Package 'RIA'

rda containing DICOM header codes to include in RIA_image object

Description

rda data file containing Name, Group and Element codes of DICOM header info to be included into RIA_image object by default when using load_dicom function. Can be edited to change defaults.

Usage

DICOM_codes

Format

Each row is a DICOM header input

Value

3 column data.frame

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

---

Loads DICOM images to RIA image format

Description

Loads DICOM images to a RIA_image object. RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object, which has two potential slots. $orig contains the original image after loading and is a 3D array of integers created with create3D. $modif contains the image that has been modified using functions.

• RIA_header is a RIA_header object, which is list of DICOM header information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

Further attributes may also be added by RIA functions.

Usage

load_dicom(
  filename,
  mask_filename = NULL,
  keep_mask_values = 1,
  switch_z = FALSE,
  crop_in = TRUE,
  replace_in = TRUE,
  center_in = TRUE,
  zero_value = NULL,
  min_to = -1024,
  header_add = NULL,
  header_exclude = NULL,
  verbose_in = TRUE,
  recursive_in = TRUE,
  exclude_in = "sql",
  mode_in = "integer",
  transpose_in = TRUE,
  pixelData_in = TRUE,
  mosaic_in = FALSE,
  mosaicXY_in = NULL,
  sequence_in = FALSE,
  ...
)

Arguments

filename	string, file path to directory containing dcm files.
mask_filename	string vector, file path to optional directory containing dcm files of mask image. If multiple are supplied, then those voxels are kept which have one of the values of keep_mask_values in any of the supplied masks.
keep_mask_values	integer vector or string, indicates which value or values of the mask image to use as indicator to identify voxels wished to be processed. Usually 1-s indicate voxels wished to be processed. However, one mask image might contain several segmentations, in which case supplying several integers is allowed. Furthermore, if the same string is supplied to filename and mask_filename, then the integers in keep_mask_values are used to specify which voxel values to analyze. This way the provided image can be segmented to specific components. For example, if you wish to analyze only the low-density non-calcified component of coronary plaques, then keep_mask_values can specify this by setting it to: -100:30. If a single string is provided, then each element of the mask will be examined against the statement in the string. For example, if '>0.5' is provided i.e. the mask is probabilities after a DL algorithm, then all voxels with values >0.5 in the mask image will be kept. This can be a complex logical expression. The data on which the expression is executed is called data or data_mask, depending on whether you wish to filter the original image, that is the original image is supplied as a mask, or if you have unique mask files respectively. Therefore for complex logical expressions you can define for example: '>-100 & data<30' to consider data values between -100 and 30, or '>0.5 & data_mask<0.75' to select voxels based-on mask values between 0.5 and 0.75 for example if they represent a probability mask.
switch_z	logical, indicating whether to change the orientation of the images in the Z axis. Some software reverse the order of the manipulated image in the Z axis, and therefore the images of the mask image need to be reversed.
crop_in	logical, indicating whether to crop RIA_image to smallest bounding box.
replace_in	logical, whether to replace smallest values indicated by zero_value, which are considered to indicate no signal, to NA.
center_in	logical, whether to shift data so smallest value is equal to min_to input parameter.
zero_value	integer, indicating voxels values which are considered not to have any information. If left empty, then the smallest HU value in the image will be used, if replace_in is TRUE.
min_to	integer, value to which data is shifted to if center_in is TRUE.
header_add	dataframe, with three columns: Name, Group and Element containing the name, the group and the element code of the DICOM fields wished to be added to theRIA_header.
header_exclude	dataframe, with three columns: Name, Group and Element containing the name, the group and the element code of the DICOM fields wished to be excluded from the default header elements present in DICOM_codes rda file.
verbose_in	logical, indicating whether to print detailed information. Most prints can also be suppresed using the suppressMessages function.
recursive_in	recursive parameter input of readDICOM.
exclude_in	exclude parameter input of readDICOM.
mode_in	mode parameter input of create3D.
transpose_in	transpose parameter input of create3D.
pixelData_in	pixelData parameter input of create3D.
mosaic_in	mosaic parameter input of create3D.
mosaicXY_in	mosaicXY parameter input of create3D.
sequence_in	sequence parameter input of create3D.
...	additional arguments to readDICOM, readDICOMFile and create3D.

Details

load_dicom is used to transform DICOM datasets into the RIA environment. RIA_image object was developed to facilitate and simplify radiomics calculations by keeping all necessary information in one place.

RIA_data stores the DICOM image that is converted to numerical 3D arrays using readDICOM and create3D. The function stores the original loaded image in RIA_data$orig, while all modified images are stored in RIA_data$modif. By default, the original image RIA_data$orig is untouched by functions other than those operating in load_dicom. While other functions operate on the RIA_data$modif image by default.
Due to memory concerns, there can only be one RIA_data$orig and RIA_data$modif image present at one time in a RIA_image. Therefore, if image manipulations are performed, then the RIA_data$modif will be overwritten. However, functions can save images into new slots of RIA_image, for example discretized images can be saved to the discretized slot of RIA_image.
load_dicom not only loads the DICOM image based on parameters that can be set for readDICOM and create3D, but also can perform minimal manipulations on the image itself.
crop_in logical variable is used to indicate, whether to crop the image to the smallest bounding box still containing all the information. If TRUE, then all X, Y and potentially Z slices containing no information will be removed. This allows significant reduction of necessary memory to store image data.
zero_value parameter is used to indicate HU values which contain no information. If left empty, then the smallest value will be considered as indicating voxels without a signal.
replace_in logical can be used to change values that are considered to have no signal to NA. This is necessary to receive proper statistical values later on.
center_in logical is used to indicate whether the values should be shifted. Some vendors save HU values as positive integers to spare memory and minimalize file sizes. Therefore, in some instances shift of the scale is needed. By default, the values are shifted by -1024, but in other cases a different constant might be required, which can be set using the min_to input.

RIA_header is a list containing the most basic patient and examination information needed for further analysis. The default DICOM set is present in DICOM_codes, which can be edited to anyones needs. But if we wish only to add of remove specific DICOM header rows, then the header_add and header_exclude can be used.

RIA_log is a list of variables, which give an overview of what has been done with the image. If the whole RIA_image is supplied to a function, the information regarding the manipulations are written into the $events array in chronological order. Furthermore, some additional information is also saved in the log, which might be needed for further analysis.

Value

Returns a RIA_image object. RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object containing the image in $orig slot.

• RIA_header is a RIA_header object, which is s list of DICOM information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

Examples

## Not run: 
 #Image will be croped to smallest bounding box, and smallest values will be changed to NA,
 while 1024 will be substracted from all other data points.
 RIA_image <- load_dicom("/Users/Test/Documents/Radiomics/John_Smith/DICOM_folder/")
 
## End(Not run)

---

Loads NIfTI images to RIA image format

Description

Loads NIfTI images to a RIA_image object. RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object, which has two potential slots. $orig contains the original image after loading $modif contains the image that has been modified using functions.

• RIA_header is a RIA_header object, which is list of header information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

Further attributes may also be added by RIA functions.

Usage

load_nifti(
  filename,
  image_dim = 3,
  mask_filename = NULL,
  keep_mask_values = 1,
  switch_z = FALSE,
  crop_in = TRUE,
  replace_in = TRUE,
  center_in = FALSE,
  zero_value = NULL,
  min_to = -1024,
  verbose_in = TRUE,
  reorient_in = TRUE,
  ...
)

Arguments

filename	string, file path to directory containing NIfTI file.
image_dim	integer, dimensions of the image.
mask_filename	string vector, file path to optional directory containing NIfTI file of mask image. If multiple are supplied, then those voxels are kept which have one of the values of keep_mask_values in any of the supplied masks.
keep_mask_values	integer vector or string, indicates which value or values of the mask image to use as indicator to identify voxels wished to be processed. Usually 1-s indicate voxels wished to be processed. However, one mask image might contain several segmentations, in which case supplying several integers is allowed. Furthermore, if the same string is supplied to filename and mask_filename, then the integers in keep_mask_values are used to specify which voxel values to analyze. This way the provided image can be segmented to specific components. For example, if you wish to analyze only the low-density non-calcified component of coronary plaques, then keep_mask_values can specify this by setting it to: -100:30. If a single string is provided, then each element of the mask will be examined against the statement in the string. For example, if '>0.5' is provided i.e. the mask is probabilities after a DL algorithm, then all voxels with values >0.5 in the mask image will be kept. This can be a complex logical expression. The data on which the expression is executed is called data or data_mask, depending on whether you wish to filter the original image, that is the original image is supplied as a mask, or if you have unique mask files respectively. Therefore for complex logical expressions you can define for example: '>-100 & data<30' to consider data values between -100 and 30, or '>0.5 & data_mask<0.75' to select voxels based-on mask values between 0.5 and 0.75 for example if they represent a probability mask.
switch_z	logical, indicating whether to change the orientation of the images in the Z axis. Some software reverse the order of the manipulated image in the Z axis, and therefore the images of the mask image need to be reversed.
crop_in	logical, indicating whether to crop RIA_image to smallest bounding box.
replace_in	logical, whether to replace smallest values indicated by zero_value, which are considered to indicate no signal, to NA.
center_in	logical, whether to shift data so smallest value is equal to min_to input parameter.
zero_value	integer, indicating voxels values which are considered not to have any information. If left empty, then the smallest HU value in the image will be used, if replace_in is TRUE.
min_to	integer, value to which data is shifted to if center_in is TRUE.
verbose_in	logical, indicating whether to print detailed information. Most prints can also be suppresed using the suppressMessages function.
reorient_in	reorient parameter input of readNIfTI.
...	additional arguments to readNIfTI, nifti_header.

Details

load_nifti is used to transform NIfTI datasets into the RIA environment. RIA_image object was developed to facilitate and simplify radiomics calculations by keeping all necessary information in one place.

RIA_data stores the image that is converted to numerical 3D arrays using readNIfTI. The function stores the original loaded image in RIA_data$orig, while all modified images are stored in RIA_data$modif. By default, the original image RIA_data$orig is untouched by functions other than those operating in load_nifti. While other functions operate on the RIA_data$modif image by default.
Due to memory concerns, there can only be one RIA_data$orig and RIA_data$modif image present at one time in a RIA_image. Therefore, if image manipulations are performed, then the RIA_data$modif will be overwritten. However, functions can save images into new slots of RIA_image, for example discretized images can be saved to the discretized slot of RIA_image.
load_nifti not only loads the image based on parameters that can be set for readNIfTI, but also can perform minimal manipulations on the image itself.
crop_in logical variable is used to indicate, whether to crop the image to the smallest bounding box still containing all the information. If TRUE, then all X, Y and potentially Z slices containing no information will be removed. This allows significant reduction of necessary memory to store image data.
zero_value parameter is used to indicate HU values which contain no information. If left empty, then the smallest value will be considered as indicating voxels without a signal.
replace_in logical can be used to change values that are considered to have no signal to NA. This is necessary to receive proper statistical values later on.
center_in logical is used to indicate whether the values should be shifted. Some vendors save HU values as positive integers to spare memory and minimalize file sizes. Therefore, in some instances shift of the scale is needed. By default, the values are shifted by -1024, but in other cases a different constant might be required, which can be set using the min_to input.

RIA_header is a list containing the most basic patient and examination information present in the NIfTI file.

RIA_log is a list of variables, which give an overview of what has been done with the image. If the whole RIA_image is supplied to a function, the information regarding the manipulations are written into the $events array in chronological order. Furthermore, some additional information is also saved in the log, which might be needed for further analysis.

Value

Returns a RIA_image object. RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object containing the image in $orig slot.

• RIA_header is a RIA_header object, which is s list of meta information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

Examples

## Not run: 
 #Image will be croped to smallest bounding box, and smallest values will be changed to NA,
 while 1024 will be substracted from all other data points.
 RIA_image <- load_nifti("/Users/Test/Documents/Radiomics/John_Smith/NIfTI_folder/sample.nii")
 
## End(Not run)

---

Loads npy files to RIA image format

Description

Loads numpy arrays from python to a RIA_image object using the reticulate package. Requires python and numpy to be installed! RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object, which has two potential slots. $orig contains the original image after loading $modif contains the image that has been modified using functions.

• RIA_header is a RIA_header object, which is list of header information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

Further attributes may also be added by RIA functions.

Usage

load_npy(
  filename,
  mask_filename = NULL,
  keep_mask_values = 1,
  switch_z = FALSE,
  crop_in = TRUE,
  replace_in = TRUE,
  center_in = FALSE,
  zero_value = NULL,
  min_to = -1024,
  PixelSpacing = 1,
  SpacingBetweenSlices = 1,
  verbose_in = TRUE,
  ...
)

Arguments

filename	string, file path to npy file.
mask_filename	string vector, file path to npy file of mask image. If multiple are supplied, then those voxels are kept which have one of the values of keep_mask_values in any of the supplied masks.
keep_mask_values	integer vector or string, indicates which value or values of the mask image to use as indicator to identify voxels wished to be processed. Usually 1-s indicate voxels wished to be processed. However, one mask image might contain several segmentations, in which case supplying several integers is allowed. Furthermore, if the same string is supplied to filename and mask_filename, then the integers in keep_mask_values are used to specify which voxel values to analyze. This way the provided image can be segmented to specific components. For example, if you wish to analyze only the low-density non-calcified component of coronary plaques, then keep_mask_values can specify this by setting it to: -100:30. If a single string is provided, then each element of the mask will be examined against the statement in the string. For example, if '>0.5' is provided i.e. the mask is probabilities after a DL algorithm, then all voxels with values >0.5 in the mask image will be kept. This can be a complex logical expression. The data on which the expression is executed is called data or data_mask, depending on whether you wish to filter the original image, that is the original image is supplied as a mask, or if you have unique mask files respectively. Therefore for complex logical expressions you can define for example: '>-100 & data<30' to consider data values between -100 and 30, or '>0.5 & data_mask<0.75' to select voxels based-on mask values between 0.5 and 0.75 for example if they represent a probability mask.
switch_z	logical, indicating whether to change the orientation of the images in the Z axis. Some software reverse the order of the manipulated image in the Z axis, and therefore the images of the mask image need to be reversed.
crop_in	logical, indicating whether to crop RIA_image to smallest bounding box.
replace_in	logical, whether to replace smallest values indicated by zero_value, which are considered to indicate no signal, to NA.
center_in	logical, whether to shift data so smallest value is equal to min_to input parameter.
zero_value	integer, indicating voxels values which are considered not to have any information. If left empty, then the smallest HU value in the image will be used, if replace_in is TRUE.
min_to	integer, value to which data is shifted to if center_in is TRUE.
PixelSpacing	numerical, Pixel spacing value of image.
SpacingBetweenSlices	numerical, Spacing between the slices value of the image.
verbose_in	logical, indicating whether to print detailed information. Most prints can also be suppressed using the suppressMessages function.
...	additional arguments to numpy.load.

Details

load_npy is used to transform numpy array datasets into the RIA environment. RIA_image object was developed to facilitate and simplify radiomics calculations by keeping all necessary information in one place.

RIA_data stores the numpy image that is converted to numerical 3D arrays using the reticulate package. The function stores the original loaded image in RIA_data$orig, while all modified images are stored in RIA_data$modif. By default, the original image RIA_data$orig is untouched by functions other than those operating in load_npy. While other functions operate on the RIA_data$modif image by default.
Due to memory concerns, there can only be one RIA_data$orig and RIA_data$modif image present at one time in a RIA_image. Therefore, if image manipulations are performed, then the RIA_data$modif will be overwritten. However, functions can save images into new slots of RIA_image, for example discretized images can be saved to the discretized slot of RIA_image.
load_npy not only loads the image, but also can perform minimal manipulations on the image itself.
crop_in logical variable is used to indicate, whether to crop the image to the smallest bounding box still containing all the information. If TRUE, then all X, Y and potentially Z slices containing no information will be removed. This allows significant reduction of necessary memory to store image data.
zero_value parameter is used to indicate HU values which contain no information. If left empty, then the smallest value will be considered as indicating voxels without a signal.
replace_in logical can be used to change values that are considered to have no signal to NA. This is necessary to receive proper statistical values later on.
center_in logical is used to indicate whether the values should be shifted. Some vendors save HU values as positive integers to spare memory and minimalize file sizes. Therefore, in some instances shift of the scale is needed. By default, the values are shifted by -1024, but in other cases a different constant might be required, which can be set using the min_to input.

RIA_header is a list containing the most basic patient and examination information present in the npy file. Data is limited to the pixel spacing and spacing between the slices information.

RIA_log is a list of variables, which give an overview of what has been done with the image. If the whole RIA_image is supplied to a function, the information regarding the manipulations are written into the $events array in chronological order. Furthermore, some additional information is also saved in the log, which might be needed for further analysis.

Value

Returns a RIA_image object. RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object containing the image in $orig slot.

• RIA_header is a RIA_header object, which is s list of header information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

Examples

## Not run: 
 #Image will be croped to smallest bounding box, and smallest values will be changed to NA
 RIA_image <- load_npy("/Users/Test/Documents/Radiomics/John_Smith/npy_folder/sample.npy")
 
## End(Not run)

---

Loads nrrd images to RIA image format

Description

Loads nrrd images to a RIA_image object. RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object, which has two potential slots. $orig contains the original image after loading $modif contains the image that has been modified using functions.

• RIA_header is a RIA_header object, which is list of header information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

Further attributes may also be added by RIA functions.

Usage

load_nrrd(
  filename,
  mask_filename = NULL,
  keep_mask_values = 1,
  switch_z = FALSE,
  crop_in = TRUE,
  replace_in = TRUE,
  center_in = FALSE,
  zero_value = NULL,
  min_to = -1024,
  verbose_in = TRUE,
  origin_in = NULL,
  ReadByteAsRaw_in = "unsigned",
  ...
)

Arguments

filename	string, file path to directory containing nrrd file.
mask_filename	string vector, file path to optional directory containing nrrd file of mask image. If multiple are supplied, then those voxels are kept which have one of the values of keep_mask_values in any of the supplied masks.
keep_mask_values	integer vector or string, indicates which value or values of the mask image to use as indicator to identify voxels wished to be processed. Usually 1-s indicate voxels wished to be processed. However, one mask image might contain several segmentations, in which case supplying several integers is allowed. Furthermore, if the same string is supplied to filename and mask_filename, then the integers in keep_mask_values are used to specify which voxel values to analyze. This way the provided image can be segmented to specific components. For example, if you wish to analyze only the low-density non-calcified component of coronary plaques, then keep_mask_values can specify this by setting it to: -100:30. If a single string is provided, then each element of the mask will be examined against the statement in the string. For example, if '>0.5' is provided i.e. the mask is probabilities after a DL algorithm, then all voxels with values >0.5 in the mask image will be kept. This can be a complex logical expression. The data on which the expression is executed is called data or data_mask, depending on whether you wish to filter the original image, that is the original image is supplied as a mask, or if you have unique mask files respectively. Therefore for complex logical expressions you can define for example: '>-100 & data<30' to consider data values between -100 and 30, or '>0.5 & data_mask<0.75' to select voxels based-on mask values between 0.5 and 0.75 for example if they represent a probability mask.
switch_z	logical, indicating whether to change the orientation of the images in the Z axis. Some software reverse the order of the manipulated image in the Z axis, and therefore the images of the mask image need to be reversed.
crop_in	logical, indicating whether to crop RIA_image to smallest bounding box.
replace_in	logical, whether to replace smallest values indicated by zero_value, which are considered to indicate no signal, to NA.
center_in	logical, whether to shift data so smallest value is equal to min_to input parameter.
zero_value	integer, indicating voxels values which are considered not to have any information. If left empty, then the smallest HU value in the image will be used, if replace_in is TRUE.
min_to	integer, value to which data is shifted to if center_in is TRUE.
verbose_in	logical, indicating whether to print detailed information. Most prints can also be suppresed using the suppressMessages function.
origin_in	origin parameter input of read.nrrd.
ReadByteAsRaw_in	origin parameter input of read.nrrd.
...	additional arguments to read.nrrd, read.nrrd.header.

Details

load_nrrd is used to transform nrrd datasets into the RIA environment. RIA_image object was developed to facilitate and simplify radiomics calculations by keeping all necessary information in one place.

RIA_data stores the nrrd image that is converted to numerical 3D arrays using read.nrrd. The function stores the original loaded image in RIA_data$orig, while all modified images are stored in RIA_data$modif. By default, the original image RIA_data$orig is untouched by functions other than those operating in load_nrrd. While other functions operate on the RIA_data$modif image by default.
Due to memory concerns, there can only be one RIA_data$orig and RIA_data$modif image present at one time in a RIA_image. Therefore, if image manipulations are performed, then the RIA_data$modif will be overwritten. However, functions can save images into new slots of RIA_image, for example discretized images can be saved to the discretized slot of RIA_image.
load_nrrd not only loads the image based on parameters that can be set for read.nrrd, but also can perform minimal manipulations on the image itself.
crop_in logical variable is used to indicate, whether to crop the image to the smallest bounding box still containing all the information. If TRUE, then all X, Y and potentially Z slices containing no information will be removed. This allows significant reduction of necessary memory to store image data.
zero_value parameter is used to indicate HU values which contain no information. If left empty, then the smallest value will be considered as indicating voxels without a signal.
replace_in logical can be used to change values that are considered to have no signal to NA. This is necessary to receive proper statistical values later on.
center_in logical is used to indicate whether the values should be shifted. Some vendors save HU values as positive integers to spare memory and minimalize file sizes. Therefore, in some instances shift of the scale is needed. By default, the values are shifted by -1024, but in other cases a different constant might be required, which can be set using the min_to input.

RIA_header is a list containing the most basic patient and examination information present in the nrrd file.

RIA_log is a list of variables, which give an overview of what has been done with the image. If the whole RIA_image is supplied to a function, the information regarding the manipulations are written into the $events array in chronological order. Furthermore, some additional information is also saved in the log, which might be needed for further analysis.

Value

Returns a RIA_image object. RIA_image is a list with three mandatory attributes.

• RIA_data is a RIA_data object containing the image in $orig slot.

• RIA_header is a RIA_header object, which is s list of nrrd information.

• RIA_log is a RIA_log object, which is a list updated by RIA functions and acts as a log and possible input for some functions.

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

Examples

## Not run: 
 #Image will be croped to smallest bounding box, and smallest values will be changed to NA,
 while 1024 will be substracted from all other data points.
 RIA_image <- load_nrrd("/Users/Test/Documents/Radiomics/John_Smith/nrrd_folder/sample.nrrd")
 
## End(Not run)

---

Merges multiple loaded images into one volume

Description

Merges multiple RIA_image class objects loaded using any of the load functions. All images need to have the same dimensions. Further, during loading the images should not be cropped to assure that the orientation and position of the data is maintained. Data of the new combined image is updated sequentially, using data from the data$orig slot, that is only parts of the image that do not have data (which are converted to NA during the load process) are updated in the order of provided RIA_images. If multiple images contain data in for the same element, the first value is used in the new image. Data in the data$log slot is updated based on the new combined image, while data in the data$header slot is copied from the first provided image.

Usage

merge_RIA_images(RIA_data_in, crop_in = TRUE, verbose_in = TRUE)

Arguments

RIA_data_in	List of Multiple RIA_images.
crop_in	logical, indicating whether to crop the merged image to smallest bounding box.
verbose_in	logical indicating whether to print detailed information. Most prints can also be suppressed using the suppressMessages function.

Value

RIA_image containing the merged volume with updated log and header data

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

Examples

## Not run: 
#Load multiple images and combine them
d1 <- load_nifti(ABC_p1.nii.gz, crop_in = FALSE)
d2 <- load_nifti(ABC_p2.nii.gz, crop_in = FALSE)
d  <- merge_RIA(list(d1, d2))

## End(Not run)

---

RIA_image object of a plaque without the napkin-ring sign

Description

rda containing an example RIA_image object of a patients plaque which does not show the napkin-ring sign.

Usage

NRS

Format

RIA_image object

Value

RIA_image object

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

---

RIA_image object of a plaque with the napkin-ring sign

Description

rda containing an example RIA_image object of a patients plaque which shows the napkin-ring sign.

Usage

NRS

Format

RIA_image object

Value

RIA_image object

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

---

Calculates all radiomic statistics on supplied RIA_image

Description

Calculates specified radiomic statistics on RIA_image. Parameters of radiomic functions may be set. By default the the images are discretized to 8, 16 and 32 bins using equally sized and probable binning. First-order statistics are calculated on the original image and if asked then on all discretizations. Symmetric GLCMs are calculated for all directions at a distance of 1 for all discretizations. GLRLMs are also calculated for all discretizations. Geometry-based statistics are calculated for the original image as well as all discretizations is requested.

Usage

radiomics_all(
  RIA_data_in,
  bins_in = c(8, 16, 32),
  equal_prob = "both",
  fo_discretized = FALSE,
  distance = c(1),
  statistic = "mean(X, na.rm = TRUE)",
  geometry_discretized = TRUE,
  verbose_in = TRUE
)

Arguments

RIA_data_in	RIA_image.
bins_in	integer vector, number of bins specified.
equal_prob	logical or string, indicating to cut data into bins with equal relative frequencies. If FALSE, then equal interval bins will be used. If "both" is supplied, the both equally probable and equal interval bins will be created.
fo_discretized	logical, indicating whether to calculate first-order statistics on discretized images.
distance	integer, distance between the voxels being compared.
statistic	string, defining the statistic to be calculated on the array of GLCM statistics. By default, statistic is set to "mean", however any function may be provided. The proper syntax is: function(X, attributes). The supplied string must contain a "X", which will be replaced with the array of the GLCM statistics value. Further attributes of the function may also be given. For example, if you wish to calculate the median of all GLCMs calculated in different directions, then it must be supplied as: median(X, na.rm = TRUE).
geometry_discretized	logical, indicating whether to calculate geometry-based statistics on discretized images.
verbose_in	logical, indicating whether to print detailed information. Most prints can also be suppressed using the suppressMessages function.

Value

RIA_image containing the statistical information.

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

Examples

## Not run: 
#Discretize loaded image and then calculate all radiomic statistics
RIA_image <- radiomics_all(RIA_image, equal_prob = "both", bins_in= c(32,64), distance = c(1:2))

## End(Not run)

---

Export radiomics calculations of RIA image to csv

Description

Exports given slots of statistics from RIA_image. Names of slots have to be defined which the user wishes to export using the stats parameter. Using the group_name parameter the user can lable the cases with a group ID, for example "Case", which can be used as a grouping variable for further analysis.

Usage

save_RIA(
  RIA_image,
  save_to = "C:/",
  save_name = "RIA_stat",
  group_name = "Case",
  stats = c("stat_fo", "stat_glcm_mean", "stat_glrlm_mean", "stat_geometry")
)

Arguments

RIA_image	RIA_image with calculated statistics.
save_to	string, path of folder to save results to.
save_name	string, path of folder to save results to.
group_name	string, a ID defining which group the case belongs to.
stats	string vector, identifing which slots to export

References

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://pubmed.ncbi.nlm.nih.gov/29233836/

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://pubmed.ncbi.nlm.nih.gov/28346329/

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