Additional file 1: Online Supplementary Material 1

Similar documents
VALIDATION OF DIR. Raj Varadhan, PhD, DABMP Minneapolis Radiation Oncology

Automatic Segmentation of Parotids from CT Scans Using Multiple Atlases

[PDR03] RECOMMENDED CT-SCAN PROTOCOLS

2 Michael E. Leventon and Sarah F. F. Gibson a b c d Fig. 1. (a, b) Two MR scans of a person's knee. Both images have high resolution in-plane, but ha

REAL-TIME ADAPTIVITY IN HEAD-AND-NECK AND LUNG CANCER RADIOTHERAPY IN A GPU ENVIRONMENT

Shading correction algorithm for cone-beam CT in radiotherapy: Extensive clinical validation of image quality improvement

TomoTherapy Related Projects. An image guidance alternative on Tomo Low dose MVCT reconstruction Patient Quality Assurance using Sinogram

Use of Deformable Image Registration in Radiation Therapy. Colin Sims, M.Sc. Accuray Incorporated 1

CT IMAGE PROCESSING IN HIP ARTHROPLASTY

radiotherapy Andrew Godley, Ergun Ahunbay, Cheng Peng, and X. Allen Li NCAAPM Spring Meeting 2010 Madison, WI

8/3/2017. Contour Assessment for Quality Assurance and Data Mining. Objective. Outline. Tom Purdie, PhD, MCCPM

Using Pinnacle 16 Deformable Image registration in a re-treat scenario

Large deformation 3D image registration in image-guided radiation therapy

Methodological progress in image registration for ventilation estimation, segmentation propagation and multi-modal fusion

Prostate Detection Using Principal Component Analysis

Auto-contouring the Prostate for Online Adaptive Radiotherapy

Intraoperative Prostate Tracking with Slice-to-Volume Registration in MR

Shadow casting. What is the problem? Cone Beam Computed Tomography THE OBJECTIVES OF DIAGNOSTIC IMAGING IDEAL DIAGNOSTIC IMAGING STUDY LIMITATIONS

An Anatomical Atlas to Support the Virtual Planning of Hip Operations

4 CIM&Lab, Universidad Nacional de Colombia, Bogota, Colombia {edromero,

Atlas-Based Segmentation of Abdominal Organs in 3D Ultrasound, and its Application in Automated Kidney Segmentation

Semi-automated Basal Ganglia Segmentation Using Large Deformation Diffeomorphic Metric Mapping

3/27/2012 WHY SPECT / CT? SPECT / CT Basic Principles. Advantages of SPECT. Advantages of CT. Dr John C. Dickson, Principal Physicist UCLH

Patch-Based Image Similarity for Intraoperative 2D/3D Pelvis Registration During Periacetabular Osteotomy

MEDICAL IMAGE REGISTRATION GUIDED BY APPLICATION-SPECIFIC GEOMETRY FLORIS BERENDSEN

Basic relations between pixels (Chapter 2)

doi: /

Auto-Segmentation Using Deformable Image Registration. Disclosure. Objectives 8/4/2011

Michael Speiser, Ph.D.

Accelerating Pattern Matching or HowMuchCanYouSlide?

Accounting for Large Geometric Changes During Radiotherapy. Disclosures. Current Generation DIR in RT 8/3/2016

Basics of treatment planning II

Building tools for image-guided adaptive radiotherapy of bladder cancer Chai, X.

Biomedical Image Processing

Overview of Proposed TG-132 Recommendations

arxiv: v1 [cs.cv] 3 Aug 2017

Automated Image Analysis Software for Quality Assurance of a Radiotherapy CT Simulator

Separate CT-Reconstruction for Orientation and Position Adaptive Wavelet Denoising

Machine Learning for Medical Image Analysis. A. Criminisi

@ Massachusetts Institute of Technology All rights reserved.

FINITE element analysis is a powerful computational tool

S. Guru Prasad, Ph.D., DABR

Smoothing Dissimilarities for Cluster Analysis: Binary Data and Functional Data

Multi-Slice to Volume Registration of Ultrasound Data to a Statistical Atlas of Human Pelvis

Spiral CT. Protocol Optimization & Quality Assurance. Ge Wang, Ph.D. Department of Radiology University of Iowa Iowa City, Iowa 52242, USA

Segmentation of Bony Structures with Ligament Attachment Sites

IMRT site-specific procedure: Prostate (CHHiP)

Methods for data preprocessing

The team. Disclosures. Ultrasound Guidance During Radiation Delivery: Confronting the Treatment Interference Challenge.

A Radiometry Tolerant Method for Direct 3D/2D Registration of Computed Tomography Data to X-ray Images

Computerized determination of the ischio-iliac angle from CT images of the pelvis

Head and Neck Lymph Node Region Delineation with Auto-segmentation and Image Registration

Large-Deformation Image Registration using Fluid Landmarks

Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means

Morphological Image Processing

GPU applications in Cancer Radiation Therapy at UCSD. Steve Jiang, UCSD Radiation Oncology Amit Majumdar, SDSC Dongju (DJ) Choi, SDSC

Lecture 4: Spatial Domain Transformations

Semantic Context Forests for Learning- Based Knee Cartilage Segmentation in 3D MR Images

LEVEL SET ALGORITHMS COMPARISON FOR MULTI-SLICE CT LEFT VENTRICLE SEGMENTATION

Nearest Neighbor 3D Segmentation with Context Features

Automatic DRR Enhancement for Patient Positioning in a Radiotherapy Treatment

How does the ROI affect the thresholding?

An Intuitive Explanation of Fourier Theory

Validation of non-rigid point-set registration methods using a porcine bladder pelvic phantom

Tomographic Reconstruction

MATERIAL property estimation has been an important

Feasibility of 3D Printed Patient specific Phantoms for IMRT QA and Other Dosimetric Special Procedures

Three-dimensional Image Processing Techniques to Perform Landmarking and Segmentation of Computed Tomographic Images

8/2/2016. Measures the degradation/distortion of the acquired image (relative to an ideal image) using a quantitative figure-of-merit

Atlas Based Segmentation of the prostate in MR images

Anatomical landmark and region mapping based on a template surface deformation for foot bone morphology

Fundamentals of CT imaging

Using Probability Maps for Multi organ Automatic Segmentation

Auxiliary Anatomical Labels for Joint Segmentation and Atlas Registration

David Wagner, Kaan Divringi, Can Ozcan Ozen Engineering

Getting to Know Your Data

Is deformable image registration a solved problem?

arxiv: v1 [cs.cv] 6 Jun 2017

A simple method to test geometrical reliability of digital reconstructed radiograph (DRR)

Applying Hounsfield unit density calibration in SkyScan CT-analyser

PET-CT in Radiation Treatment Planning

Good Morning! Thank you for joining us

Interactive segmentation of vascular structures in CT images for liver surgery planning

IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 30, NO. 1, JANUARY

High Throughput Computing and Sampling Issues for Optimization in Radiotherapy

Image Segmentation and Registration

Elastically Deforming a Three-Dimensional Atlas to Match Anatomical Brain Images

CHAPTER 9 INFLUENCE OF SMOOTHING ALGORITHMS IN MONTE CARLO DOSE CALCULATIONS OF CYBERKNIFE TREATMENT PLANS: A LUNG PHANTOM STUDY

A Multiple-Layer Flexible Mesh Template Matching Method for Nonrigid Registration between a Pelvis Model and CT Images

Implementation of Advanced Image Guided Radiation Therapy

EE368 Project: Visual Code Marker Detection

Image Co-Registration II: TG132 Quality Assurance for Image Registration. Image Co-Registration II: TG132 Quality Assurance for Image Registration

A framework for deformable image registration validation in radiotherapy clinical applications

Reconstructing the 3D Shape and Bone Mineral Density Distribution of the Proximal Femur From Dual-Energy X-Ray Absorptiometry

Medical Image Segmentation Based on Mutual Information Maximization

doi: /

Determination of rotations in three dimensions using two-dimensional portal image registration

Use of Monte Carlo modelling in radiotherapy linac design. David Roberts, PhD Senior Physicist Elekta

3D Volume Mesh Generation of Human Organs Using Surface Geometries Created from the Visible Human Data Set

Edge-Preserving Denoising for Segmentation in CT-Images

Transcription:

Additional file 1: Online Supplementary Material 1 Calyn R Moulton and Michael J House School of Physics, University of Western Australia, Crawley, Western Australia. Victoria Lye, Colin I Tang, Michele Krawiec, David J Joseph, and Martin A Ebert Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia. James W Denham School of Medicine and Population Health, University of Newcastle, Callaghan, New South Wales. calyn.moulton@research.uwa.edu.au School of Surgery, University of Western Australia, Crawley, Western Australia. School of Physics, University of Western Australia, Crawley, Western Australia. 1

I. IMAGE PROCESSING DETAILS 1. HDR needles were detected by thresholding CT numbers ( 3500). For each axial slice, a boundary was extracted from the outer most detected points. It was empirically determined that dilating the boundary in each axial slice by 6 voxels radially allowed the entirety of the HDR needles and associated artefacts to be contained. The pixels in the interior of the new boundaries were checked for a CT number above that of muscle/tissue (1200) or below that of fat/tissue (800). The detected pixels were replaced with the average CT number of neighboring undetected pixels. A binary mask was created from these detected pixels. The Hounsfield number is the CT number minus 1000. 2. For the HDR CTs, the rectum packing material extended beyond the superior-inferior extent of the HDR rectum structure. As such, pixels beyond the superior-inferior extent of the HDR structure that contained rectum packing material ( 1200) and low CT number artefacts ( 800) were detected and replaced with the average CT number of neighboring undetected pixels. The earlier mentioned binary mask was updated to include these detected pixels. 3. The following Gaussian smoothing and blurring process resulted in balanced HDR CT (I balanced ) and was applied to avoid features in the image caused by the pixel adjustments in steps 1 and 2: (a) A smoothed image (I smoothed ) was obtained by passing the adjusted HDR image (I adjusted ) through a Gaussian low pass-filter (σ = 2 voxels). (b) A blurred binary mask (M blurred ) was obtained by applying a Gaussian low-pass filter (σ = 2 voxels) to the binary mask. (c) A balanced HDR image (I balanced ) was obtained from the adjusted image (I adjusted ), smoothed image (I smoothed ) and the blurred binary mask (M blurred ) according to (1). I balanced = I adjusted (1 M blurred ) + I smoothed M blurred (1) 4. An approach for dealing with inconsistent contents between the rectum structures for the EBRT and HDR CTs is to paint the contents of the rectum structures with a uniform CT number. The contents of rectum structures were replaced with a high CT number (2500), which was empirically determined as separating the rectum from surrounding tissue. The final HDR and rigidly-registered EBRT CTs were obtained by applying this rectum-structure-painting to the balanced HDR CT (I balanced ) and the rigidly-registered EBRT CT. II. FIGURES AND TABLES COVERING ADDITIONAL ANALYSIS 2

FIG. A1. A planning CT and rectum contour for high-dose-rate brachytherapy (HDR). 3

FIG. A2. A planning CT and rectum contour for external beam radiotherapy (EBRT). Without IP With IP FIG. A3. The high-dose-rate brachytherapy (HDR) CT with and without image processing (IP). The contour is in black. 4

Without IP With IP FIG. A4. The rigidly-registered external beam radiotherapy (EBRT) CT with and without image processing (IP). The contour is in black. 5

FIG. A5. (a) Male pelvis with the coccyx (A), femoral heads (B), femoral necks (C) and pubic symphysis (D) marked. [Source: University of Rochester. 2015. Anatomy of the Male and Female Pelvis. University of Rochester Medical Center - Online Medical Encyclopedia, June24, 2015. http://www.urmc.rochester.edu/encyclopedia/getimage.aspx?imageid=322083] (b) Hip bone with the ischium near the inferior extent of the obturator foramen (E), superior ramus of pubis near the obturator canal (F) and medial aspect of the acetabulum (G) marked [Source: OpenStax College. 2013. The Pelvic Girdle and Pelvis. OpenStax-CNX, June 4, 2013. https://legacy.cnx.org/content/m46375/1.3/.]. The major anatomy misalignments for rigid registrations were observed around the coccyx, ischium near the inferior extent of the obturator foramen, superior ramus of pubis near the obturator canal, femoral heads and femoral necks. The rigid registration was observed in the majority of slices to provide unacceptable alignment of the rectum and soft tissue surrounding the rectum. After DIR, the major anatomy misalignments were observed around the pubic symphysis, ischium near the inferior extent of the obturator foramen, superior ramus of pubis near the obturator canal, coccyx and the medial aspect of the acetabulum. 6

TABLE A1. A summary of the improvement in the Dice similarity coefficient (DSC), average surface distance (ASD) and Hausdorff distance (HD) under various registration method comparisons for 64 patients via the median of percentage changes [X versus Y = 100*(X/Y-1)] and exact Wilcoxon singed-rank tests of medians of zero. A positive percentage difference in DSC indicates that the first mentioned registrations is superior as it had the larger DSC and a larger DSC indicates more contour overlap. A negative percentage difference in ASD indicates that the first mentioned registration is superior as it had the smaller ASD and a smaller ASD indicates closer overall contour shape matching. A negative percentage difference in HD indicates that the first mentioned registration is superior as it had the smaller HD and a smaller HD indicates a less extreme contour shape discrepancy. DSC Results ASD Results HD Results Registration Median Signed-rank Signed-rank Median Signed-rank Signed-rank Median Signed-rank Signed-rank Comparison Change (%) Z-value P-value Change (%) Z-value P-value Change (%) Z-value P-value V1 versus Rigid 19.4 6.94 <0.0001-37.9-6.96 <0.0001-15.5-6.53 <0.0001 V2 versus Rigid 22.6 6.94 <0.0001-39.7-6.95 <0.0001-16.7-6.57 <0.0001 D versus Rigid 26.8 6.87 <0.0001-47.1-6.77 <0.0001-9.17-2.37 0.0170 HS versus Rigid 32.0 6.96 <0.0001-63.0-6.96 <0.0001-15.6-5.58 <0.0001 7

Box Around Rectum Median of Change in MI After DIR Median of Change in MSE After DIR Median of the Percentage of Voxels with a Negative JAC 6 3 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 Rectum Volume Median of Change in MI After DIR Median of Change in MSE After DIR Median of the Percentage of Voxels with a Negative JAC Metric Description 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 Median of Change in MI After DIR Whole Image Median of Change in MSE After DIR Median of the Percentage of Voxels with a Negative JAC 18 15 12 9 6 3 0 3 6 9 12 15 18 21 24 27 Metric Value Registration Method D HS V1 V2 FIG. A6. The medians of the percentage of voxels with a negative JAC after various registration methods applied to 64 patients. Additionally, the medians of the percentage changes (e.g. 100 after/before 100) in the MSE and MI image-similarity metrics due to registrations applied to 64 patients. The percentage changes in the MSE and MI are the values after DIR relative to values after rigid registration. Consequently, a negative change in MI or a positive change in MSE indicate that the DIR has not improved upon the image similarity achieved by the rigid registration. For each metric you read horizontally to get the metric values for the various registrations indicated by symbols. The JACs were calculated across the entire displacement vector field and the region contained by the volume of the rigidly-registered EBRT rectum structure. The MSE and MI metrics were calculated for the entirety of the images and a bounding box enclosing both the HDR CT and rigidly-registered EBRT CT rectum structures. 8

Box Around Rectum Median of Change in MI After DIR Median of Change in MSE After DIR Median of the Percentage of Voxels with a Negative JAC 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 Median of Change in MI After DIR Rectum Volume Median of Change in MSE After DIR Median of the Percentage of Voxels with a Negative JAC Metric Description 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 Median of Change in MI After DIR Whole Image Median of Change in MSE After DIR Median of the Percentage of Voxels with a Negative JAC 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 Z value Registration Comparison By Marker HS V2 HS V1 D V2 D V1 HS D V2 V1 P value By Fill 0.0001 0.001 0.01 0.05 0.1 1 FIG. A7. The results for the exact Wilcoxon signed-rank test of the pairwise differences in the JAC, MSE and MI metric values between various registration methods applied to 64 patients. The JACs were calculated across the entire displacement vector field and the rigidlyregistered EBRT CT rectum structure volume. The MSE and MI metrics were calculated for the entirety of the images and a bounding box enclosing both the HDR CT and rigidly-registered EBRT CT rectum structures. For each metric you read horizontally to get the Z-values for tests on the paired difference against a median of zero. The p-values associated with the Z-values are provided by a color fill of the plot markers. 9

TABLE A2. Results for Wilcoxon signed-rank tests of the pairwise differences in JAC, MI and MSE metric values between the various registration methods applied to 64 patients. The JACs, MIs and MSEs were calculated across the whole image (entire displacement vector field). The paired metric comparison (difference method) is median absolute difference (M = X Y) for metrics already in percentage format. The Z-values (Z) and p-values (p) from the tests that the medians are zero are included. A positive difference in the percentage of voxels with a negative JAC or change in MSE after DIR indicates that the second mentioned registration has less physical-unachievable displacements or less image dissimilarity respectively. A positive difference for the change in MI after DIR indicates that the first mentioned registration has superior image similarity. 10

TABLE A3. Results for Wilcoxon signed-rank tests of the pairwise differences in the JAC metric values between the various registration methods applied to 64 patients. The JACs were calculated in the region contained by the volume of the rigidly-registered EBRT rectum structure. The paired metric comparison (difference method) is median absolute difference (M = X Y) for metrics already in percentage format. The Z-values (Z) and p-values (p) from the tests that the medians are zero are included. A positive difference in the percentage of voxels with a negative JAC indicates that the second mentioned registration has less physical-unachievable displacements. 11

TABLE A4. Results for Wilcoxon signed-rank tests of the pairwise differences in the MI and MSE metric values between the various registration methods applied to 64 patients. The MIs and MSEs were calculated across a bounding box enclosing both the HDR CT and rigidly-registered EBRT CT rectum structures. The paired metric comparison (difference method) is median absolute difference (M = X Y) as the metrics are already in percentage format. The Z-values (Z) and p-values (p) from the tests that the medians are zero are included. A positive difference in the change in MSE after DIR indicates that the second mentioned registration has less image dissimilarity. A positive difference for the change in MI after DIR indicates that the first mentioned registration has superior image similarity. 12