image registration, deformation, and enhanced …...1 image registration, deformation, and enhanced...

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Image registration, deformation, and enhanced contouring for radiotherapy

with MIM MaestroTM

Debra H. Brinkmann, Ph.D., Mayo Clinic Rochester, MN

Jon Piper, MIM Software Inc., Cleveland, OH

Disclosure

• JP is a developer, employee, and has ownership interest in MIM Software Inc., Cleveland, OH

Outline

• Introduction/Overview of MIM MaestroTM JP

• Clinical Examples DHB / Technical features JP– Image Registration

• Rigid• Deformable

– Enhanced Contouring• Adaptive Contouring • PET segmentation tools• Atlas-based Segmentation

• Future Directions of MIM MaestroTM JP

Introduction / OverviewMIM Maestro 5.1

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• Evolution: Diagnostic tools redesigned for Radiation

Oncology

• Deformable tools initially developed in 2007

• Contouring, registration, fusion, dose review

MIM Maesro Introduction

Image registration: Rigid

Image Registration: Rigid

Workflow• Select image series• Select workflow &

follow instructions• Registration options:

– Assisted Alignment– Box-based Alignment– Contour-based

Alignment– Manual– View/edit translations

/rotations• Review match• Save as reformatted

images

blending

spyglass


Clinical Examples – MR/CTplan

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assisted

alignment

box-based

alignment

reset translations

manually move close

define ROI for box-based alignment

resulting registration


Clinical Examples – initial alignment off

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Tips from clinical experience

• Use workflows– Customizable instructions

– Streamlines process

– Consistent output

• Use box-based/contour-based alignment– Focuses/Improves registration over region of interest

• If initial match is strange– Reset shift

– Get close with manual tools

– Then re-run alignment method



• For multiple MR to CT fusion, assign best anatomical MR series as first series to match – Applies match to all series from same exam

• opportunity to adjust if needed

– Pay attention – assignment order currently flips around each time you enter workflow

• Orientation issues resolved in 5.1.2 beta– Trouble with other systems interpreting orientation of

reformatted FFS matched to HFS

• Assisted alignment: uses field of view displayed

• Contour/Box-based alignment: uses only data inside

• All algorithms employ nMI

• Point-based alignment: for fiducials, but beware

Rigid registration

• Evaluate/adjust registration in all planes

• Be careful when using rigid alignment to solve a

deformable problem• Select rigid surrogates carefully: bones, small structures

• Multiple locally rigid registrations approximate deformation

• Remember MIM uses displayed contrast for rigid

alignments

Rigid registration

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Image registration: Deformable

Image Registration: Deformable

Workflow• Initial rigid alignment

to get close• Deformable CT-CT

alignment – Uses entire overlapping

volume

• Apply deformation to other series (PET, SPECT, RTSS, RTDose)

• Evaluation(voxel-to-voxel)

• Save as reformatted images


Clinical Examples – PET/CTnm/CTplan


Clinical Examples – PET/CTnm/CTplan

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• We tend to try “regional” rigid registration first (in case that is sufficient)

• Results can be strange - less so in 5.1

• During initial rigid alignment – focus on ROI (only input the user has control over)

• Be aware of potential offset between PET/SPECT and “inherently registered” CT

• Wishlist: evaluation map (where was deformation

significant)

Methods

• Constrained Intensity-based free-

form (DOF: millions)

• Validation•Correlation

•Recover known deformations

•Consistency (forward and reverse)

Results

• Correlation: on the order of 1.4mm error

• Phantom: 1.1mm mean error

• Consistency: 3.1mm mean

concatenated error

Evaluation of an Intensity-Based Free-form

Deformable Registration AlgorithmJW Piper1,2

1MIM Software Inc, 2Wake Forest University

PET/CT Deformable Registration

PET/CT Arms Down - SIM CT Arms Up

PET/CT Deformable Registration

PET/CT Arms Down - SIM CT Arms Up

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Methods

• 15 patients in different positions

(4 BOT, 5 Tonsil, 6 Larynx)

• GTV definition•Manual correlation (gold standard)

•Rigid registration

•Deformable registration

Results

• Rigid errors: 7.81 (5.36) mm

• Deformable errors: 2.63 (2.76) mm

Utility of Deformable PET Fusion in Elucidating

GTV in Head & Neck MalignanciesSE Fogh1, GJ Kubicek1, R Axelrod1, WM Keane1, JW Piper2,3, Y Xiao1, M Machtay1,

1Thomas Jefferson University Hospital, 2MIM Software Inc, 3Wake Forest University

• Non-correspondences

• Point-correlation is important to evaluation

• PET/CT deformation in the thorax and abdomen•PET/CT is 3D CT but 4D PET

•Best will be 4D PET/CT or use 4DCT for planning with careful

selection of phase for deformation

Deformable PET/CT

Enhanced Contouring: Adaptive Contouring

Enhanced Contouring: Adaptive Re-contouring

Workflow• Input: original CT,

original RTSS, new CT• Initial rigid alignment

to get close• Deformable CT-CT

alignment• Apply deformation

to original structures• Save deformed

structures

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Clinical Examples – 5.1 vs. 4.1

Version

5.1.2beta

Extra

smoothing?

Version

4.1

Planning

CT

CBCT

week1


Clinical Examples – CBCT changes

CBCT

week5

CBCT

week1


Clinical Examples – Replan

Defining ROI for box-based

alignment to update initial

rigid registration over area of

interest (tongue)

Bolus masked out

Rigid

Deformable



• During initial rigid alignment – focus on ROI

• Have user recreate PTV from deformed GTV/CTV

• Make sure users save structures to NEW CT

• Works well for head and neck, CNS…(Can use mask function as a workaround e.g. if bolus in only

one scan)

• Works well for lung CBCT (using first CBCT as reference)

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• Does not work well for abdomen if contrast present in only one scan

• Do not use deformable dose currently– Have included relevant isodose lines as contours– RTDose output (new in MIM 5) is 16bit vs 32bit needed by our

TPS – should be available in MIM 5.2

• We do not use clinically yet, but functionality available for propagating contours on 4DCT phases to generate ITV, play movie loops…

Deformable Adaptive Re-contouring

• Automatically deforms structure sets to match anatomy

in replanning CT

• Contours should be edited as necessary

• Data from Tsuji, Hwang, and Weinberg* indicate•No significant impact on dose between manual and adaptive OAR

•CTVs are significantly different due to changed treatment strategy

*Tsuji SY, Hwang A, Weinberg V, et al. Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck

Cancer. IJROBP 2010;77(3):707-714.

Methods

• 2 CTs obtained for 7 H&N patients

• Contouring methods

•Manually generated contours

•Automatic adaptive re-contouring

• Modification of automatic contours

Results

• 68-86% reduction in contouring time

• Difference Automatic to Modified less than Manual to Modified (p < 0.01)

• Automatic contours of iso-volumic regions (cord, brainstem, etc.) more consistent with original contours than manual (p < 0.05)

Evaluation of a Deformable Re-Contouring

Method for Adaptive TherapyRC Fragoso1, JW Piper2,3, AS Nelson2, AS Harrison1, M Machtay2, Y Xiao1,


Deformable Dose Accumulation

Old CT

Original

dose

New CT

Deformed

dose

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Added

dose

New

dose

Deformed

old dose


• Dose accumulation commonly effects treatment plan for

recurrence

• Accurate deformable registration is needed because of

changes in anatomy

Methods• Acquire CT (CTr) & CBCT within 1 day

• Deform original CT to CBCT (CTd)

• Compare dose from CTd and CBCT to CTr

Results• Reduction in error using CTd compared

with CBCT

• PTV D95: 0.60% 0.40%

• PTV Dmean: 0.85% 0.47%

• Cord Dmax: 0.45% 1.29%

• Brainstem Dmax: 6.41% 0.03%

• R Parotid D50: 11.40% 1.06%

• L Parotid D50: 26.20% 2.57%

• Mandible Dmax: 0.90% 1.84%

CBCT for Dose Tracking in HNCK Hu1, A Surapeneni1, J Dolan1, JW Piper2,3, A Neff1, LB Harrison1

1Beth Israel Medical Center, 2MIM Software Inc, 3Wake Forest University

Deformation for Tumor Response

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• Deformably propagate contours from one phase to all

phases for more accurate ITV generation

• Dose, contours, and fusions on 4D cine

• 4D DVH

• The deformable algorithm has a broad capture range

4DCT 4DCT Deformable ITV Generation

• Adaptive re-contouring: some physicians like to keep

targets large even if the anatomy is shrinking. • Use rigid transfer for targets and deformable for nodes and OARs

• Contrast differences in CT are challenging for intensity-

based algorithms

• CBCT to CBCT is usually fine. CBCT to simCT is more

variable• Generally fine for correcting HU for dose calculation

• Recent reconstruction or hardware produces more consistent results

• Calibrate the HU on your OBI - or use our correction tools

• Use first fraction CBCT as your reference

Where to be Careful

Enhanced Contouring: PET segmentation tools

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Workflow• Select desired tool

– % threshold– SUV– PET Edge

• Set desired value (%, SUV)

• Click and drag to define ROI to search for %, SUV or edge


Examples – Segmentation Tools

% threshold

absolute

threshold

PET Edge

PET Edge

defined in each

plane gives

results within

1-2cc

Different

thresholds

Identifying most

active region


Clinical Examples – SPECT/CT

Plan CT

SPECT/CT



• PET Edge can be used with other modalities

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PET Tumor Segmentation

Challenge:

Accurate segmentation of PET GTV• Manual contouring

• Subject to contrast/window/level settings

• Threshold-based contouring• Small tumors

• Low activity tumors

• Variable activity tumors

• Variable background activity

Solution:

Gradient-based PET tumor segmentation• Image processing algorithm segments using maximum spatial

gradients

• Accurate, robust, and reproducible

PET Edge

Monte Carlo Lung PhantomLung tumors were simulated in a Monte Carlo Zubal phantom

at the University of Chicago,Med Phys 2008:35:3331-

3342

PET Tumor Segmentation

Methods

• 31 Monte Carlo simulated tumors


•Gradient-based method

•15-50% of max in 5% increments

Results• Mean absolute % error in volume

•Gradient: 11.0% (SD: 12%)

•25% thresh: 17.5% (SD: 29%)

• Slope of the best fit line

• Gradient: 1.03

• 15% thresh: 0.93

• 25% thresh: 0.77

• 35% thresh: 0.64

• 45% thresh: 0.44

PET Tumor Segmentation: Validation of a

Gradient-Based Method Using a NSCLC PhantomAD Nelson1, KD Brockway1, AS Nelson1, JW Piper1,2

1MIM Software Inc, 2Wake Forest University

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PET Tumor Segmentation: Multi-Observer

Validation Using a NSCLC PET Phantom

Methods• 31 MC simulated tumors

• 7 observers (3 rad, 4 RO)


•Gradient-based method

•25-50% of max

•Manual contouring

Results• Mean absolute % error in volume

•Gradient 11.0% (SD: 12%)

•25% thresh: 17.5% (SD: 29%)

• Mean absolute % error in volume

•p < 0.01 gradient vs MC or 25% threshold

AS Nelson1, M Werner-Wasik2, W Choi3, Y Arai4, P Faulhaber5,, P Kang3, F Almeida6, N Ohri2, JW Piper1, AD Nelson1

1MIM Software Inc., 2Thomas Jefferson University Hospital, 3Beth Israel Medical Center, 4University of Pittsburgh Medical Center,5University Hospitals Case Medical Center, 6University of Arizona Health Systems

Pathologic Correlation of PET-CT Based Auto

Contouring for Radiation Planning in Lung CancerSE Fogh1, J Kannarkatt1, A Farach1, C Intenzo4, R Axelrod2, P McCue3, AS Nelson5, M Warner-Wasik1

Departments of 1Radiation Oncology, 2Medical Oncology, 3Pathology, 4Radiology, Thomas Jefferson University, 5MIM Software Inc

Methods• 18 PET or PET/CT and lobectomy

• Max resected tumor diameter vs Max

dimension of contour

• PET Edge

• 34% of Max Threshold

Results• Pearson’s Correlation Coefficient

•PET Edge: 0.72

•34% thresh: 0.08

• Check all three planes to verify you're starting close to

the center if the target

• Targets with funny shapes may require multiple passes

with PET Edge

• Contrast, contrast, contrast

• Clinical decision rests with the physician

PET Edge

Enhanced Contouring: Atlas-based segmentation

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Workflow• Input: original CT, original

RTSS, new CT

• Select range (sup-inf)

• Select filter parameters for atlas subjects

• Select desired contours

• After best-match is found, select any additional contours to transfer

• Save deformed structures under the new CT


Example – with atlas built from cases



• Prior to building an atlas with input from multiple users, take time up front to standardize contours

• Anticipate co-pilot contouring tool will facilitate manageable editing for clinical use

Methods

• OPX (6), NPX (3), LAX (3)

• Comparison

•(A) Resident editing atlas contours

•(B) Atlas instead of resident

• Attending edits resident/atlas contours

Results

• 68% reduction in contouring time (A)

• 87% reduction in contouring time (B)

• Atlas contours saved as much time as resident

contours (B)

Multi-institutional Experience with

Atlas-Based Segmentation in Head and Neck IMRTK Hu1, A Lin2, A Young2, G Kubicek1, JW Piper3,4, AS Nelson3, J Dolan1, R Masino1, M Machtay2

1Beth Israel Medical Center, 2Thomas Jefferson University Hospital, 3MIM Software Inc, 4Wake Forest University

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Methods

• 98 patient atlas

• Comparison between

• Resident Attending

• Atlas Resident Attending

Results• 46% reduction in contouring time

• 47% for resident

• 36% for attending

• 54% for femurs

• 46% for prostate

Atlas-Based Segmentation in Prostate IMRT:

Time-savings in the Clinical WorkflowA Lin1, G Kubicek1, JW Piper2,3, AS Nelson2, AP Dicker1, RK Valicenti1


• 45% for bladder

• 35% for rectum

MIM Auto-contouring Tools

• Slice-to-slice contour deformation

• Re-shapes contour based on underlying image

• Works on any (anatomical) modality and in any plane

• Works on any contour

• Edit atlas contours or contour from scratch

• It isn’t auto... yet. Verify and edit the contours

Auto-contouring

• Rigid alignment first guess improvements for small FOV

• Major performance enhancement for deformable

registration

• Continued improvements to the deformable algorithms• Encouraged to QA versions of MIM with known data

• Contour CoPilot is still in its infancy

• 4D Dose accumulation

• Ability to limit and influence the registration

• MR/CT deformable registration... not yet.

• Session Save

Future enhancements

image registration, deformation, and enhanced …...1 image registration, deformation, and enhanced...

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