advanced_imaging.mbt

// 高级成像技术（v40.0.0）
// 量子成像、太赫兹成像、红外成像、高光谱成像、偏振成像、热成像、X射线成像、超声成像、MRI成像、PET成像

///|
/// 成像模态类型
pub(all) enum ImagingModality {
  Quantum
  Terahertz
  Infrared
  Hyperspectral
  Polarization
  Thermal
  XRay
  Ultrasound
  MRI
  PET
  CT
  Unknown
} derive(Eq)

///|
/// 量子成像算法类型
pub(all) enum QuantumImagingAlgorithm {
  GhostImaging
  SinglePixel
  QuantumIllumination
  EntangledPhoton
  CompressiveSensing
  Unknown
} derive(Eq)

///|
/// 红外成像类型
pub(all) enum InfraredImagingType {
  MWIR
  LWIR
  SWIR
  NIR
  ThermalIR
  Unknown
} derive(Eq)

///|
/// 高光谱数据
pub(all) struct HyperspectralData {
  image_cube : Array[@types.Image]
  num_bands : Int
  wavelengths : Array[Double]
  spectral_resolution : Double
  spatial_width : Int
  spatial_height : Int
}

///|
/// 偏振数据
pub(all) struct PolarizationData {
  intensity : @types.Image
  degree_of_polarization : @types.Image
  angle_of_polarization : @types.Image
  stokes_parameters : Array[@types.Image]
  num_stokes : Int
}

///|
/// 热成像数据
pub(all) struct ThermalData {
  temperature_map : @types.Image
  min_temperature : Double
  max_temperature : Double
  average_temperature : Double
  emissivity : Double
  units : String
}

///|
/// X射线数据
pub(all) struct XRayData {
  projection : @types.Image
  energy_kev : Double
  dose : Double
  contrast : Double
  noise_level : Double
}

///|
/// 超声数据
pub(all) struct UltrasoundData {
  b_mode : @types.Image
  doppler : @types.Image
  frequency_mhz : Double
  depth_cm : Double
  gain : Double
  frame_rate : Double
}

///|
/// MRI数据
pub(all) struct MRIData {
  t1_weighted : @types.Image
  t2_weighted : @types.Image
  flair : @types.Image
  tr_ms : Double
  te_ms : Double
  slice_thickness_mm : Double
  field_strength_t : Double
}

///|
/// PET数据
pub(all) struct PETData {
  activity_map : @types.Image
  radiotracer : String
  injected_dose_mbq : Double
  uptake_time_min : Double
  reconstruction_algorithm : String
  suv_scale : Double
}

///|
/// 成像配置
pub(all) struct AdvancedImagingConfig {
  modality : ImagingModality
  image_width : Int
  image_height : Int
  num_slices : Int
  use_noise_reduction : Bool
  use_enhancement : Bool
  calibration_factor : Double
}

///|
/// 创建默认高级成像配置
pub fn create_default_advanced_imaging_config(
  modality : ImagingModality,
) -> AdvancedImagingConfig {
  AdvancedImagingConfig::{
    modality,
    image_width: 512,
    image_height: 512,
    num_slices: 1,
    use_noise_reduction: true,
    use_enhancement: true,
    calibration_factor: 1.0,
  }
}

///|
/// 量子成像（鬼成像简化版）
pub fn quantum_ghost_imaging(
  num_patterns : Int,
  width : Int,
  height : Int,
  algorithm : QuantumImagingAlgorithm,
) -> @types.Image {
  ignore(algorithm)
  let total = width * height
  let out : Array[Byte] = []
  let accumulator : Array[Double] = []
  for _ in 0..<total {
    accumulator.push(0.0)
  }

  // 简化的鬼成像：随机模式与桶信号的关联
  let mut state = 42
  for _ in 0..<num_patterns {
    // 生成随机照明模式
    let pattern : Array[Bool] = []
    let mut bucket_signal = 0.0
    for _ in 0..<total {
      state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
      let is_on = state % 2 == 0
      pattern.push(is_on)
      if is_on {
        bucket_signal += 1.0
      }
    }

    // 关联计算
    for i in 0..<total {
      if pattern[i] {
        accumulator[i] += bucket_signal
      }
    }
  }

  // 归一化
  let mut max_val = 0.0
  for i in 0..<total {
    if accumulator[i] > max_val {
      max_val = accumulator[i]
    }
  }

  for i in 0..<total {
    let normalized = if max_val > 0.0 {
      accumulator[i] / max_val * 255.0
    } else {
      0.0
    }
    out.push(normalized.to_int().to_byte())
  }

  @types.Image::{ width, height, channels: 1, data: Bytes::from_array(out) }
}

///|
/// 红外成像伪彩色映射
pub fn infrared_pseudocolor(
  infrared_image : @types.Image,
  ir_type : InfraredImagingType,
) -> @types.Image {
  ignore(ir_type)
  let w = infrared_image.width
  let h = infrared_image.height
  let total = w * h
  let out : Array[Byte] = []

  for i in 0..<total {
    let gray = infrared_image.data[i].to_double() / 255.0

    // 铁彩虹伪彩色映射
    let mut r = 0.0
    let mut g = 0.0
    let mut b = 0.0

    if gray < 0.25 {
      r = 0.0
      g = 0.0
      b = gray * 4.0 * 255.0
    } else if gray < 0.5 {
      let t = (gray - 0.25) * 4.0
      r = 0.0
      g = t * 255.0
      b = 255.0 - t * 255.0
    } else if gray < 0.75 {
      let t = (gray - 0.5) * 4.0
      r = t * 255.0
      g = 255.0
      b = 0.0
    } else {
      let t = (gray - 0.75) * 4.0
      r = 255.0
      g = 255.0 - t * 255.0
      b = t * 128.0
    }

    out.push(r.to_int().to_byte())
    out.push(g.to_int().to_byte())
    out.push(b.to_int().to_byte())
  }

  @types.Image::{
    width: w,
    height: h,
    channels: 3,
    data: Bytes::from_array(out),
  }
}

///|
/// 生成高光谱数据立方体（简化版）
pub fn generate_hyperspectral_cube(
  base_image : @types.Image,
  num_bands : Int,
  start_wavelength : Double,
  end_wavelength : Double,
) -> HyperspectralData {
  let image_cube : Array[@types.Image] = []
  let wavelengths : Array[Double] = []
  let spectral_resolution = (end_wavelength - start_wavelength) /
    num_bands.to_double()

  for band in 0..<num_bands {
    let wavelength = start_wavelength + band.to_double() * spectral_resolution
    wavelengths.push(wavelength)

    // 简化的波段生成：基于波长的颜色调制
    let w = base_image.width
    let h = base_image.height
    let total = w * h
    let out : Array[Byte] = []

    for i in 0..<total {
      let base = base_image.data[i].to_double()
      // 简化的光谱响应
      let response = if wavelength < 500.0 {
        base * 0.8
      } else if wavelength < 600.0 {
        base * 1.0
      } else if wavelength < 700.0 {
        base * 0.9
      } else {
        base * 0.7
      }
      let clamped = if response > 255.0 { 255.0 } else { response }
      out.push(clamped.to_int().to_byte())
    }

    image_cube.push(@types.Image::{
      width: w,
      height: h,
      channels: 1,
      data: Bytes::from_array(out),
    })
  }

  HyperspectralData::{
    image_cube,
    num_bands,
    wavelengths,
    spectral_resolution,
    spatial_width: base_image.width,
    spatial_height: base_image.height,
  }
}

///|
/// 偏振成像分析（简化版）
pub fn analyze_polarization(
  images_0deg : @types.Image,
  images_45deg : @types.Image,
  images_90deg : @types.Image,
  images_135deg : @types.Image,
) -> PolarizationData {
  let w = images_0deg.width
  let h = images_0deg.height
  let total = w * h
  let intensity_out : Array[Byte] = []
  let dop_out : Array[Byte] = []
  let aop_out : Array[Byte] = []

  for i in 0..<total {
    let i0 = images_0deg.data[i].to_double()
    let i45 = images_45deg.data[i].to_double()
    let i90 = images_90deg.data[i].to_double()
    let i135 = images_135deg.data[i].to_double()

    // Stokes参数
    let s0 = (i0 + i90) / 2.0
    let s1 = i0 - i90
    let s2 = i45 - i135

    // 偏振度
    let dop = if s0 > 0.0 {
      (s1 * s1 + s2 * s2).sqrt() / s0 * 255.0
    } else {
      0.0
    }

    // 偏振角
    let aop = if s1 != 0.0 {
      (0.5 * atan_approx(s2 / s1) + 3.14159 / 2.0) / 3.14159 * 255.0
    } else {
      0.0
    }

    let intensity_clamped = if s0 > 255.0 { 255.0 } else { s0 }
    let dop_clamped = if dop > 255.0 { 255.0 } else { dop }
    let aop_clamped = if aop > 255.0 { 255.0 } else { aop }

    intensity_out.push(intensity_clamped.to_int().to_byte())
    dop_out.push(dop_clamped.to_int().to_byte())
    aop_out.push(aop_clamped.to_int().to_byte())
  }

  let stokes : Array[@types.Image] = []
  stokes.push(images_0deg)
  stokes.push(images_45deg)
  stokes.push(images_90deg)
  stokes.push(images_135deg)

  PolarizationData::{
    intensity: @types.Image::{
      width: w,
      height: h,
      channels: 1,
      data: Bytes::from_array(intensity_out),
    },
    degree_of_polarization: @types.Image::{
      width: w,
      height: h,
      channels: 1,
      data: Bytes::from_array(dop_out),
    },
    angle_of_polarization: @types.Image::{
      width: w,
      height: h,
      channels: 1,
      data: Bytes::from_array(aop_out),
    },
    stokes_parameters: stokes,
    num_stokes: 4,
  }
}

///|
/// 热成像温度转换（简化版）
pub fn thermal_to_temperature(
  thermal_image : @types.Image,
  emissivity : Double,
  min_temp : Double,
  max_temp : Double,
) -> ThermalData {
  let w = thermal_image.width
  let h = thermal_image.height
  let total = w * h
  let temp_out : Array[Byte] = []

  let mut min_t = 1000.0
  let mut max_t = -1000.0
  let mut sum_t = 0.0

  for i in 0..<total {
    let gray = thermal_image.data[i].to_double() / 255.0
    let temp = min_temp + gray * (max_temp - min_temp)
    let corrected = temp / emissivity

    if corrected < min_t {
      min_t = corrected
    }
    if corrected > max_t {
      max_t = corrected
    }
    sum_t += corrected

    let normalized = (corrected - min_temp) / (max_temp - min_temp) * 255.0
    let clamped = if normalized < 0.0 {
      0.0
    } else if normalized > 255.0 {
      255.0
    } else {
      normalized
    }
    temp_out.push(clamped.to_int().to_byte())
  }

  ThermalData::{
    temperature_map: @types.Image::{
      width: w,
      height: h,
      channels: 1,
      data: Bytes::from_array(temp_out),
    },
    min_temperature: min_t,
    max_temperature: max_t,
    average_temperature: sum_t / total.to_double(),
    emissivity,
    units: "Celsius",
  }
}

///|
/// X射线图像增强（简化版）
pub fn xray_enhance(xray_image : @types.Image, energy_kev : Double) -> XRayData {
  let w = xray_image.width
  let h = xray_image.height
  let total = w * h
  let out : Array[Byte] = []

  // 简化的X射线增强：对比度增强+噪声抑制
  let mut sum = 0.0
  let mut sum_sq = 0.0
  for i in 0..<total {
    let v = xray_image.data[i].to_double()
    sum += v
    sum_sq += v * v
  }
  let mean = sum / total.to_double()
  let variance = sum_sq / total.to_double() - mean * mean
  let std_dev = if variance > 0.0 { variance.sqrt() } else { 1.0 }

  for i in 0..<total {
    let v = xray_image.data[i].to_double()
    // 标准化+对比度拉伸
    let normalized = (v - mean) / std_dev
    let enhanced = normalized * 40.0 + 128.0
    let clamped = if enhanced < 0.0 {
      0.0
    } else if enhanced > 255.0 {
      255.0
    } else {
      enhanced
    }
    out.push(clamped.to_int().to_byte())
  }

  XRayData::{
    projection: @types.Image::{
      width: w,
      height: h,
      channels: 1,
      data: Bytes::from_array(out),
    },
    energy_kev,
    dose: 0.1,
    contrast: std_dev / mean,
    noise_level: std_dev * 0.1,
  }
}

///|
/// 超声图像合成（简化版）
pub fn ultrasound_synthesize(
  width : Int,
  height : Int,
  frequency_mhz : Double,
  depth_cm : Double,
) -> UltrasoundData {
  let _ = width * height
  let b_out : Array[Byte] = []
  let doppler_out : Array[Byte] = []

  let mut state = 42
  for y in 0..<height {
    for x in 0..<width {
      state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
      let noise = (state % 1000).to_double() / 1000.0

      // 简化的B模式：扇形扫描+斑点噪声
      let center_x = width.to_double() / 2.0
      let dx = (x.to_double() - center_x) / (width.to_double() / 2.0)
      let dy = y.to_double() / height.to_double()
      let distance = (dx * dx + dy * dy).sqrt()

      let attenuation = 1.0 - dy * 0.5
      let speckle = noise * 0.3
      let structure = if distance < 0.3 { 150.0 } else { 50.0 }
      let b_value = (structure + speckle * 100.0) * attenuation
      let b_clamped = if b_value < 0.0 {
        0.0
      } else if b_value > 255.0 {
        255.0
      } else {
        b_value
      }
      b_out.push(b_clamped.to_int().to_byte())

      // 简化的多普勒：血流信号
      let doppler_value = if distance < 0.2 && dy > 0.3 {
        noise * 255.0
      } else {
        0.0
      }
      doppler_out.push(doppler_value.to_int().to_byte())
    }
  }

  UltrasoundData::{
    b_mode: @types.Image::{
      width,
      height,
      channels: 1,
      data: Bytes::from_array(b_out),
    },
    doppler: @types.Image::{
      width,
      height,
      channels: 1,
      data: Bytes::from_array(doppler_out),
    },
    frequency_mhz,
    depth_cm,
    gain: 50.0,
    frame_rate: 30.0,
  }
}

///|
/// MRI图像合成（简化版）
pub fn mri_synthesize(
  width : Int,
  height : Int,
  field_strength_t : Double,
) -> MRIData {
  let _ = width * height
  let t1_out : Array[Byte] = []
  let t2_out : Array[Byte] = []
  let flair_out : Array[Byte] = []

  let center_x = width.to_double() / 2.0
  let center_y = height.to_double() / 2.0
  let radius = width.to_double() * 0.35

  for y in 0..<height {
    for x in 0..<width {
      let dx = x.to_double() - center_x
      let dy = y.to_double() - center_y
      let dist = (dx * dx + dy * dy).sqrt()

      // 简化的脑部MRI
      let mut t1_value = 0.0
      let mut t2_value = 0.0
      let mut flair_value = 0.0

      if dist < radius * 0.3 {
        // 脑室
        t1_value = 30.0
        t2_value = 200.0
        flair_value = 30.0
      } else if dist < radius * 0.6 {
        // 灰质
        t1_value = 100.0
        t2_value = 150.0
        flair_value = 120.0
      } else if dist < radius {
        // 白质
        t1_value = 180.0
        t2_value = 100.0
        flair_value = 160.0
      } else {
        // 背景
        t1_value = 10.0
        t2_value = 10.0
        flair_value = 10.0
      }

      t1_out.push(t1_value.to_int().to_byte())
      t2_out.push(t2_value.to_int().to_byte())
      flair_out.push(flair_value.to_int().to_byte())
    }
  }

  MRIData::{
    t1_weighted: @types.Image::{
      width,
      height,
      channels: 1,
      data: Bytes::from_array(t1_out),
    },
    t2_weighted: @types.Image::{
      width,
      height,
      channels: 1,
      data: Bytes::from_array(t2_out),
    },
    flair: @types.Image::{
      width,
      height,
      channels: 1,
      data: Bytes::from_array(flair_out),
    },
    tr_ms: 500.0,
    te_ms: 14.0,
    slice_thickness_mm: 5.0,
    field_strength_t,
  }
}

///|
/// PET图像合成（简化版）
pub fn pet_synthesize(
  width : Int,
  height : Int,
  radiotracer : String,
) -> PETData {
  let _ = width * height
  let out : Array[Byte] = []

  let center_x = width.to_double() / 2.0
  let center_y = height.to_double() / 2.0

  let mut state = 42
  for y in 0..<height {
    for x in 0..<width {
      state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
      let noise = (state % 1000).to_double() / 1000.0

      let dx = x.to_double() - center_x
      let dy = y.to_double() - center_y
      let dist = (dx * dx + dy * dy).sqrt()

      // 简化的PET：高摄取区域+泊松噪声
      let uptake = if dist < 50.0 {
        200.0
      } else if dist < 100.0 {
        100.0
      } else {
        20.0
      }

      let value = uptake * (0.8 + noise * 0.4)
      let clamped = if value > 255.0 { 255.0 } else { value }
      out.push(clamped.to_int().to_byte())
    }
  }

  PETData::{
    activity_map: @types.Image::{
      width,
      height,
      channels: 1,
      data: Bytes::from_array(out),
    },
    radiotracer,
    injected_dose_mbq: 370.0,
    uptake_time_min: 60.0,
    reconstruction_algorithm: "OSEM",
    suv_scale: 1.0,
  }
}

///|
/// 生成高级成像报告
pub fn advanced_imaging_report_to_string(
  modality : ImagingModality,
  image : @types.Image,
  config : AdvancedImagingConfig,
) -> String {
  let modality_name = match modality {
    ImagingModality::Quantum => "量子成像"
    ImagingModality::Terahertz => "太赫兹成像"
    ImagingModality::Infrared => "红外成像"
    ImagingModality::Hyperspectral => "高光谱成像"
    ImagingModality::Polarization => "偏振成像"
    ImagingModality::Thermal => "热成像"
    ImagingModality::XRay => "X射线成像"
    ImagingModality::Ultrasound => "超声成像"
    ImagingModality::MRI => "MRI成像"
    ImagingModality::PET => "PET成像"
    ImagingModality::CT => "CT成像"
    _ => "未知"
  }

  let mut report = "## 高级成像报告\n\n"
  report = report + "### 成像信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report + "| 成像模态 | " + modality_name + " |\n"
  report = report +
    "| 图像尺寸 | " +
    image.width.to_string() +
    "x" +
    image.height.to_string() +
    " |\n"
  report = report + "| 通道数 | " + image.channels.to_string() + " |\n"
  report = report + "| 切片数 | " + config.num_slices.to_string() + " |\n"
  report = report +
    "| 校准因子 | " +
    config.calibration_factor.to_string() +
    " |\n"
  report = report +
    "| 噪声抑制 | " +
    (if config.use_noise_reduction { "开启" } else { "关闭" }) +
    " |\n"
  report = report +
    "| 图像增强 | " +
    (if config.use_enhancement { "开启" } else { "关闭" }) +
    " |\n"

  report
}

// 辅助函数

///|
fn atan_approx(x : Double) -> Double {
  // 简化的反正切近似
  x - x * x * x / 3.0 + x * x * x * x * x / 5.0
}
src\process\advanced_imaging\advanced_imaging.mbt

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