---
dataset_info:
  features:
  - name: iq_real
    dtype:
      array3_d:
        shape:
        - 180
        - 180
        - 1024
        dtype: float32
  - name: iq_imag
    dtype:
      array3_d:
        shape:
        - 180
        - 180
        - 1024
        dtype: float32
  - name: bmode
    dtype:
      array2_d:
        shape:
        - 507
        - 456
        dtype: float32
  - name: bmode_focused
    dtype:
      array2_d:
        shape:
        - 507
        - 456
        dtype: float32
  - name: bmode_extent
    list: float32
    length: 4
  - name: sound_speed_map
    dtype:
      array2_d:
        shape:
        - 32
        - 32
        dtype: float32
  - name: sound_speed_extent
    list: float32
    length: 4
  - name: segmentation_map
    dtype:
      array2_d:
        shape:
        - 507
        - 456
        dtype: uint8
  - name: phase_error
    dtype: float32
  - name: fs
    dtype: float64
  - name: fc
    dtype: float64
  - name: fd
    dtype: float64
  - name: t0
    dtype: float64
  - name: c0
    dtype: float64
  - name: elpos
    dtype:
      array2_d:
        shape:
        - 3
        - 180
        dtype: float32
  - name: frame_idx
    dtype: int32
  splits:
  - name: validation
    num_bytes: 24903052308
    num_examples: 93
  - name: train
    num_bytes: 222253047480
    num_examples: 830
  download_size: 271357212463
  dataset_size: 247156099788
configs:
- config_name: default
  data_files:
  - split: validation
    path: data/validation-*
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
  - image-segmentation
  - other
tags:
  - ultrasound
  - medical-imaging
  - simulation
  - beamforming
  - sound-speed-estimation
  - phase-aberration
language:
  - en
pretty_name: NV-Raw2Insights-US Simulations
size_categories:
  - n<1K
---

# NV-Raw2Insights-US Simulations

## Dataset Description

NV-Raw2Insights-US Simulations is a simulated full synthetic aperture (FSA) ultrasound dataset for training and evaluating neural networks on sound speed estimation, phase aberration correction, and tissue segmentation.

Each sample is a single-frame FSA acquisition from a 180-element linear array simulated over a heterogeneous tissue phantom containing cysts. The dataset provides raw baseband IQ channel data alongside ground truth sound speed maps, binary cyst segmentation masks, and phase aberration values.

This dataset is ready for commercial use.

## Dataset Owner(s)

NVIDIA Corporation

## Dataset Creation Date

12/01/2025

## License/Terms of Use

Governing Terms: This dataset is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/legalcode.en).

## Intended Usage

NV-Raw2Insights-US Simulations is intended to be used by the research community to develop and benchmark methods for ultrasound image reconstruction, sound speed estimation, phase aberration correction, and tissue segmentation from raw channel data.

## Dataset Characterization

**Data Collection Method**<br>
Synthetic (k-Wave acoustic simulation)

**Labeling Method**<br>
Synthetic (ground truth from simulation parameters)

## Dataset Format

Raw Ultrasound Channel Capture Data (Apache Arrow)

## Dataset Quantification

923 samples (830 train / 93 validation)

17 features per sample:

| Feature | Shape | Dtype | Units | Description |
|---|---|---|---|---|
| `iq_real` | `[180, 180, 1024]` | float32 | -- | Real part of baseband IQ. Axes: `[n_tx, n_el, n_ax]` |
| `iq_imag` | `[180, 180, 1024]` | float32 | -- | Imaginary part of baseband IQ |
| `bmode` | `[507, 456]` | float32 | linear | Synthetic aperture DAS b-mode (envelope, not log-compressed) |
| `bmode_focused` | `[507, 456]` | float32 | linear | Focused transmit b-mode |
| `bmode_extent` | `[4]` | float32 | mm | Spatial extent `[x_min, x_max, z_max, z_min]` |
| `sound_speed_map` | `[32, 32]` | float32 | m/s | Ground truth speed of sound map |
| `sound_speed_extent` | `[4]` | float32 | m | Spatial extent `[x_min, x_max, z_max, z_min]` |
| `segmentation_map` | `[507, 456]` | uint8 | -- | Binary segmentation: `0` = background tissue, `1` = cyst |
| `phase_error` | scalar | float32 | radians | Per-sample phase aberration error |
| `fs` | scalar | float64 | Hz | Sampling frequency (13.3 MHz) |
| `fc` | scalar | float64 | Hz | Center frequency (6.5 MHz) |
| `fd` | scalar | float64 | Hz | Demodulation frequency (6.5 MHz) |
| `t0` | scalar | float64 | s | Time of transmit peak pressure |
| `c0` | scalar | float64 | m/s | Background sound speed (1540 m/s) |
| `elpos` | `[3, 180]` | float32 | m | Element positions `[x, y, z]` for each of 180 elements |
| `frame_idx` | scalar | int32 | -- | Frame index within source simulation (always `0` — each sample is a single-frame acquisition) |

Pre-computed IQ normalization statistics:

| Statistic | Value |
|---|---|
| `iq_rms_global` | 0.6616 |
| `iq_mean_real_global` | 0.0021 |
| `iq_mean_imag_global` | -0.0016 |
| `iq_max_mean_global` | 223.67 |

## Supported Tasks

- **Sound speed estimation:** Predict `sound_speed_map` from `iq_real` / `iq_imag`
- **Phase aberration estimation:** Predict `phase_error` from IQ data
- **Tissue segmentation:** Predict `segmentation_map` from `bmode` or IQ data
- **Image reconstruction:** Reconstruct `bmode` from raw IQ data (learned beamforming)

## Usage

```python
from datasets import load_dataset

ds = load_dataset("nvidia/nv-raw2insights-us")

sample = ds["train"][0]
iq = sample["iq_real"] + 1j * sample["iq_imag"]  # [180, 180, 1024] complex64
sos = sample["sound_speed_map"]                    # [32, 32] float32
seg = sample["segmentation_map"]                   # [507, 456] uint8
```


## Known Issues

- `bmode_extent` is in millimeters while `sound_speed_extent` and `elpos` are in meters.
- `bmode_extent` z-axis ordering is `[x_min, x_max, z_max, z_min]` (z_max before z_min).
- Cysts that touch the edge of the b-mode frame are not included in `segmentation_map`.
- Some regions of gross reverberation artifact are incorrectly segmented as cysts in `segmentation_map`.


## Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

## Citation

```bibtex
@misc{nv_raw2insights_us_simulations_2026,
  title={NV-Raw2Insights-US Simulations},
  author={Simson, Walter and Huver, Sean},
  year={2026},
  publisher={NVIDIA Corporation},
  howpublished={\url{https://huggingface.co/datasets/nvidia/nv-raw2insights-us-simulations}},
  license={CC BY 4.0}
}
```