The Mathematics of Diffusion

The Mathematics of Diffusion,
by John Crank.

Diffusion is one of those topics that is rarely covered in an introductory physics class, but is essential for understanding biology. In the 4th edition of Intermediate Physics for Medicine and Biology, Russ Hobbie and I discuss diffusion and its biomedical applications. One of the books we cite is The Mathematics of Diffusion by John Crank. Hard-core mathematical physicists who are interested in biology and medicine will find Crank’s book to be a good fit. Physiologists who want to avoid as much mathematical analysis as possible may prefer to learn their diffusion from Random Walks in Biology, by Howard Berg.
Crank died five years ago this week. Like Wilson Greatbatch, who I discussed in my last blog entry, Crank was one of those scientists who came of age serving in the military during World War Two (Tom Brokaw would call them members the “Greatest Generation”). Crank’s 2006 obituary in the British newspaper The Telegraph states:

John Crank was born on February 6 1916 at Hindley, Lancashire, the only son of a carpenter’s pattern-maker. He studied at Manchester University, where he gained his BSc and MSc. At Manchester he was a student of the physicist Lawrence Bragg, the youngest-ever winner of a Nobel prize, and of Douglas Hartree, a leading numerical analyst.

Crank was seconded to war work during the Second World War, in his case to work on ballistics. This was followed by employment as a mathematical physicist at Courtaulds Fundamental Research Laboratory from 1945 to 1957. He was then, from 1957 to 1981, professor of mathematics at Brunel University (initially Brunel College in Acton).

Crank published only a few research papers, but they were seminal. Even more influential were his books. His work at Courtaulds led him to write The Mathematics of Diffusion, a much-cited text that is still an inspiration for researchers who strive to understand how heat and mass can be transferred in crystalline and polymeric material. He subsequently produced Free and Moving Boundary Problems, which encompassed the analysis and numerical solution of a class of mathematical models that are fundamental to industrial processes such as crystal growth and food refrigeration.

Crank is best known for a numerical technique to solve equations like the diffusion equation, developed with Phyllis Nicolson and known as the Crank-Nicolson method. The algorithm has the advantage that it is numerically stable, which can be shown using von Neuman stability analysis. They published this method in a 1947 paper in the Proceedings of the Cambridge Philosophical Society

Crank, J., and P. Nicolson (1947) “A Practical Method for Numerical Evaluation of Solutions of Partial Differential Equations of the Heat Conduction Type,” Proc. Camb. Phil. Soc., Volume 43, Pages 50–67.

Rather than describe the Crank-Nicolson method, I will let the reader explore it in a new homework problem.

Section 4.8

Problem 24 ½ The numerical approximation for the diffusion equation, derived as part of Problem 24, has a key limitation: it is unstable if the time step is too large. This problem can be avoided using the Crank-Nicolson method. Replace the first time derivative in the diffusion equation with a finite difference, as was done in Problem 24. Next, replace the second space derivative with the finite difference approximation from Problem 24, but instead of evaluating the second derivative at time t, use the average of the second derivative evaluated at times t and t+Δt.
(a) Write down this numerical approximation to the diffusion equation, analogous to Eq. 4 in Problem 24.
(b) Explain why this expression is more difficult to compute than the expression given in the first two lines of Eq. 4. Hint: consider how you determine C(t+Δt) once you know C(t).
The difficulty you discover in part (b) is offset by the advantage that the Crank-Nicolson method is stable for any time step. For more information about the Crank-Nicolson method, stability, and other numerical issues, see Press et al. (1992).

The citation is to my favorite book on computational methods: Numerical Recipes (of course, the link is to the FORTRAN 77 version, which is the edition that sits on my shelf).

Wilson Greatbatch (1919-2011)

This week we lost a giant of engineering: Wilson Greatbatch, inventor of the implantable cardiac pacemaker.

The cardiac pacemaker represents one of the most important contributions of physics and engineering to medicine. In Chapter 7 of the 4th edition of Intermediate Physics for Medicine and Biology, Russ Hobbie and I describe the pacemaker.

Cardiac pacemakers are a useful treatment for certain heart diseases [Jeffrey (2001); Moses et al. (2000); Barold (1985)]. The most frequent are an abnormally slow pulse rate (bradycardia) associated with symptoms such as dizziness, fainting (syncope), or heart failure. These may arise from a problem with the SA node (sick sinus syndrome) or with the conduction system (heart block)….

A pacemaker can be used temporarily or permanently. The pacing electrode can be threaded through a vein from the shoulder to the right ventricle (transvenous pacing, Fig. 7.31) or placed directly in the myocardium during heart surgery.

Several years ago, I taught a class about pacemakers and defibrillators as part of Oakland University’s honors college. The class was designed to challenge our top undergraduates, but not necessarily those majoring in science. Among the readings for the class was a profile in the March 1995 issue of IEEE Spectrum about Wilson Greatbatch (Volume 32, Pages 56-61). The article tells the story of Greatbatch’s first implantable pacemaker:

Greatbatch was on one team that had been summoned by William C. Chardack, chief of surgery at Buffalo’s Veteran’s Administration Hospital, to deal with a blood oximeter. The engineers could not help with that problem, but the meeting for the inventor was momentous: finally, after many previous attempts, he had met a surgeon who was enthusiastic about prospects for an implantable pacemaker. The surgeon estimated such a device might save 10000 lives a year.

Three weeks later, on May 7, 1958, the engineer brought what would become the worlds first implantable cardiac pacemaker to the animal lab at Chardack’s hospital. There Chardack and another surgeon, Andrew Gage, exposed the heart of a dog, to which Greatbatch touched the two pacemaker wires. The heart proceeded to beat in synchrony with the device, made with two Texas Instruments 910 transistors. Chardack looked at the oscilloscope, looked back at the animal, and said, “Well, I’ll be damned.”

Machines in Our Hearts,
by Kirk Jeffrey.

Another source the honors college students studied from was Kirk Jeffrey’s excellent book Machines in Our Hearts: The Cardiac Pacemaker, the Implantable Defibrillator, and American Health Care. Jeffrey tells the long history of how pacemakers and defibrillators were developed. In a chapter titled Multiple Invention of Implantable Pacemakers he describes Greatbatch’s contributions as well as others, including Elmqvist and Senning in Sweden. Jeffrey writes

If theirs [Chardack and Greatbatch] was not the only pacemaker of the 1950s, it appears to be the only one that survives today in the collective memory of the community of physicians, engineers, and businesspeople whose careers are tied to the pacemaker… The Chardack-Greatbatch pacamaker stood out from other prototype implantables of the late 1950s not because it was first or clearly a better design, but because it succeeded in the U.S. market as did no other device.

Jeffrey also discusses at length Greatbatch’s contributions to developing the lithium battery.

Because of his prestige in the pacing community and his effectiveness as a champion of technology be believed in, Greatbatch was able almost single-handedly to turn the industry to lithium; in fact by 1978, a survey of pacing practices indicated that only 5 percent of newly implanted pulse generators still used mercury-zinc batteries.

Greatbatch was inducted into the National Inventor’s Hall of Fame in 1986. His citation says

Wilson Greatbatch invented the cardiac pacemaker, an innovation selected in 1983 by the National Society of Professional Engineers as one of the two major engineering contributions to society during the previous 50 years. Greatbatch has established a series of companies to manufacture or license his inventions, including Greatbatch Enterprises, which produces most of the world's pacemaker batteries.

Invention Impact

His original pacemaker patent resulted in the first implantable cardiac pacemaker, which has led to heart patient survival rates comparable to that of a healthy population of similar age.

Inventor Bio

Born in Buffalo, New York, Greatbatch received his preliminary education at public schools in West Seneca, New York. In 1936 he entered military service and served in the Atlantic and Pacific theaters during World War II. He was honorably discharged with the rating of aviation chief radioman in 1945. He attended Cornell University and graduated with a B.E.E. in electrical engineering in 1950. Greatbatch received a master's from the State University of New York at Buffalo in 1957 and was awarded honorary doctor's degrees from Houghton College in 1970 and State University of New York at Buffalo in 1984. Although trained as an electrical engineer, Greatbatch has primarily studied interdisciplinary areas combining engineering with medical electronics, agricultural genetics, the electrochemistry of pacemaker batteries, and the electrochemical polarization of physiological electrodes.

Below are some links related to Wilson Greatbatch that you might find useful.

An article about Greatbatch published by the Lemelson Center for the Study of Invention and Innovation: http://invention.smithsonian.org/centerpieces/ilives/lecture09.html

A video about Greatbatch produced by the Vega Science Trust: http://www.vega.org.uk/video/programme/248

Biography of Wilson Greatbatch on the Heart Rhythm Society website:
http://www.hrsonline.org/News/ep-history/notable-figures/wilsongreatbatch.cfm

New York Times obituary: http://www.nytimes.com/2011/09/28/business/wilson-greatbatch-pacemaker-inventor-dies-at-92.html

BBC obituary: http://www.bbc.co.uk/news/world-us-canada-15085056

A video honoring Wilson Greatbatch, the 1996 Lemelson-MIT Lifetime Achievement Award Winner.

Learn about Wilson Greatbatch, 1996 Lemelson-MIT Lifetime Achievement Award Winner.
https://www.youtube.com/embed/WLZBl118Ads

Optical Mapping

In Chapter 7 of the 4th edition of Intermediate Physics for Medicine and Biology, Russ Hobbie and I mention an optical technique that is used to measure the transmembrane potential in the heart.

Experimental measurements of the transmembrane potential often rely on the use of a voltage sensitive dye whose fluorescence changes with the transmembrane potential [Knisley et al. (1994); Neunlist and Tung (1995); Rosenbaum and Jalife (2001)].

This method, often called optical mapping, has revolutionized cardiac electrophysiology, because it allows you to use optical methods to make electrical measurements. If you want to learn more, take a look at the book Optical Mapping of Cardiac Excitation and Arrhythmias, by David Rosenbaum and Jose Jalife (2001). The chapters in this book were written by the stars of this field.

1. Optical Mapping: Background and Historical Perspective. Guy Salama.
2. Mechanisms and Principles of Voltage-Sensitive Fluorescence. Leslie M. Loew.
3. Optical Properties of Cardiac Tissue. William T. Baxter.
4. Optics and Detectors Used in Optical Mapping. Kenneth R. Laurita and Imad Libbus.
5. Optimization of Temporal Filtering for Optical Transmembrane Potential Signals. Francis X. Witkowski, Patricia A. Penkoske, and L. Joshua Leon.
6. Optical Mapping of Impulse Propagation within Cardiomyocytes. Herbert Windisch.
7. Optical Mapping of Impulse Propagation between Cardiomyocytes. Stephan Rohr and Jan P. Kucera.
8. Role of Cell-to-Cell Coupling, Structural Discontinuities, and Tissue Anisotropy in Propagation of the Electrical Impulse. André G. Kléber, Stephan Rohr, and Vladimir G. Fast.
9. Optical Mapping of Impulse Propagation in the Atrioventricular Node: 1. Todor N. Mazgalev and Igor R. Efimov.
10. Optical Mapping of Impulse Propagation in the Atrioventricular Node: 2. Guy Salama and Bum-Rak Choi.
11. Optical Mapping of Microscopic Propagation: Clinical Insights and Applications. Albert L. Waldo.
12. Mapping Arrhythmia Substrates Related to Repolarization: 1. Dispersion of Repolarization. Kenneth R. Laurita, Joseph M. Pastore, and David S. Rosenbaum.
13. Mapping Arrhythmia Substrates Related to Repolarization: 2. Cardiac Wavelength. Steven Girouard and David S. Rosenbaum.
14. Video Imaging of Cardiac Fibrillation. Richard A. Gray and José Jalife.
15. Video Mapping of Spiral Waves in the Heart. William T. Baxter and Jorge M. Davidenko.
16. Video Imaging of Wave Propagation in a Transgenic Mouse Model of Cardiomyopathy. Faramarz Samie, Gregory E. Morley, Dhjananjay Vaidya, Karen L. Vikstrom, and José Jalife.
17. Optical Mapping of Cardiac Arrhythmias: Clinical Insights and Applications. Douglas L. Packer.
18. Response of Cardiac Myocytes to Electrical Fields. Leslie Tung.
19. New Perspectives in Electrophysiology from The Cardiac Bidomain. Shien-Fong Lin and John P. Wikswo, Jr..
20. Mechanisms of Defibrillation: 1. Influence of Fiber Structure on Tissue Response to Electrical Stimulation. Stephen B. Knisley.
21. Mechanisms of Defibrillation: 2. Application of Laser Scanning Technology. Stephen M. Dillon.
22. Mechanisms of Defibrillation: 3. Virtual Electrode-Induced Wave Fronts and Phase Singularities; Mechanisms of Success and Failure of Internal Defibrillation. Igor R. Efimov and Yuanna Cheng.
23. Optical Mapping of Cardiac Defibrillation: Clinical Insights and Applications. Douglas P. Zipes.

For those who are tired of reading, two videos have recently been published in the Journal of Visualized Experiments that explain the technique step-by-step. One video is about studying the rabbit heart and the other is about the mouse heart. These excellent video clips were filmed in the laboratory of Igor Efimov, of Washington University in Saint Louis.

My former graduate student, Debbie Janks, is now a post doc in Efimov’s lab. Regular readers of this blog may recognize Janks’ name, as she provides many insightful comments following these blog entries. Janks studied optical mapping from a theoretical perspective when she was here at Oakland University. She published a nice paper that examined the question of averaging over depth during optical mapping. The optical method does not measure the transmembrane potential at the tissue surface. Rather, light penetrates some distance into the tissue, and the optical signal is a weighted average of the transmembrane potential over depth. Janks looked at the effect of this averaging during an electrical shock. Rather than explaining the whole story, I will present it as a new homework problem. That way, you can figure it out for yourself. Enjoy.

Section 7.10

Problem 47 1/2 The signal measured during optical mapping, V, is a weighted average of the transmembrane potential, V_m(z), as a function of depth, V=∫₀^∞V_m(z)w(z)dz, where w(z) is a normalized weighting function. Suppose the light decays with depth exponentially, with an optical length constant δ. Then w(z) = exp(−z/δ)/δ. Often a shock will cause V_m(z) to fall off exponentially with depth, V_m(z)=V_o exp(−z/λ), where V_o is the transmembrane potential at the tissue surface and λ is the electrical length constant (see Sec. 6.12).
(a) Perform the required integration to find an analytical expression for the optical signal, V, as a function of V_o, δ and λ.
(b) What is V in the case δ much less than λ? Explain this result physically.
(c) What is V in the case δ much greater than λ? Explain this result physically.
(d) For which limit do you obtain an accurate measurement of the transmembrane potential at the surface, V=V_o?

In cardiac tissue, δ is usually on the order of a millimeter, whereas λ is more like a quarter of a millimeter, so averaging over depth significantly distorts the measured signal. For a more detailed analysis of this problem, see Janks and Roth (2002).

Does cell biology need physicists?

The American Physical Society has an online journal, Physics, with the goal of making recent research accessible to a wide audience. The journal website states:

Physics highlights exceptional papers from the Physical Review journals. To accomplish this, Physics features expert commentaries written by active researchers who are asked to explain the results to physicists in other subfields. These commissioned articles are edited for clarity and readability across fields and are accompanied by explanatory illustrations.

One recent paper that caught my eye was an essay written by Charles Wolgemuth, titled “Does Cell Biology Need Physicists?” Wolgemuth asks key questions in the introduction to his essay.

The past has shown that cell biologists are extremely capable of making great progress without much need for physicists (other than needing physicists and engineers to develop many of the technologies that they use). Do the new data and new technological capabilities require a physicist’s viewpoint to analyze the mechanisms of the cell? Is physics of use to cell biology?

Later in the essay, Wolgemuth asks his central question in a more specific way:

It is possible that the physics that cells must deal with is slave to the reactions; i.e., the protein levels and kinetics of the biochemical reactions determine the behavior of the system, and any physical processes that a cell must accomplish are purely consequences of the biochemistry. Or, could it be that cellular biology cannot be fully understood without physics?

Readers of the 4th edition of Intermediate Physics for Medicine and Biology are likely to scream “Yes!” to these questions. I too enthusiastically answer yes, but I agree with Wolgemuth that it is proper to ask such basic questions occasionally.

I should add that Russ Hobbie and I tend to look primarily at macroscopic phenomena in Intermediate Physics for Medicine and Biology, such as the biomechanics of walking with a cane, the interpretation of an electrocardiogram, or the algorithm required to form an image of the brain using a CAT scan. We occasionally look at events on the atomic scale, but for the most part we ignore molecular biophysics. Yet, the cellular scale is an interesting intermediate level that is becoming a fertile field for the applications of physics to biology. Indeed, I examined this issue when discussing the textbook Physical Biology of the Cell last year in this blog. The discussion that Russ and I give to fluid dynamics, diffusion, and bioelectricity in Intermediate Physics for Medicine and Biology is relevant to cellular topics.

To answer his question, Wolgemuth provides five examples in which physics provides key insights into cellular biology: 1) Molecular motors, 2) Cellular movement, 3) How cells swim, 4) Cell growth and division, and 5) How cells interact with the environment. One of my favorite parts of the essay is the consideration of potential pitfalls for physicists in biology.

Fifteen years ago, around the time that I began working in biophysics, there were very few collaborations between physicists and cell biologists, especially if the physicists were theorists. Theory was, and still is to a good degree, a word that should be avoided in the presence of biologists. Those of us who use math and computers to try to understand how cells work tend to call ourselves modelers instead of theorists. My suspicion is that many of the first physicists and mathematicians who tried to develop models for how biology works attempted to be too abstract or too general. As physicists we like to try to find universal laws, and though there are undoubtedly general principles at play in cell biology, it is likely that there are no real universal rules. Evolution need not find only one way to do something but more often probably finds many. Rather than search out generalities, we will serve biology better if we deal with specifics. As Aharon Katchalsky, who is largely credited with bringing nonequilibrium thermodynamics to biology, purportedly said, “It is easier to make a theory of everything than a theory of something.”

In recent years, physicists have done a much better job at addressing specific problems in biology. However, there still remains a divide between the two communities. Indeed, good physical biology that comes out of the physics community often goes unnoticed or is under appreciated. The burden falls on us to properly convey our work so as to be accessible to biologists. We need to make conscious efforts at communication and dissemination of our results. Two useful approaches toward this end are to publish in broader audience journals that reach both communities, and for papers that contain theoretical analyses to provide a qualitative description of the modeling in the main text, while leaving the more mathematical details for the appendices or supplemental material (for further discussion of this topic, see Ref. [55]). It is also of prime importance to maintain and to forge new connections between physicists and biologists.

Wolgemuth comes closest to answering his own questions near the end of the essay, where he predicts

To be truly successful, we must provide an understanding of biology that spans the gorge from biochemistry and genetics to cellular function, and do it in such a way that our models and experiments are not only informative about physics, but directly impact biology.

Cell biology is awaiting these descriptions. And it may be that physicists are the most able to draw these connections between the protein level description of cellular biology that currently dominates and a more intuitive, yet still quantitative, description of the behavior of cells and their responses to their environments.

Radon Transform

In Chapter 12 of the 4th Edition of Intermediate Physics for Medicine and Biology, Russ Hobbie and I introduce the Radon transformation. It consists of finding the projections F(θ, x') at different angles θ from a function f(x,y). But why is it called the “Radon” transformation, and does it have anything to do with the radioactive gas radon discussed in Chapter 16?

Well, it has nothing to do with the element radon. Instead, and predictably, the term honors Johann Radon, the Austrian mathematician who investigated this transformation. In “A Tribute to Johann Radon” in the IEEE Transactions on Medical Imaging (Volume 5, Page 169, 1986, reproduced long after his death to honor his memory) Hans Hornich wrote

With the death in Vienna on 25 May 1956 of Dr. Johann Radon, Professor of the University of Vienna, not only the mathematical world and Austrian science but also the German Mathematical Union has suffered a severe loss, as have also many other scientific bodies of which the deceased was a prominent member, and who spent most of his teaching life in German universities.

Radon was born in the small town of Tetschen in Bohemia near the border of Saxony on December 16, 1887. He studied at Vienna University where, alongside Mertens and Wirtinger, Escherisch above all was the great influence on Radon’s development: Escherisch had, as one of the first in Austria, imparted to his students the world of ideas of Weierstrass and his rigorous foundations of analysis. Through Escherich, Radon was led next to variational calculus….

A few years later appeared his “Habilitationsschrift” “Theory and application of absolute additive weighting functions” (S. Ber. math. naturw., Kl. K. Akad. Wiss. Wien II Abt., vol. 122, pp. 1295–1438, 1913), which played a leading role in the development of analysis; the Radon integral and the Radon theorem laid the foundations of functional analysis. As an application Radon somewhat later treated the first and second boundary value problem of the logarithmic potential in a very general way.

The Radon transformation has important applications in medical imaging, and plays a crucial role in computed tomography, positron emission tomography, and single photon emission tomography. I found a nice layman’s description of the Radon transform in an essay at the website http://www.ams.org/samplings/feature-column/fcarc-tomography, written by Bill Casselman.

The original example of this sort of technology [involving a collaboration between medicine and mathematics], and the ancestor of many of these technologies, is what is now called computed tomography, for which Allan Cormack, a physicist whose research became more and more mathematical as time went on, laid down the theoretical foundations around 1960. He shared the 1979 Nobel prize in medicine for his work in this field.

In fact the basic idea of tomography had been discovered for purely theoretical reasons in 1917 by the Austrian mathematician Johann Radon, and it had been rediscovered several times since by others, but Cormack was not to know this until much later than his own independent discovery. The problem he solved is this: Suppose we know all the line integrals through a body of varying density. Can we reconstruct the body itself? The answer, perhaps surprisingly, is that we can, and furthermore we can do so constructively. In practical terms, we know that a single X-ray picture can give only limited information because certain things are obscured by other, heavier things. We might take more X-ray pictures in the hope that we can somehow see behind the obscuring objects, but it is not at all obvious that by taking a lot—really, a lot—of X-ray pictures we can in effect even see into objects, which is what Radon tells us, at least in principle. Making Radon’s theorem into a practical tool was not a trivial matter.”

You can listen to a lecture on tomography and inverting the Radon transform here.

Fraunhofer Diffraction

Last week’s blog entry discussed Fresnel diffraction, which Russ Hobbie and I analyzed in the 4th edition of Intermediate Physics for Medicine and Biology when we examined the ultrasonic pressure distribution produced near a circular piezoelectric transducer. This week, I will analyze diffraction far from the wave source, known as Fraunhofer diffraction, named for the German scientist Joseph von Fraunhofer.

The mathematics of Fraunhofer diffraction is a bit too complicated to derive here, but the gist of it can be found by inspecting the first equation in Section 13.7 (Medical Uses of Ultrasound), found at the bottom of the left column on page 351. The pressure is found by integrating 1/r times a cosine function over the transducer face. When you are far from the transducer, r is approximately a constant and can be taken out of the integral. In that case, you just integrate cosine over the transducer area. This becomes similar to the two-dimensional Fourier transform defined in Chapter 12 (Images). The far field pressure distribution produced by a circular transducer given in Eq. 13.40 is the same Bessel function result as derived in Problem 10 of Chapter 12.

The intensity distribution in Eq. 13.40 is known as the Airy pattern, after the English scientist and mathematician George Biddell Airy. As shown in Fig. 13.15, the pattern consists of a central peak, surrounded by weaker secondary maxima. The Airy pattern occurs during imaging using a circular aperture, such as when viewing stars through a telescope. Two adjacent stars appear as two Airy patterns. Distinguishing the two stars is difficult unless the separation between the images is greater than the separation between the peak of the Airy pattern and its first zero. This is called the Rayleigh criterion, after Lord Rayleigh. Rayleigh (1842–1919, born John William Strutt)—one of those 19th century English Victorian physicists I like so much—did fundamental work in acoustics, and published the classic textbook Theory of Sound.

Fresnel Diffraction

In Section 13.7 of the 4th edition of Intermediate Physics for Medicine and Biology, Russ Hobbie and I discuss the medical uses of ultrasound. One important problem we analyze is the pressure distribution produced by a piezoelectric transducer.

There are some important features of the radiation pattern from a transducer which we review next. Consider a circular transducer, the surface of which is oscillating back and forth in a fluid… Each small element of the vibrating fluid creates a wave that travels radially outward, the points of constant phase being expanding hemispheres. The amplitude of each spherical wave decreases as 1/r, the intensity falling as 1/r². We want the pressure at a point z on the axis of the transducer. It is obtained by summing up the effect of all the spherical waves emanating from the face of the transducer….

The [average intensity] is plotted in Fig. 13.13 for a fairly typical but small transducer (a = 0.5 cm, f = 2 MHz)... Close to the transducer there are large oscillations in intensity along the axis: there are corresponding oscillations perpendicular to the axis, as shown in Fig. 13.14. The maxima and minima form circular rings. This is called the near field or Fresnel zone… The depth of the Fresnel zone is approximately a²/λ [where a is the radius of the transducer, λ is the wavelength, and f is the frequency].

Fig, 13.13.

The calculated intensity along the axis, as shown in our Fig. 13.13, is interesting. In the Fresnel zone, the intensity has many points where it is zero. In Intermediate Physics for Medicine and Biology we calculate why this happens mathematically, but it is illuminating to describe what is happening physically. Basically, this is a result of wave interference. Our statement that “each small element of the vibrating fluid creates a wave that travels radially outward” is often called Huygens principle. Each point on the face of the transducer produces such a wavelet. To understand the pressure distribution, we must examine the phase relationship among these various wavelets. Very near the face of the transducer, the waves that contribute significantly to the pressure are in phase; they all interfere constructively and you get a maximum (evaluate Eq. 13.39 at z = 0 and you get a nonzero constant). However, as you move away, more distant points on the transducer face contribute to the pressure on the axis, and these points may be out of phase with the pressure produced by the point at the center. For some value of z the in-phase and out-of-phase wavelets interfere destructively, resulting in zero intensity. Increase z a little more, and not only do the in-phase points at the center and the out-of-phase points just away from the center contribute to the pressure, but so do some in-phase points even farther from the center. When you add it all up, you get a net constructive interference and a non-zero intensity. And so it goes, as you move out farther and farther along the z axis.

Fig. 13.14.

The radial distribution of the intensity is surprisingly rich and complex, given the rather simple integral that underlies the behavior. If you want to explore the radial distribution in more detail, go to the excellent website http://wyant.optics.arizona.edu/fresnelZones/fresnelZones.htm, where you can perform these calculations yourself. You can adjust the parameters as you wish and create plots such as those in Fig. 13.14, and also produce grayscale images of the full intensity distribution that provide much insight. The website was produced with optics in mind, so you have to put in strange looking parameters to model ultrasound. To reproduce the middle panel of Fig 13.14, input 770,000 for the wavelength in nm, 10,000 for the aperture diameter in microns, and 15.75 for the observation distance in mm. To my eye, the agreement between the website’s calculation and Fig. 13.14 is impressive. At small values of z the plots get very complex and beautiful. For the same wavelength and aperture, I like the richness of z = 5 mm, and for z = 4 mm you get a fairly uniform brightness except for a dramatic dark spot right at the center. It reminds me of Poisson’s spot, which I discussed in the September 17, 2010 entry in this blog, about Augustin-Jean Fresnel. Indeed, the physics behind the calculations in Fig. 13.14 and Poisson’s spot in optics are nearly identical. The circular aperture is a classic problem in Fresnel diffraction. You can find a more detailed discussion of this topic in the textbook Optics (4th edition), by Eugene Hecht. (My bookshelf contains the first edition, by Hecht and Zajac, that I used in my undergraduate optics class at the University of Kansas).

If you want to be clever, you could make the ultrasound transducer vibrate only at those radii that result in constructive interference along the axis, and have it remain stationary at radii that cause destructive interference. (Of course, this would mean you would have to design your transducer face cleverly so concentric rings vibrate, separated by rings that do not, which might make constructing the transducer more difficult.) Using such a trick eliminates the dark spots along the z axis, increasing the intensity there. This method is commonly used to focus light waves, and is called a zone plate. It has been used occasionally with ultrasound.

The Nonlinear Poisson-Boltzmann Equation

Last week’s blog entry was about the Gouy-Chapman model for a charged double layer at an electrode surface. The model is based on the Poisson-Boltzmann equation (Eq. 9.10 in the 4th edition of Intermediate Physics for Medicine and Biology). One interesting feature of the Poisson-Boltzmann equation is that it is nonlinear. In applications when the thermal energy of ions in solution is much greater than the energy of the ions in an electrical potential, the equation can be linearized (Eq. 9.13). That is not always the case.

Homework problem 9 in Chapter 9 of Intermediate Physics for Medicine and Biology was added in the 4th edition. It begins

Problem 9 Analytical solutions to the nonlinear Poisson-Boltzmann equation are rare but not unknown. Consider the case when the potential varies in one dimension (x), the potential goes to zero at large x, and there exists equal concentrations of monovalent cations and anions. Chandler et al. (1965) showed that the solution to the 1-d Poisson-Boltzmann equation, d²ζ/dx²=sinh(ζ), is…

You will need to get a copy of the book to see this lovely solution. It is a bit too complicated to write in this blog, but it involves the exponential function, the hyperbolic tangent function, and the inverse hyperbolic tangent function. I like this homework problem, because you can solve both the nonlinear and linear equations exactly, with the same boundary conditions, and compare them to get a good intuitive feel for the impact of the nonlinearity. I admit, the problem is a bit advanced for an intermediate-level book, but upper-level undergraduates or graduate students studying from our text should be up to the challenge.

The full citation to the paper by Knox Chandler, Alan Hodgkin, and Hans Meves mentioned in the problem is

Chandler, W. K., A. L. Hodgkin, and H. Meves (1965) “The Effect of Changing the Internal Solution on Sodium Inactivation and Related Phenomena in Giant Axons,” Journal of Physiology, Volume 180, Pages 821–836.

I always thought it odd that one finds a really elegant analytical solution to the nonlinear Poisson-Boltzmann equation in a paper about sodium channel inactivation in a squid nerve axon (with Nobel Prize-winning physiologist Alan Hodgkin as a coauthor). The solution is buried in the discussion (in a section set of in a smaller font than the rest of the paper). The reason for its appearance is that Chandler et al. found changes in membrane behavior with intracellular ion concentration, and postulated that the measured voltage drop between the inside and outside of the axon consisted of a voltage drop across the membrane itself (which affects the ion channel behavior) and a voltage drop within a double layer adjacent to the membrane. It is the double layer voltage that they model using the Poisson-Boltzmann equation.

Nowadays, the nonlinear Poisson-Boltzmann equation is typically solved using numerical methods. See, for example, the paper that Russ Hobbie and I cite in Intermediate Physics for Medicine and Biology, written by Barry Honig and Anthony Nicholls: “Classical Electrostatics in Biology and Chemistry,” Science, Volume 268, Pages 1144–1149, 1995 (it now has over 1500 citations in the literature). Their abstract states

A major revival in the use of classical electrostatics as an approach to the study of charged and polar molecules in aqueous solution has been made possible through the development of fast numerical and computational methods to solve the Poisson-Boltzmann equation for solute molecules that have complex shapes and charge distributions. Graphical visualization of the calculated electrostatic potentials generated by proteins and nucleic acids has revealed insights into the role of electrostatic interactions in a wide range of biological phenomena. Classical electrostatics has also proved to be a successful quantitative tool yielding accurate descriptions of electrical potentials, diffusion limited processes, pH-dependent properties of proteins, ionic strength-dependent phenomena, and the solvation free energies of organic molecules.

Such calculations continue to be an active area of research. See, for example, “The Role of DNA Shape in Protein-DNA Recognition” by Remo Rohs, Sean West, Alona Sosinsky, Peng Liu, Richard Mann and Barry Honig (Nature, Volume 461, Pages 1248–1253, 2009).

Gouy and Chapman

Sometimes when I am studying physics, I run across a model or equation with names attached to it, and I wonder “just who are these people?” For example, in Chapter 9 of the 4th edition of Intermediate Physics for Medicine and Biology, Russ Hobbie and I discuss the Gouy-Chapman model.

In this section we study one model for how ions are distributed at the interface in Donnan equilibrium. The model was used independently by Gouy and by Chapman to study the interface between a metal electrode and an ionic solution. They investigated the potential changes along the x axis perpendicular to a large plane electrode. The same model is used to study the charge distribution in a semiconductor.

So who are Gouy and Chapman? I can tell they lived a long time ago, because in the next section we write “In an ionic solution, ions of opposite charge attract one another. A model of this neutralization was developed by Debye and Huckel a few years after Gouy and Chapman developed [their] model.” I know Peter Debye worked in the early days of quantum mechanics, so Gouy and Chapman had to be a bit earlier.

As if often the case with scientific developments in the 19th century, a Frenchman and an Englishman share credit for the discovery. Louis Georges Gouy (1854–1926) was a French experimental physicist from the University of Lyon. He is best known as the inventor of the Gouy balance, a device for measuring magnetic susceptibility. Gouy had an interest in Brownian motion at a time when the atomic nature of matter was still an open question. Russ and I discuss Brownian motion in Section 4.3 of our book.

[The] movement of microscopic-sized particles, resulting from bombardment by much smaller invisible atoms, was first observed by the English botanist Robert Brown in 1827 and is called Brownian motion.

Albert Einstein wrote a fundamental paper on Brownian motion in 1905, his miraculous year. In a subsequent paper on the same topic, Einstein began (Annalen der Physik, Volume 19, Pages 371–381, 1906)

Soon after the appearance of my paper on the movements of particles suspended in liquids demanded by the molecular theory of heat, Siedentopf (of Jena) informed me that he and other physicists—in the first instance, Prof. Gouy (of Lyons)—had been convinced by direct observation that the so-called Brownian motion is caused by the irregular thermal movements of the molecules of the liquid.

Gouy’s model sought to explain the electrical potential around small Brownian particles, or colloids. He described the double layer of charge that develops at the surface, with one charge layer bound to the surface of the particle, and a layer of counterions in the surrounding fluid.

David Leonard Chapman (1869–1958) was an English physical chemist at Oxford. He was interested in the theory of detonation in gasses, and developed the Chapman-Jouget condition describing their behavior. About three years after Gouy, Chapman derived a model describing the double layer at a charged surface. The Gouy-Chapman model is an application of what is now known as the Poisson-Boltzmann equation (Eq. 9.13 in our book).

In 1924, physicist Otto Stern extended the Gouy-Chapman model by noting that ions can’t be represented as point charges when they are within a few ion radii of the surface. This leads to the Stern layer of immobile counter-ions right next to the surface, and a diffuse layer of counter-ions whose concentration decays exponentially.

Fisher-Kolmogorov equation

Mathematical Biology,
by James Murray.

In the 4th edition of Intermediate Physics for Medicine and Biology, Russ Hobbie and I discuss many of the important partial differential equations of physics, such as Laplace’s equation, the diffusion equation, and the wave equation. One lesser-known PDE that we don’t discuss is the Fisher-Kolmogorov equation. However, our book supplies most of what you need to understand this equation.

In Section 2.10, we examine the logistic equation, an ordinary differential equation governing population growth,

du/dt = b u (1 − u) .

For u much less than one, the population grows exponentially with rate b. As u approaches one, the population levels off near a steady state value of u = 1. Our Eq. 2.28 gives an analytical solution to this nonlinear equation.

In Section 4.8, we drive the diffusion equation, which for one dimension is

du/dt = D d²u/dx² .

This linear partial differential equation is one of the most famous in physics. It describes diffusion of particles, and also the flow of heat by conduction. D is the diffusion constant.

To get the Fisher-Kolmogorov equation, just put the logistic equation and the diffusion equation together:

du/dt = D d²u/dx² + b u (1 − u) .

The Fisher-Kolmogorov equation is an example of a “reaction-diffusion equation.” Russ and I discuss a similar reaction-diffusion equation in Homework Problem 24 of Chapter 4, when modeling intracellular calcium waves. The only difference is that we use a slightly more complicated reaction term rather than the logistic equation.

Mathematical Biology,
by James Murray.

In his book Mathematical Biology, James Murray discusses the Fisher-Kolmogorov equation in detail. He states

The classic simplest case of a nonlinear reaction diffusion equation … is [The Fisher-Kolmogorov equation]… It was suggested by Fisher (1937) as a deterministic version of a stochastic model for the spatial spread of a favoured gene in a population. It is also the natural extension of the logistic growth population model discussed in Chapter 11 when the population disperses via linear diffusion. This equation and its travelling wave solutions have been widely studied, as has been the more general form with an appropriate class of functions f(u) replacing ku(1−u). The seminal and now classical paper is that by Kolmogoroff et al. (1937)…. We discuss this model equation in the following section in some detail, not because in itself it has such wide applicability but because it is the prototype equation which admits travelling wavefront solutions. It is also a convenient equation from which to develop many of the standard techniques for analyzing single-species models with diffusive dispersal.”

The Fisher-Kolmogorov equation was derived independently by Ronald Fisher (1890–1962), an English biologist, and Andrey Kolmogorov (1903–1987), a Russian mathematician. The key original papers are

Fisher, R. A. (1937) “The Wave of Advance of Advantageous Genes.” Annals of Eugenics, Volume 7, Pages 353–369.

Kolmogoroff, A., I. Petrovsky, and N. Piscounoff (1937) “Etude de l’equation de la diffusion avec croissance de la quantite de matiere et son application a un probleme biologique.” Moscow University Mathematics Bulletin, Volume 1, Pages 1–25.