Linear algebra
Linear algebra is a field of mathematics with extremely diverse applications. This type of mathematics extends arithmetical operation from numbers to complex objects like matrices and vectors.
In terms of radiology, linear algebra applications include CT reconstruction algorithms, neural network algorithms, windowing, and MRI sequence algorithms. Practically speaking, linear algebra deals with linear equations, matrices and vectors. The vectors of linear algebra should not be confused with vectors as conceptualized in physics or trigonometry.
Linear algebra underlies much of computerized image processing, as all digital images are matrices. All computerized texture analysis and convolutional neural network programs rely on operations on matrices of the image, for example matrix multiplication inside of their various algorithms.
Related Radiopaedia articles
Artificial intelligence
 artificial intelligence (AI)
 imaging data sets
 computeraided diagnosis (CAD)
 natural language processing
 machine learning (overview)
 visualizing and understanding neural networks
 common data preparation/preprocessing steps
 DICOM to bitmap conversion
 dimensionality reduction
 scaling
 centering
 normalization
 principal component analysis
 training, testing and validation datasets
 augmentation
 loss function

optimization algorithms
 ADAM
 momentum (Nesterov)
 stochastic gradient descent
 minibatch gradient descent

regularisation
 linear and quadratic
 batch normalization
 ensembling
 rulebased expert systems
 glossary
 activation function
 anomaly detection
 automation bias
 backpropagation
 batch size
 computer vision
 concept drift
 cost function
 confusion matrix
 convolution
 cross validation
 curse of dimensionality
 dice similarity coefficient
 dimensionality reduction
 epoch
 feature extraction
 gradient descent
 ground truth
 hyperparameters
 image registration
 imputation
 iteration
 jaccard index
 linear algebra
 noise reduction
 normalization
 R (Programming language)
 Python (Programming language)
 segmentation
 semisupervised learning
 synthetic and augmented data
 overfitting
 transfer learning