loarake's comments

Whatever you do, don't Google "prostate brachytherapy" ;)

Don't quote me on this because I only covered the topic briefly in some applied classes before doing 6 years of research, but if I remember correctly, you grab it with long tweezers and dump it in a shielded "garbage can" type container. And fill tons of paperwork that involves estimating the radiation dose delivered to everyone that could've been exposed. And probably present a post mortem at conferences about how you dealt with it.

It's much more primitive than you think. Dose distributions are simulated based on a CT/MRI that was acquired before treatment (treatment often lasts weeks). Only minor corrections are made when anatomy changes during the course of treatment, even though the patient is often losing tons of weight due to chemo, etc. There are quite a few tools that help with patient positioning, like vac-lok bags or literally molding a mask and drilling it down on the treatment couch (an example is shown here: https://newsnetwork.mayoclinic.org/discussion/new-radiothera...).

Motion during treatment can be tracked with cameras or IR sensors or subcutaneous probes but that doesn't tell you about internal organs moving. The topic of deformable registration, where you find a non-rigid mapping between initial imaging conditions and the current ones, is still a topic of active research. Adaptive planning, where you actively change the treatment plan every N sessions based on the most up to date information, is also actively researched / implemented in some good research centers.

For treatment planning you just use a standard Cartesian grid, or a "beam's eye view" coordinate system that's aligned with the radiation beam axis as it rotates around the patient.

Just defended my PhD thesis in medical physics. Worked on radiation therapy treatment planning, which combines optimisation theory with the physics of Monte Carlo particle transport engines (and more macro energy deposition modeling as well) to simulate millions of different radiation dose distributions in patients and figure out which combination will lead to the right outcome based on what the radiation oncologist prescribes.

People in my field are fairly fortunate as there is a career track as a clinical medical physicist that is highly paid and pretty low stress, so most people end up going there. The work consists of maintaining and calibrating the radiation therapy machines, along with implementing new technologies in the clinic, and fixing problems that don't fall within the job description of the radiation therapists. Like what to do when a radioactive seed falls on the floor instead of going inside the patient where it's supposed to go. There's also a separate track as an imaging physicist where you maintain and QA the diagnostic imaging machines.

I'm personally doing a postdoc at the junction between optimisation, machine learning and radiation therapy. Just starting out though. Basically just extending my PhD work to automate the treatment planning process and remove the variability in treatment plan quality due to the level of experience of the people making the plans.

I want to echo point #2 about listening rather than talking. When I first pitched my work to radiation therapy vendors at big conferences, I was expecting a kind of adversarial exchange to take place where I'd have to defend my software against cynical people trying to find its flaws, shark-tank style.

Instead I found that vendors were dying to tell me what they need and what's important for them. I quickly realised that the most important part after giving my pitch was to basically ask tons of questions about what they think is important and why. The vendors' answers were invaluable in honing my pitch for other vendors, but also to steer the direction of my project.

-Working for a big tech company: you're writing code to deliver more ads to more people so the company can grow and deliver even more ads to even more people

-Being a truck driver: you're moving boxes around

-Being a professional hockey player: you and your team are moving really fast with a disk shaped object trying to put it in a net while people try to prevent you from doing it

-Being a stock trader: making money by spending your whole life reading company reports and hoping you're right about whether they're doing well or not

-Being a quant: using your hard earned computer science skills to move money around and turn a profit instead of helping humanity

Almost every human endeavor can be trivialized if you choose to only see one side of it.

I wish MUDs were featured more prominently in online gaming history. They basically defined my childhood, and I learned how to code by writing vendor bots on a MUD. Dealing with edge cases (customers trying to buy invisible items without being able to see invisible!) and people trying to scam my bots taught me some valuable programming lessons that stick with me to this day.

In general, the field is split into imaging research (MRI, CT, Ultrasound, optical imaging) and radiotherapy research. It's a very applied field, the fundamental physics has been figured out a long time ago except for some really niche areas. For example, some people are trying to model particle transport inside DNA itself at the nanometer scale, where the transported particles have very low energies. There's still some physics work to be done there, but it's pretty marginal and the lack of theory in those areas is mostly due to theoretical physicists losing interest rather than the theory being too difficult.

I personally work in radiotherapy, making simplified (faster) Monte Carlo particle transport algorithms for use in treatment planning, and also finding more "modern" optimisation techniques to handle the many degrees of freedom available on radiotherapy linear accelerators to produce higher quality treatment plans compared to what we can do right now. It's hard to define what a high quality treatment plan is without a lot of background, but basically we try to find ways to put more radiation in tumours while sparing the healthy tissue all around the tumour. My "research" is like 95% programming.

Medical Physics 4th year PhD student

Workday:

- [Variable] Wake up, shower, make coffee, read news.

- [10-11 AM] Take the subway to work (15 minutes)

- [11 AM to 5-6 PM] Work on Monte Carlo simulation or optimisation code / write paper if it's one of those days

- [6-9 PM] Eat dinner with GF while watching something on the computer

- [9 PM - 1 AM] Do some more work (most common option) or play piano or play Rocket League if some friends are around

People who go straight from undergrad to PhD often take 5+ years (in the states and Canada, at least), especially in the experimental fields

There's a cool dystopian-ey youtube video briefly looking into that topic, among other things: https://www.youtube.com/watch?v=YJg02ivYzSs

worked for me

I've had a similar experience. I'm in a hybrid field (medical physics) where half of the people are from pure physics and the other half from a more biomedical engineering background.

It's pretty funny to see the culture clash between the two when it comes to writing articles. We had to learn latex in our first year of undergrad to write lab reports whereas the biomed people really don't see any value in using latex over word (especially for collaborating).

If you're writing papers in LaTeX, then https://www.overleaf.com should be good for you. Almost all of our department has started using it.

He published a single author paper even though he acknowledges in the end "I thank Matthew Fiedler, PhD, and Jeanne Lambrew, PhD, who assisted with planning, writing, and data analysis". In my field, those people probably would have deserved a place on the authors list ;)

I'm not super familiar with computer graphics, so you'll have to let me know if my description fits what you guys do ;)

I found a youtube video (https://www.youtube.com/watch?v=msX1ypCjkK4) that should give you an idea of what I'm describing actually looks like. Specifically it introduces the concept of a multi-leaf collimator which serves as the main collimating device in modern radiotherapy. The other degree of freedom is the angle of the gantry you see rotating around the patient.

Typically for every gantry angle, the treatment planning software would split up an open field (no collimation) into a bunch of 1x1 cm^2 "beamlets" and would simulate what kind of dose distribution you would get inside the patient from each beamlet (you turn the patient CT into a big 3D grid of voxels to simulate dose in).

You then throw all those dose distributions into an optimiser, and you do what's called a fluence map optimisation which gives you the amount of radiation you want to deliver out of each beamlet. This is the optimisation step I described earlier where the cost function is basically a square difference between the dose in each organ from a given set of beamlet weights and what you want the dose to actually be. Healthy tissue is the limiting factor so you give as much as you can to the tumour while making sure that less than X% of the volume of a nearby organ gets more than Y units of radiation. There's a final step at the end that turns the fluence maps into actual deliverable apertures shaped by the multi leaf collimator.

There's a huge amount of work that goes into the simulation aspect. You can't just model the radiation beam as pure light sources that attenuate in the body via some exponential decay because the high energy photons scatter off electrons which themselves scatter around while depositing energy (radiation dose) away from the point of interaction. The gold standard is Monte Carlo simulations (which is my area of research) since you can model the actual physics of particle transport but in practice most clinics will use a faster engine to generate dose distributions. The faster engines typically superpose a primary component (a pure exponential decay) convolved with a kernel representing the energy that gets deposited away from the point of interaction.

That's probably way more information than you wanted ;)

This is actually what we do in radiotherapy nowadays, it's even called "inverse treatment planning". Basically a radio-oncologist or a dosimetrist contours (manually, with a pen) organs on every slice of a CT scan and then we just say "We want this much radiation in the tumour, and less than Y units in surrounding healthy tissues".

Treatment planning software then simulates dose distributions from a bunch of possible radiation beam collimations and adjusts the amount of radiation coming out of each radiation "field" to minimize a cost function penalizing overdosing healthy tissue and underdosing the tumour(s).

I've been using http://naturesoundsfor.me/ as they let you mix a bunch of sounds and control the relative volumes of each. I've settled on 40% "Creek" and 70% "Rain" for sleeping.

Are you sure you're not mixing up neutrons and protons? Here are some typical depth dose curves from google images(http://www.nap.edu/books/11976/xhtml/images/p20014b2bg205001...). Neutrons, being neutral particles, don't exhibit the Bragg Peak (large increase in energy deposition at the end of the particle's track) that you get for heavy charged particles.

Yeah it's a shame. The wiki page for ".edu" specifies "Since 2001, new registrants to the domain have been required to be United States-affiliated institutions of higher education, though before then non-U.S.-affiliated—and even non-educational institutions—registered, with some retaining their registrations to the present."

So it definitely has a US bias.