Slice 2 UnUn VnVn Bolus present and No motion Slice 1 Slice 2 UnUn VnVn Bolus absent and Minimal motion Slice 1 Slice 2 UnUn VnVn Bolus absent and Mild motion

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IIIT Hyderabad Net Entropy Metric For a given time series {F n ; n=1…N}, the net entropy (H n ) of flow fields (U n ;V n )is given by: The net entropy is 0 for no motion and increases with degree of motion.

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IIIT Hyderabad Zero net entropy even in the presence of bolus. Net Entropy Profile 1 5 8 33 40 Wash-in Time-point Wash-in Time-point

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IIIT Hyderabad A non-zero net entropy even in the absence of motion Does Intensity Correction help ?

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IIIT Hyderabad Slice Resolution Block SizeMean time per slice pair (sec) Total time (sec) 128x12832x320.00 + 3.48 = 3.48132.21 128x12816x160.00 + 3.99 = 3.99151.69 128x1288x80.00 + 4.34 = 4.34164.84 64x6416x160.01 + 0.77 = 0.7829.71 64x648x80.01 + 0.97 = 0.9837.38 32x328x80.01 + 0.19 = 0.207.68 Time Analysis of motion detection Large reduction in computation time

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IIIT Hyderabad Division of Time-Series Find the different bolus stages. Intensity Correction Perform intensity correction on bolus affected volumes. Motion Detection Detect motion corrupted volumes (Tn). Motion Categorization Classify the time-series to a motion category. Divide and Conquer Strategy

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IIIT Hyderabad Motion Categorization Peak entropy of flow fields is used to quantify the degree of motion. The peak entropy H peak of the flow fields is found by: where, H n is the net entropy.

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IIIT Hyderabad Motion Category Angle of rotation (in degrees) None00.00 Minimal10.00 Minimal20.00 Minimal30.040.000.04 Minimal40.080.000.23 Minimal50.200.000.76 Mild60.250.001.29 Mild70.400.002.04 Mild80.520.002.67 Mild90.610.003.25 Mild100.750.083.78 Severe111.050.324.33 Severe121.150.485.14 Severe131.310.595.75 Severe141.370.856.21 Severe151.510.976.88 Such a small motion cannot be detected. Peak entropy can distinguish between different motion categories. Entropy values for different motion categories for image size – 32x32 and block size 8x8

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IIIT Hyderabad Comparison of our approach We compare the efficiency of motion detection method by applying it prior to existing motion correction algorithms. Motion Correction Method Mean Time per volume registration (sec) Percentage Time Reduction (%) [1]640.3916.42397.42242.9737.94 [2]636.3816.32395.74240.6437.81 [3]1018.2026.11668.78349.4234.32 [1] Kosior et al., JMRI 2007. [2] Straka et al., JMRI 2010. [3] Tanner et al., MICCAI 2000.

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IIIT Hyderabad Conclusions We proposed a motion detection method that is immune to intensity changes due to injected contrast agent. We achieved a large reduction in time (~37%) required for motion correction by rejecting the stationary volumes. The detection method can be made to be fast but the sensitivity to minimal motion maybe compromised.

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IIIT Hyderabad Questions ?

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IIIT Hyderabad Thank you

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