# Presentation: 811530

## The Forefront in Image Processing: PET/Molecular Approaches

- Joel Karp
- University of Pennsylvania

*Sixth Annual NCI-Industry Forum*
*Quantitative Oncologic Imaging*
*April 7-8, 2005*

## Issues of Performance, Image Processing, Quantification

-  Performance of current-generation PET scanners			Global effects - data correction					Local effects - image reconstruction				Statistical and count-rate effects
-  Self-consistency: instrument performs same day-to-day
-  Cross-consistency: all instruments produce same result
-  Comparing images (PET *and* CT) from different patients, different instruments, and different institutes

## What is Measured with PET

*What is Measured with PET*

**a**

**b**

- What is measured

- Trues

- True coincidence

- Scattered coincidence

- Random coincidence
- (~2 **.** singles2)

- Coincidence?

*Record event*

- Normalization

- Attenuation

- Randoms

- Scatter

## Reconstruct image from line-of-response (LOR) projection data

**Signals from Different Voxels are Coupled**
****** ****Statistical Noise Does Not Obey Counting Statistics**

**If there are *****N***** counts in the image,**
**SNR ≠ N / (N)****1/2****	**

**Notes:**
Why does TOF information affect the noise? The reason is due to the nature of statistical noise in PET images. If there were a single point source in the field of view, then the noise in the image of that source would obey counting statistics. However, the fundamental measurement made in PET is the number of counts observed in a single chord. If activity is placed in another voxel, then counts due to the second source also contribute to the measured activity along the chord containing the two sources, increasing the statistical noise in that chord. Thus, the noise from activity in different voxels is correlated and greater than that from a single source.
Using time of flight information, activity from different voxels can effectively be ignored during reconstruction, provided that the voxels are well-enough separated in space that the timing resolution can distinguish between them. In this case, noise correlations only exist between nearby voxels (rather than all of the voxels), reducing the correlation and making the noise closer to the limit imposed by counting statistics.

## Iterative reconstruction 

- Iterative reconstruction

**Correction for **
**Attenuation,**
**Scatter,  **
**Randoms**

**y**

***Data***

**A** x  =  y

**y*****(k)***

**x*****(k)***

***Image***

- Forward projection

*******(k)***

**Differ**
**ence**

**c*****(k)***

**Update**

- Back-projection

**^**

*Start here*

## Data Flow

- DIGITIZER

- POSITION
-  CALCULATOR

- BINNER

- COMPUTER

- _RAWVIEW_                (52 bytes/event)
-    For A,B side of event
- 26 PMT energies/zone  (26 bytes)
*100M events = 5200 Mbytes*

- _LISTVIEW_                  (8 bytes/event)
-    For A,B side of event
- 2D position   (3 bytes)       timestamp
- Energy           (1 byte)        TOF   (1 byte)
*100M events = 800 Mbytes*

- _SINOGRAM_                (80 Mbytes/frame)
-    R, Phi            (295x161x2 = 95 Kbytes)
-    Slice              (29^2 = 841)
*100M events = 560 Mbytes *(7 frames)

- _Data Flow_

*Philips Allegro: 616 x 29 crystals*

- _IMAGE_
-    X,Y                (128x128 = 16 Kbytes)
*100M events = 4 Mbytes* (250 slices)

- reconstruction

- PACS archive

## 2D (septa) vs. 3D (no septa)

- 2D Imaging

- 3D Imaging

*Low* geometric sensitivity

-   *High* geometric sensitivity

- S

- T

- R

- Scatter decreases with high energy threshold - *depends on energy resolution*

## Out-of-field activity increases randoms in 3D

- Randoms ~ 2 **.** Singles2
* **decreases with narrow timing window (*2*)*
* **decreases with high energy threshold*
* **estimated (and subtracted) with 2nd (delayed) timing window*

- Problem increases as bore size increases
*-> less shielding*

## Count-rate Performance

- Noise Equivalent Count-rate
- NEC = T/(1+S/T+R/T)
- NEC ~ SNR2

- Philips *Allegro*

- 10 mCi dose

- 70-cm long x 20-cm diameter
- NEMA 2001 (body)

## PET Imaging Performance

-  Spatial resolution	 -> partial volume effect				intrinsic:		4-6 mm					reconstructed:	>10 mm
-  Scatter fraction -> noise *and* bias (after correction)		2D:			10-20% SF					3D:			30-60% SF
-  Sensitivity and count-rate capability	-> statistical quality	25 - 100 kcps 							*or* 5 M - 20 Mevents per 3 min frame

## Scatter Correction

- Single Scatter - Model based correction
- Calculate the contribution for an arbitrary scatter point using the Klein-Nishina equation

- Before
- Scatter
- correction

- After
- Scatter
- correction

## Attenuation correction with radioisotope transmission scan

- 20 mCi 137Cs source - 662 keV

- A = 1 / e -d
-   d = length of chord through tissue
-  = attenuation coefficient

## Attenuation correction for PET

- Types of transmission images

- Coincident photon Ge-68/Ga-68
- (511 keV)
- high noise
- 15-30 min scan time
- low bias
- low contrast

- Single photon Cs-137
- (662 keV)
- lower noise
- 5-10 min scan time
- some bias
- lower contrast

- X-ray
- (~30-140kVp)
- no noise
- 1 min scan time
- potential for bias
- high contrast

## Attenuation/Scatter correction

*University of Pennsylvania PET Center*

- No AC or Scatter Corr

- AC and Scatter Corr

- Philips* Allegro*

## Fully 3D Iterative Reconstruction improves image quality

**Fore-FBP**

**3D Ramla**

- How about quantification?

## NEMA NU2-2001 Image Quality Phantom

- Out-of-field Activity

- foam

## Partial Volume Effect 

## NEMA IEC Phantom

- LOR RAMLA reconstruction

- Vary relaxation parameter  from
- 0.00025 (top left) to 0.075 (bottom right)

## Contrast vs. Noise

- 1.7 cm hot sphere

- 2.8 cm cold sphere

- Iterative - Ramla
- Filtered Backprojection (FBP)

## Image processing Filters for restoring the spatial frequency components 

- Low (left)  -           Maximum gain = 2.5
- Medium(middle)-  Maximum gain = 3.5
- High gain (right) -  Maximum gain = 4.5

-  WF(f) = 1/MTF(f)
-             for f<fcut
- WF(f) = 1/MTF(fcut) exp-kf 2
-             for f>fcut

- k - parameter describing the Gaussian
-       roll-off
-  fcut - cutoff frequency
- K, fcut -were bracketed from an analysis
-           of phantom data

## Profile through the lesion

- Lesion contrast improves with filtering

- no

- low

- med

- high

## Time-of-Flight : list-mode iterative reconstruction

- Time-of-Flight : list-mode iterative reconstruction

**no TOF**

**300 ps TOF**

**1 Mcts**

**5 Mcts**

**10 Mcts**

## Challenges in comparing images

-  Spatial resolution differences						partial volume - simple (approximate) correction		spatial recovery in reconstruction model adds noise
-  Reconstruction algorithm 						local convergence depends on algorithm and activity
-  Accuracy of corrections  - randoms, scatter, attenuation		depends on patient size and activity distribution
-  Imaging protocol								scan acquisition time and delay post-injection
-  Quantification - typically based on simple cylinder		QC - monitor and correct daily drifts				Activity calibration - counts/voxel/min -> nCi/ml		Count-rate corrections - dead-time

## Challenges in comparing images

-  Instrumentation in PET is constantly evolving			performance of new scanner >> older scanner
-  Image data size is large	- data transfer and archiving		PET: 4 Mbyte (with 4 mm3 voxels)				CT: 64 Mbyte (with ~1 mm3 voxels)
-  DICOM									quantification (SUV) requires PT format (not NM)		manufacturers workstations still most practical
-  Data analysis tools must be standardized and validated		region-of-interest
-  Image processing								behavior must be understood - difficult to standardize