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Introduction to get ready for looking at the Bohr model of the atom - by reviewing the Thomson model and then the Rutherford model. An electron would ultimately lose energy, spiralling towards the nucleus, gaining higher velocity but with lower frequency EM radiation emissions.

Deep learning for image reconstruction: introductory perspectives Why use AI or deep learning? Image to image mappings Sinogram to image mappings

Classical physics could not explain sunlight. However, by putting forward the idea that light energy is quantised, Planck was able to come up with a formula that could explain black-body radiation, Planck's radiation law, overcoming the ultraviolet catastrophe of the Rayleigh-Jeans law.

Brief tutorial and history of conventional PET image reconstruction, including deep learning based approaches, and finishing with methods using a diffusion model within image reconstruction ABSTRACT PET image reconstruction has been in ongoing development over many decades, due to the need for improved image quality when reconstructing from raw PET data with limited counts and limited spatial r...

Simple understanding of one of the most widely accepted interpretations of the wave function

Understanding precision and uncertainty in position and momentum

Nobel prize winning Bohr's amazing result, which explains the spectral lines for hydrogen in terms of fundamental constants.

Photoelectric Effect Explained with Equations: Einstein's Nobel Prize Winning Work on Photons

Millikan's Nobel Prize: Oil Drop Experiment for Measuring the Fundamental Unit of Electric Charge

PET acquisition and iterative reconstruction (basics in 6 minutes!)

Basics of least squares: modelling, predicting and reconstructing!

How to undo convolution: deconvolution for image reconstruction (via Fourier & convolution theorem)

Schrodinger's cat: did you ever really get it, and the link to quantum computing?

Einstein's Nobel Prize: Photoelectric Effect

Basics of k-space for MRI (magnetic resonance imaging)

Filtered backprojection (FBP) for image reconstruction: central section theorem, Radon & Fourier

Visualising 2D k-space and Fourier synthesis (1D & 2D, helps for image reconstruction and analysis)

Image reconstruction: sinograms, backprojected images, convolution, Radon & x-ray transform

Deep image prior: simple code for image restoration with no training data needed

AI vs. Physics in Image Reconstruction

Schrodinger equation: fast, simple mathematical description with consideration of the hydrogen atom

Thomson's cathode ray tube: finding the charge to mass ratio of the electron

Get started with convolutional neural networks (CNNs) to process an image - Jupyter Notebook/PyTorch

Master your understanding of Fourier: basics to equations

Most remarkable formula in mathematics: Euler’s formula and expressions for cosine and sine

Steepest Descent (Live Lecture)

Image reconstruction: FBP (filtered backprojection) basics, X-Ray Transform, Central Section Theorem

Image reconstruction by deconvolution: least squares, Tikhonov regularisation & Gaussian likelihood

Convolution equation explained simply in 3 forms: discrete, continuous and matrix-vector