Compressed Sensing
Compressed Sensing - Related Videos
04:17
Approximate Number Sense Test
0 Views •
2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University A common carnival game is to ask people to guess the number of jellybeans packed into a jar. The chances that anyone will get the exact number right are low. But what about the chances that someone will guess 17 or 147,000? Probably even less than the chances of guessing the correct answer; 17 and 147,000 just seem irrational. Why? After all, if the beans cannot be taken out and counted one-at-a-time, how can someone tell that an...
08:08
Compression Tests on Hardened Concrete
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA There are two distinct stages in a construction project involving concrete. The first stage involves batching, transporting, and casting fresh concrete. At this stage, the material is viscous, and the workability and finishability are the key performance criteria. The second stage occurs when the hydration process begins shortly after the concrete is placed in the form, and the concrete will...
05:15
The Rouge Test: Searching for a Sense of Self
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2023
Source: Laboratories of Nicholaus Noles and Judith Danovitch—University of Louisville Humans are different from other animals in many ways, but one of the abilities that sets humans apart is their advanced ability to understand other people and simulate their thoughts and feelings, even when the thoughts and feelings do not align with their own. In scientific terms, these abilities are referred to as theory of mind, and this understanding is necessary for activities like giving compliments,...
02:57
The Sense of Self: Reflected Self-Appraisal and Social Comparison
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2020
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
16:40
A New Single Chamber Implantable Defibrillator with Atrial Sensing: A Practical Demonstration of Sensing and Ease of Implantation
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Cited by 5 •
2012
Dual-chamber implantable cardioverter-defibrillators (ICDs) may improve detection of atrial fibrillation as well as differentiation of tachycardias. However, this advantage is undermined by complications associated with the second electrode, which is required in conventional dual chamber devices. Therefore, BIOTRONIK has developed a new electrode called the LinoxSMART S DX that, when used in conjunction with the Lumax DX ICD, offers dual-chamber detection without the risks associated with the...
FAQs
The reconstruction balances two requirements: the recovered signal should agree with the measurements, while its representation should remain sparse or compressible. Mathematical optimization expresses this tradeoff and searches for a solution that satisfies both constraints. This pairing allows undersampled data to support recovery, but success still depends on whether the signal actually has exploitable structure.
Sparsity describes a signal using relatively few meaningful components in an appropriate representation. Compressed sensing favors reconstructions with this property rather than treating every possible signal as equally likely. If the medical image or signal is sufficiently sparse or compressible, fewer measurements may still retain diagnostically useful content; otherwise, reconstruction quality can decline.
Sampling design determines which measurements are acquired and how informative they are for reconstruction. An undersampling pattern that does not capture enough relevant structure can leave the optimization with inadequate evidence, even when the algorithm is well chosen. Consequently, performance reflects the interaction among measurement strategy, signal structure, and reconstruction method rather than any one component alone.
Unlike conventional sampling, which generally acquires data at a rate set by the sampling requirement, compressed sensing deliberately works with fewer measurements when signal structure makes recovery possible. The advantage is potential efficiency, not guaranteed recovery. Its results depend on optimization, sampling design, and compressibility, so reduced acquisition cannot be assumed to preserve useful information in every setting.
A medical workflow first acquires a strategically designed, often undersampled set of measurements. Reconstruction then applies mathematical optimization to find a signal consistent with those data while favoring a sparse representation. The resulting image or signal is assessed for whether it retains diagnostically useful information. This sequence links acquisition choices directly to reconstruction and evaluation.
In MRI, the technique is relevant when reducing acquisition time or data volume would improve workflow efficiency. The scanner collects fewer, deliberately selected measurements, and reconstruction estimates the underlying image from them. The practical goal is a faster examination or a lighter data burden while maintaining information that remains useful for diagnosis, subject to validation.
Beyond MRI, compressed sensing can support other medical imaging workflows where fewer measurements may be valuable. Depending on the modality and acquisition design, the approach may potentially reduce data collection demands or radiation exposure while preserving diagnostically useful information. Those benefits are conditional: the recovered result must be evaluated for adequacy rather than inferred solely from a lower measurement count.
Validation is essential before clinical use because reconstruction quality cannot be guaranteed by undersampling alone. Evaluation should examine whether the recovered images or signals preserve diagnostically useful information under the intended sampling design and algorithm. This requirement is especially important when changing acquisition conditions, signal characteristics, or reconstruction choices, since each can alter the final outcome.