This application claims the benefit of PPA Application Number 62591253 filed 28 Nov. 2017 by the present inventor, which is incorporated by reference.
Numerous instruments function by essentially carrying out experiments and fitting sensor data to scientific models. Developers of such instruments typically task themselves to deploy sensors for signal detection, to design procedures for systematic collection of signals, to devise algorithms for inference or reconstruction of unknown quantities of interest with the collected signal data and applicable scientific models, and to present the inference or reconstruction results in easy-to-interpret formats.
Medical imaging equipment is a good example. A CT or MRI scanner for instance, uses sensors carefully configured near a scanned target to detect signals containing diagnostic information, uses developer-crafted measurement procedures or sequences to collect signal data, and uses developer-crafted algorithms to reconstruct quantities of diagnostic value based on the collected data and such established models as attenuation model and Bloch equation, and finally presents results in an image format for radiologists to read.
Inspired by recent achievements in the machine learning field, we propose intelligent instruments with human knowledge (which include scientific models and problem solving experiences) making one part of the core, but with machine learning elements replacing many of the other human design/devise elements (which include, e.g., development or deployment of algorithms, procedures and sensors, as well as interpretation of results) making the other part. Further, the machine learning part is optionally set to evolve or optimize continuously, leveraging features continuously learned from data throughout the use cycle of the instruments.
The intelligent instruments are hybrid learning machines—they function by utilizing both human learning and machine learning. While the former often excels in modeling with elegance and depth the operation principles of the physical world, the latter is becoming increasingly powerful spotting patterns in high-dimensional space and in massive data, and responding with solution strategies autonomously. On the new instruments the two learning paradigms work together, creating unprecedented capabilities.
In this hybrid fashion, an example embodiment employs the human established models/experiences to guide the set-up of an adaptive compute unit (ACU) for carrying out machine learning, and employs data samples to guide the adaptation or optimization of the ACU and even the experimental configurations. The experimental configurations may include sensor, procedure or sequence parameters. The ACU may be a neural network whose weights are to be optimized in training in accordance with data samples and proper loss metrics. The optimization of the ACU and/or the experimental parameters can use gradient descent and gradient back propagation techniques, and benefit from the often differentiable scientific models.
Two further embodiments are illustrated in
Hybrid learning machine solves inference problems by applying both human learning and machine learning. As results of human learning, laws of the physical world, high quality simulations and designed experiments, for example, are often good at quantifying or summarizing behaviors of interest. One can instill or integrate them into a machine learning paradigm to enhance the quality and speed of training/optimizing an ACU, and to facilitate the creation of an autonomous problem solving machine.
Hybrid learning machine solves inference problems by applying both human learning and machine learning.
Variants or more specialized versions of the above embodiments are illustrated in
Loss quantification provides the driving force for the adaptation or optimization of the ACU, and its definition directly influences the outcomes' quality as well as the optimization landscape.
One embodiment furthering that illustrated in
One embodiment furthering that illustrated in
s(r,tn)=(ρW(r)+ρF(r)ej2πf
where tn, n=1, 2, 3, . . . denotes a string of echo time (TE) shifts, r denotes voxel location, fF denotes frequency shift (in Hz) of fat relative to water, ΔB0 is local frequency shift (in Hz) due to static field inhomogeneity, and R2* represents T2* effect.
There are explicitly controllable parameters in experiments, including, in MRI for example, sequence timing, RF excitation strength, gradient trajectories, receive coil configuration and etc. By adding to the total loss optimization an additional target such as SNR and imaging speed, the hybrid learning machine can use techniques including gradient descent and gradient back propagation to further optimize these controllable parameters and to achieve performance gains, leveraging, in a unique way, both models established for the physical world and patterns identified in high-dimensional space and in massive data.
Referring to
The system control 32 includes a set of modules connected together by a backplane 32a. These include a CPU module 36 and a pulse generator module 38 which connects to the operator console 12 through a serial link 40. It is through link 40 that the system control 32 receives commands from the operator to indicate the scan sequence that is to be performed. The pulse generator module 38 operates the system components to carry out the desired scan sequence and produces data which indicates, for RF transmit, the timing, strength and shape of the RF pulses produced, and, for RF receive, the timing and length of the data acquisition window. The pulse generator module 38 connects to a set of gradient amplifiers 42, to indicate the timing and shape of the gradient pulses that are produced during the scan. The pulse generator module 38 can also receive patient data from a physiological acquisition controller 44 that receives signals from a number of different sensors connected to the patient, such as ECG signals from electrodes attached to the patient. And finally, the pulse generator module 38 connects to a scan room interface circuit 46 which receives signals from various sensors associated with the condition of the patient and the magnet system. It is also through the scan room interface circuit 46 that a patient positioning system 48 receives commands to move the patient to the desired position for the scan.
The gradient waveforms produced by the pulse generator module 38 are applied to the gradient amplifier system 42 having Gx, Gy, and Gz amplifiers. Each gradient amplifier excites a corresponding physical gradient coil in a gradient coil assembly generally designated 50 to produce the magnetic field gradients used for spatially encoding acquired signals. The gradient coil assembly 50 and a polarizing magnet 54 form a magnet assembly 52. An RF coil assembly 56 is placed between the gradient coil assembly 50 and the imaged patient. A transceiver module 58 in the system control 32 produces pulses which are amplified by an RF amplifier 60 and coupled to the RF coil assembly 56 by a transmit/receive switch 62. The resulting signals emitted by the excited nuclei in the patient may be sensed by the same RF coil assembly 56 and coupled through the transmit/receive switch 62 to a preamplifier module 64. The amplified MR signals are demodulated, filtered, and digitized in the receiver section of the transceiver 58. The transmit/receive switch 62 is controlled by a signal from the pulse generator module 38 to electrically connect the RF amplifier 60 to the coil assembly 56 during the transmit mode and to connect the preamplifier module 64 to the coil assembly 56 during the receive mode. The transmit/receive switch 62 can also enable a separate RF coil (for example, a surface coil) to be used in either the transmit or receive mode. The transceiver module 58, the separate RF coil and/or the coil assembly 56 are commonly configured to support parallel acquisition operation.
The MR signals picked up by the separate RF coil and/or the RF coil assembly 56 are digitized by the transceiver module 58 and transferred to a memory module 66 in the system control 32. A scan is complete when an array of raw k-space data has been acquired in the memory module 66. This raw k-space data is rearranged into separate k-space data arrays for each image to be reconstructed, and each of these is input to an array processor 68 which operates to Fourier transform the data to combine MR signal data into an array of image data. This image data is conveyed through the serial link 34 to the computer system 20 where it is stored in memory, such as disk storage 28. In response to commands received from the operator console 12, this image data may be archived in long term storage, such as on the tape drive 30, or it may be further processed by the image processor 22 and conveyed to the operator console 12 and presented on the display 16.
While the above descriptions of methods and systems contain many specificities, these should not be construed as limitations on the scope of any embodiment, but as exemplifications of the presently preferred embodiments thereof. Many other ramifications and variations are possible within the teachings of the various embodiments.
Number | Date | Country | |
---|---|---|---|
62591253 | Nov 2017 | US |