Analogue AdderDigital Adder

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2011.11.28.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 1

Medical diagnostic systems

B-mode imaging components

(Orvosbiológiai képalkotó rendszerek)

( B-mód képalkotás összetevői)

Miklós Gyöngy

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The origins: pulse-echo ranging

[Szabo 2004, pp. 1-12]

Sonar: SOund NAvigation and Ranging

– Titanic disaster (1912)

– Anti-submarine warfare (1916-)

Radar: RAdio Detection and Ranging

– Tesla (1917)

• Early experiments in medical

ultrasound came from equipment and experience in above two fields

• Ranging (distance measurement based on time of arrival information) relies on relatively constant speed of sound

“Hurricane Abby approaching the coast of British Honduras” NOAA Photo Library, http://www.photolib.noaa.gov/htmls/wea01 219.htm

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The origins: using an oscilloscope

• Echo returning from transmission observed on oscilloscope

• Amplitude-mode (A-mode): traditional oscilloscope display

• Brightness-mode (B-mode): display envelope of each A-line on top of each other

time ↔ longitudal distance received

voltage

transverse distance

Multiple reflections from a boundary. Left: A-line. Right: B-mode image

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The role of technology

[Szabo 2004, pp. 16-20]

Advances in transducers

– piezoelectricity (Curie brothers, 1880) – mass, reproducible manufacture

– miniaturization (e.g. MEMS) Advances in electronics

– application-specific integrated circuit (ASIC) – digital signal processors (DSP)

– very large scale integration (VLSI)

– move towards digitization (beamforming, TGC) – reduced cost of digital storage

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Pulse-echo pathway (A-line)

Waveform generator

Transmit

Beamformer Amplifier

Transducer elements Multiplexer

Receive Beamformer

Envelope Detection Time-Gain

Compensation

acoustic medium Transmit/ Receive Switch

(micro-coaxial cable)

DAC (if digital beamformer)

ADC (later if analogue beamformer)

Scan Conversion

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(compression, downsampling,

projection)

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User control/access Transmission

• Typical commercial system:

• choose imaging depth (determines focus)

• choose frequency (determines waveform)

• Research system: arbitrary transmission

Reception

• Typical commercial system:

• access to bitmap screen grab

• access to post-beamformed RF data (maybe!)

• Research system: pre-beamformed channel RF

panel of imaging parameters on the z.one ultrasound system (ZONARE Medical Systems)

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Needs for user control/access

Clinician:

• basic parameters (resolution, depth) Researcher of registration/segmentation

• ideally post-beamformed (BF) data Researcher of new imaging modalities:

• some research possible with BF data (e.g.

estimation of acoustic parameters)

• ideally, total control over imaging parameters

• calibration of transmitted and received signal for quantitative studies

panel of imaging parameters on a z.one ultrasound system (ZONARE Medical Systems)

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Ultrasound systems for research use

Commerical (C)/ Channel data (C)/

Name Purpose-built (P) Post-beamformed (P) Other options

Antares (Siemens) C P

DiPhAS

(IBMT,Fraunhofer)

LeCoeur (OPEN) C C arbitrary transmission

RASMUS (DTU) P C arbitrary transmission

SonixTouch (Ultrasonix) C C imaging parameters

SONOS 500 URP

(Agilent + U. Virginia) C/P C

SITAU FP (Dasel) C C programmable width transmission

t3000 (Terason) C P arbitrary apodization, focal depth

ULA-OP (U. Florence) P C arbitrary transmission

z.one ZONARE C C (on request) arbitrary transmission

[Tortoli et al. 2009; Wilson et al. 2006]

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Transmit/Receive switch

• Implementations:

– diode

– transmission line (frequency selective)

• Transmission: ~10 V; Reception: ~mV Some leakage will always occur

• Receive circuitry needs to be resistant to saturation blinding (especially from matching layer)

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Multiplexing

• Reduction of complexity

• Maintain fixed subaperture during linear scan

i+128 (if 192 elements) channel i

element i

element i+64 MUX

• Shifting of subaperture during linear scan:

(1,2,...64), (65,2,...,64), (65,66,3,...,64), etc.

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Time-gain compensation (TGC)

[Brunner 2002]

• Diffraction loss relatively unimportant. Consider, in the worst case, spherically diverging Tx/Rx beams. Identical scatterer at 5 cm, 10 cm, causes -12 dB signal difference.

• Tissue attenuation ~1dB/MHz/cm. 5 MHz signal, 10 cm penetration depth, causes -100 dB loss.

• Linear-in-decibel variable-gain amplifiers (VGA) needed to for time-gain compensation (TGC)

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Frequency-shift compensation

[Szabo 2004, pp. 86-88]

• Tissue causes frequency-dependent attenuation

• Frequency peak of a Gaussian-modulated pulse shifts with distance (~1 MHz for 5 cm imaging depth, 50% fractional bandwidth)

• Depth-dependent compensation needed (but where in the signal processing pathway is it most appropriate?)

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adapted from [Brunner2002]

Analogue beamforming

Digital beamforming

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Analogue Adder

ADC

Array Variable

delays Output

signal

FIFO

FIFO FIFO

FIFO FIFO FIFO FIFO

Digital Adder

Focal Point

Array Variable

delays Output signal

ADC ADC ADC ADC ADC ADC ADC

Sampling clock

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Analogue beamforming

– Difficult to match channels across delay lines – Many delay taps needed or phase shifting

+ Only one ADC needed – can make it high-spec

Digital beamforming

– High cost of in-sync, fast (vs) high-resolution ADCs

– Large bit depth and sampling rate incur large storage and computational costs

+ Easier to program/configure

+ Novel implementations (e.g. several receive beams)

[Brunner 2002]

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ADC considerations

• Fast MHz applications, flash ADC is used (comparator for every signal level)

• Oversampling: sample at a higher rate, take average of values.

E.g. 10 bits at 100 MHz can generate 12-bit data at 25 MHz

• Sigma-delta processing: “pulse density modulation” – local density of 1s represents value (used both for ADC and DAC)

• IQ (in-phase/quadrature) modulation/demodulation

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IQ demodulation adapted from [Kirkhorn 1999]

f_s/2

(Nyquist frequency)

Bandwidth of interest B

f_c

(mixing frequency)

f

-2f_c f_s/2 f

B/2 Down-mixing

Low-pass filter (LPF)

IQ demodulation

1. Mix bandwidth of interest down to baseband

2. Apply LFP

3. Sample at reduced sample rate (less storage cost)

RF signal recovery

1. Upsample to original

sample rate (interpolation) 2. Remodulate by mixing

frequency f_c

-f_c

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IQ (in-phase/quadrature) data: interpretation

x_IQ = LPF{exp(-ω_ct) x_RF}=

LPF{cos(ω_ct) x_RF - jsin(ω_ct) x_RF}= x_I + jx_Q

• Express RF signal as sum of slowly varying signal i(t) modulating in-phase cosine oscillation and slowly varying q(t) modulating quadrature sinusoid

x_RF= i(t)cos(ω_ct) + q(t)sin(ω_ct) where i(t), q(t) are slowly varying

• IQ signal is then

x_IQ = 0.5 LPF{i(t)(1+cos(2ω_ct)-jsin(2ω_ct)) + q(t)(sin(2ω_ct)-j-jcos(2ω_ct))}

= 0.5 i(t) -0.5 jq(t)

• Low-pass filter removes ±2f_c

• Re{x_IQ} contains in-phase signal

• Im{x_IQ} contains quadrature signal

• |x_IQ| gives envelope

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IQ example: cosinusoid (in-phase) around t=0 µs, sinusoid (quadrature) around t=2 µs (both 3 cycles at 5 MHz)

-1 0 1 2 3

-1 0 1

-50 -40 -30 -20 -10 0 10 20 30 40 50 0

0.05 0.1

-1 0 1 2 3

-1 0 1

-50 -40 -30 -20 -10 0 10 20 30 40 50 0

0.05 0.1

-1 0 1 2 3

-0.5 0 0.5 1

time (µµµµ^s)

-10 0 10

0 0.05 0.1

frequency (MHz)

real component

imaginary component

signal power spectrum

RF signal

demodulated signal

IQ signal (after LPF) Note how IQ signal can be sampled at much lower rate!

real component I

imaginary component Q

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Envelope detection

• Take magnitude of x_IQ OR

• Hilbert transform H{.} of

reconstructed x_RF: 90º phase shift

• Analytic function of r(t):

x_RF(t) + j×H{x_RF(t)}

• In Matlab: abs(hilbert(r(t)))

(hilbert(.) actually generates analytic function!)

• In your own time: consider similarities between IQ and Hilbert transforms

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5 10 15 20

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

1 original signal

Hilbert transform (90°^delay) envelope

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-10 0 10

0

10

20

30

40

50

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70

80

-10 0 10

0

10

20

30

40

50

60

70

80

-10 0 10

0

10

20

30

40

50

60

70

80

im log(im) log(max(im,value))

Scan conversion

• Log compression for perception of large (~60 dB) dynamic range

• Threshold to reject noise

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References

[Brunner 2002] Ultrasound system considerations and their impact on front-end components

[Kirkhorn 1999] Introduction to IQ-demodulation of RF data.

http://folk.ntnu.no/htorp/Undervisning/TTK10/IQdemodulation.pdf [Szabo 2004] Diagnostic ultrasound imaging: Inside out

[Tortoli et al. 2009] ULA-OP: an advanced open platform for ultrasound research

[Wilson et al. 2006] The Ultrasonix 500RP: a commercial ultrasound research interface