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Related Concept Videos

Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Sampling Theorem01:15

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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K2S Challenge: From Undersampled K-Space to Automatic Segmentation.

Aniket A Tolpadi1,2, Upasana Bharadwaj2, Kenneth T Gao1,2

  • 1Department of Bioengineering, University of California, Berkeley, CA 94720, USA.

Bioengineering (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Integrating Magnetic Resonance Imaging (MRI) reconstruction and segmentation into end-to-end tasks improved performance. This approach better reflects real-world applications for knee imaging analysis.

Keywords:
compressed sensingdeep learningimage reconstructionmagnetic resonance imagingmulti-task learningmusculoskeletalsegmentation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Magnetic Resonance Imaging (MRI) provides excellent soft tissue contrast but faces challenges with long scan times and manual segmentation.
  • Current methods address MRI reconstruction and image analysis separately, potentially limiting overall performance.

Purpose of the Study:

  • To investigate the benefits of treating MRI reconstruction and image analysis as an end-to-end task.
  • To evaluate performance improvements in knee bone and cartilage segmentation using undersampled k-space data.

Main Methods:

  • Hosted the K2S challenge, providing a dataset of 300-patient multicoil k-space data and expert-verified segmentations.
  • Challenged participants to segment knee bones and cartilage from 8x undersampled k-space data.
  • Evaluated diverse methodologies, including serial and end-to-end approaches.

Main Results:

  • Twelve submissions were received from 87 registered teams, employing various reconstruction and segmentation strategies.
  • The top four submissions demonstrated suitability for biomarker analysis, preserving critical cartilage and bone features.
  • The winning method achieved a weighted Dice Similarity Coefficient of 0.910 ± 0.021.

Conclusions:

  • Treating MRI reconstruction and segmentation as an end-to-end process offers optimization opportunities.
  • This integrated approach aligns better with the practical requirements for developing clinical MRI tools.
  • No correlation was found between reconstruction quality and segmentation metrics in the evaluated methods.